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Foltyn-Dumitru M, Mahmutoglu MA, Brugnara G, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Shape matters: unsupervised exploration of IDH-wildtype glioma imaging survival predictors. Eur Radiol 2024:10.1007/s00330-024-11042-6. [PMID: 39251442 DOI: 10.1007/s00330-024-11042-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 06/15/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Abstract
OBJECTIVES This study examines clustering based on shape radiomic features and tumor volume to identify IDH-wildtype glioma phenotypes and assess their impact on overall survival (OS). MATERIALS AND METHODS This retrospective study included 436 consecutive patients diagnosed with IDH-wt glioma who underwent preoperative MR imaging. Alongside the total tumor volume, nine distinct shape radiomic features were extracted using the PyRadiomics framework. Different imaging phenotypes were identified using partition around medoids (PAM) clustering on the training dataset (348/436). The prognostic efficacy of these phenotypes in predicting OS was evaluated on the test dataset (88/436). External validation was performed using the public UCSF glioma dataset (n = 397). A decision-tree algorithm was employed to determine the relevance of features associated with cluster affiliation. RESULTS PAM clustering identified two clusters in the training dataset: Cluster 1 (n = 233) had a higher proportion of patients with higher sphericity and elongation, while Cluster 2 (n = 115) had a higher proportion of patients with higher maximum 3D diameter, surface area, axis lengths, and tumor volume (p < 0.001 for each). OS differed significantly between clusters: Cluster 1 showed a median OS of 23.8 compared to 11.4 months of Cluster 2 in the holdout test dataset (p = 0.002). Multivariate Cox regression showed improved performance with cluster affiliation over clinical data alone (C index 0.67 vs 0.59, p = 0.003). Cluster-based models outperformed the models with tumor volume alone (evidence ratio: 5.16-5.37). CONCLUSION Data-driven clustering reveals imaging phenotypes, highlighting the improved prognostic power of combining shape-radiomics with tumor volume, thereby outperforming predictions based on tumor volume alone in high-grade glioma survival outcomes. CLINICAL RELEVANCE STATEMENT Shape-radiomics and volume-based cluster analyses of preoperative MRI scans can reveal imaging phenotypes that improve the prediction of OS in patients with IDH-wild type gliomas, outperforming currently known models based on tumor size alone or clinical parameters. KEY POINTS Shape radiomics and tumor volume clustering in IDH-wildtype gliomas are investigated for enhanced prognostic accuracy. Two distinct phenotypic clusters were identified with different median OSs. Integrating shape radiomics and volume-based clustering enhances OS prediction in IDH-wildtype glioma patients.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
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Ellingson BM, Sanvito F, Cloughesy TF, Huang RY, Villanueva-Meyer JE, Pope WB, Barboriak DP, Shankar LK, Smits M, Kaufmann TJ, Boxerman JL, Weller M, Galanis E, Groot JD, Gilbert MR, Lassman AB, Shiroishi MS, Nabavizadeh A, Mehta M, Stupp R, Wick W, Reardon DA, Vogelbaum MA, van den Bent M, Chang SM, Wen PY. A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults. AJNR Am J Neuroradiol 2024:ajnr.A8396. [PMID: 38926092 DOI: 10.3174/ajnr.a8396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 06/22/2024] [Indexed: 06/28/2024]
Abstract
Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.
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Affiliation(s)
- Benjamin M Ellingson
- From the UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences (B.M.E., F.S.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Francesco Sanvito
- From the UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences (B.M.E., F.S.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Timothy F Cloughesy
- UCLA Brain Tumor Program, Department of Neurology (T.F.C.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital (R.Y.H.), Harvard Medical School, Boston, MA
| | | | - Whitney B Pope
- Department of Radiological Sciences (W.B.P.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Daniel P Barboriak
- Department of Radiology (D.P.B.), Duke University Medical Center, Durham, NC
| | - Lalitha K Shankar
- Clinical Trials Branch, Cancer Imaging Program (L.K.S.), National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Marion Smits
- Department of Radiology & Nuclear Medicine (M.S.), Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | | | - Jerrold L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI
| | - Michael Weller
- Department of Neurology (M.W.), University Hospital and University of Zurich, Zurich, Switzerland
| | | | - John de Groot
- Division of Neuro-Oncology, Department of Neurosurgery (J.d.G., S.M.C.), University of California, San Francisco, CA
| | - Mark R Gilbert
- Neuro-Oncology Branch, Center for Cancer Research (M.R.G.), National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Andrew B Lassman
- Division of Neuro-Oncology, Department of Neurology (A.B.L.), Herbert Irving Comprehensive Cancer Center and Irving Institute for Clinical and Translational Research, Columbia University Vagelos College of Physicians and Surgeons and New York-Presbyterian Hospital, New York, NY
| | - Mark S Shiroishi
- Department of Radiology (M.S.S.), Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA
| | - Ali Nabavizadeh
- Department of Radiology (A.N.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Roger Stupp
- Malnati Brain Tumor Institute, Lurie Comprehensive Cancer Center and Departments of Neurological Surgery, Neurology and Division of Hematology/Oncology (R.S.), Northwestern University, Chicago, IL
| | - Wolfgang Wick
- Department of Neurology Heidelberg University Hospital & Clinical Cooperation Unit Neurooncology (W.W.), German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David A Reardon
- Center For Neuro-Oncology, Dana-Farber Cancer Institute and Harvard Medical School(D.A.R., P.Y.W.), Boston, MA
| | | | - Martin van den Bent
- Department Neuro-Oncology (M.v.d.B.), Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Susan M Chang
- Division of Neuro-Oncology, Department of Neurosurgery (J.d.G., S.M.C.), University of California, San Francisco, CA
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute and Harvard Medical School(D.A.R., P.Y.W.), Boston, MA
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Azizova A, Prysiazhniuk Y, Wamelink IJHG, Petr J, Barkhof F, Keil VC. Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance. AJNR Am J Neuroradiol 2024; 45:1053-1062. [PMID: 38937115 DOI: 10.3174/ajnr.a8274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 03/01/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking. PURPOSE Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment. DATA SOURCES MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023. STUDY SELECTION Original articles predicting diagnosis, progression, and survival in patients with glioma were included. DATA ANALYSIS The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided. DATA SYNTHESIS Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium (n = 33) and low (n = 2). Recurring objectives were overall survival (n = 18) and isocitrate dehydrogenase mutation (IDH; n = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n = 2, grade 2/3 glioma n = 1, grade 3 glioma n = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I2 = 52%). IDH mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies (n = 17). LIMITATIONS Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for IDH was limited due to a high study heterogeneity. CONCLUSIONS Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.
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Affiliation(s)
- Aynur Azizova
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Imaging and Biomarkers (A.A., I.J.H.G.W., V.C.K.), Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Yeva Prysiazhniuk
- The Second Faculty of Medicine (Y.P.), Department of Pathophysiology, Charles University, Prague, Czech Republic
| | - Ivar J H G Wamelink
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Imaging and Biomarkers (A.A., I.J.H.G.W., V.C.K.), Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Jan Petr
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Institute of Radiopharmaceutical Cancer Research (J.P.), Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Frederik Barkhof
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Brain Imaging (F.B., V.C.K.), Amsterdam Neuroscience, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Center for Medical Image Computing (F.B.), University College London, London, United Kingdom
| | - Vera C Keil
- From the Radiology and Nuclear Medicine Department (A.A., I.J.H.G.W., J.P., F.B., V.C.K.), Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Imaging and Biomarkers (A.A., I.J.H.G.W., V.C.K.), Cancer Center Amsterdam, Amsterdam, the Netherlands
- Brain Imaging (F.B., V.C.K.), Amsterdam Neuroscience, Amsterdam, the Netherlands
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De Sutter S, Wuts J, Geens W, Vanbinst AM, Duerinck J, Vandemeulebroucke J. Modality redundancy for MRI-based glioblastoma segmentation. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03238-4. [PMID: 39093499 DOI: 10.1007/s11548-024-03238-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/11/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE Automated glioblastoma segmentation from magnetic resonance imaging is generally performed on a four-modality input, including T1, contrast T1, T2 and FLAIR. We hypothesize that information redundancy is present within these image combinations, which can possibly reduce a model's performance. Moreover, for clinical applications, the risk of encountering missing data rises as the number of required input modalities increases. Therefore, this study aimed to explore the relevance and influence of the different modalities used for MRI-based glioblastoma segmentation. METHODS After the training of multiple segmentation models based on nnU-Net and SwinUNETR architectures, differing only in their amount and combinations of input modalities, each model was evaluated with regard to segmentation accuracy and epistemic uncertainty. RESULTS Results show that T1CE-based segmentation (for enhanced tumor and tumor core) and T1CE-FLAIR-based segmentation (for whole tumor and overall segmentation) can reach segmentation accuracies comparable to the full-input version. Notably, the highest segmentation accuracy for nnU-Net was found for a three-input configuration of T1CE-FLAIR-T1, suggesting the confounding effect of redundant input modalities. The SwinUNETR architecture appears to suffer less from this, where said three-input and the full-input model yielded statistically equal results. CONCLUSION The T1CE-FLAIR-based model can therefore be considered as a minimal-input alternative to the full-input configuration. Addition of modalities beyond this does not statistically improve and can even deteriorate accuracy, but does lower the segmentation uncertainty.
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Affiliation(s)
- Selene De Sutter
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium.
| | - Joris Wuts
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Wietse Geens
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Anne-Marie Vanbinst
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Johnny Duerinck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Jef Vandemeulebroucke
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- imec, Leuven, Belgium
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5
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Naji N, Gee M, Jickling GC, Emery DJ, Saad F, McCreary CR, Smith EE, Camicioli R, Wilman AH. Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo. NMR IN BIOMEDICINE 2024; 37:e5139. [PMID: 38465729 DOI: 10.1002/nbm.5139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 03/12/2024]
Abstract
T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored. Detection sensitivity was evaluated against standard multiecho gradient echo (MEGE) QSM using 3-T in vivo data of 15 subjects with a combined total of 108 confirmed microbleeds. The two methods were compared based on the microbleed size and susceptibility measurements. In addition, simulations explored the detection sensitivity of MPRAGE-QSM at different representative magnetic field strengths and echo times using microbleeds of different size, susceptibility, and location. Results showed that in vivo microbleeds appeared to be smaller (× 0.54) and of higher mean susceptibility (× 1.9) on MPRAGE-QSM than on MEGE-QSM, but total susceptibility estimates were in closer agreement (slope: 0.97, r2: 0.94), and detection sensitivity was comparable. In simulations, QSM at 1.5 T had a low contrast-to-noise ratio that obscured the detection of many microbleeds. Signal-to-noise ratio (SNR) levels at 3 T and above resulted in better contrast and increased detection. The detection rates for microbleeds of minimum one-voxel diameter and 0.4-ppm susceptibility were 0.55, 0.80, and 0.88 at SNR levels of 1.5, 3, and 7 T, respectively. Size and total susceptibility estimates were more consistent than mean susceptibility estimates, which showed size-dependent underestimation. MPRAGE-QSM provides an opportunity to detect and quantify the size and susceptibility of microbleeds of at least one-voxel diameter at B0 of 3 T or higher with no additional time cost, when standard T2*-weighted images are not available or have inadequate spatial resolution. The total susceptibility measure is more robust against sequence variations and might allow combining data from different protocols.
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Affiliation(s)
- Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Myrlene Gee
- Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Glen C Jickling
- Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Derek J Emery
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Feryal Saad
- Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Cheryl R McCreary
- Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Eric E Smith
- Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Richard Camicioli
- Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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Streibel Y, Breckwoldt MO, Hunger J, Pan C, Fischer M, Turco V, Boztepe B, Fels-Palesandro H, Scheck JG, Sturm V, Karimian-Jazi K, Agardy DA, Annio G, Mustapha R, Soni SS, Alasa A, Weidenfeld I, Rodell CB, Wick W, Heiland S, Winkler F, Platten M, Bendszus M, Sinkus R, Schregel K. Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model. Sci Rep 2024; 14:15613. [PMID: 38971907 PMCID: PMC11227492 DOI: 10.1038/s41598-024-66519-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 07/02/2024] [Indexed: 07/08/2024] Open
Abstract
Glioblastoma is the most common and aggressive primary malignant brain tumor with poor prognosis. Novel immunotherapeutic approaches are currently under investigation. Even though magnetic resonance imaging (MRI) is the most important imaging tool for treatment monitoring, response assessment is often hampered by therapy-related tissue changes. As tumor and therapy-associated tissue reactions differ structurally, we hypothesize that biomechanics could be a pertinent imaging proxy for differentiation. Longitudinal MRI and magnetic resonance elastography (MRE) were performed to monitor response to immunotherapy with a toll-like receptor 7/8 agonist in orthotopic syngeneic experimental glioma. Imaging results were correlated to histology and light sheet microscopy data. Here, we identify MRE as a promising non-invasive imaging method for immunotherapy-monitoring by quantifying changes in response-related tumor mechanics. Specifically, we show that a relative softening of treated compared to untreated tumors is linked to the inflammatory processes following therapy-induced re-education of tumor-associated myeloid cells. Mechanistically, combined effects of myeloid influx and inflammation including extracellular matrix degradation following immunotherapy form the basis of treated tumors being softer than untreated glioma. This is a very early indicator of therapy response outperforming established imaging metrics such as tumor volume. The overall anti-tumor inflammatory processes likely have similar effects on human brain tissue biomechanics, making MRE a promising tool for gauging response to immunotherapy in glioma patients early, thereby strongly impacting patient pathway.
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Affiliation(s)
- Yannik Streibel
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Michael O Breckwoldt
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jessica Hunger
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Chenchen Pan
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Manuel Fischer
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Verena Turco
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology, Heidelberg University Hospital, National Center for Tumor Diseases, Heidelberg, Germany
| | - Berin Boztepe
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Hannah Fels-Palesandro
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Jonas G Scheck
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Volker Sturm
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Kianush Karimian-Jazi
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dennis A Agardy
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- Department of Neurology, Medical Faculty Mannheim, MCTN, Heidelberg University, Mannheim, Germany
| | - Giacomo Annio
- INSERM UMRS1148-Laboratory for Vascular Translational Science, University Paris, Paris, France
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Rami Mustapha
- Richard Dimbleby Laboratory of Cancer Research, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
| | - Shreya S Soni
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, USA
| | - Abdulrahman Alasa
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, USA
| | - Ina Weidenfeld
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Christopher B Rodell
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, USA
| | - Wolfgang Wick
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Frank Winkler
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Platten
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology, Medical Faculty Mannheim, MCTN, Heidelberg University, Mannheim, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Ralph Sinkus
- INSERM UMRS1148-Laboratory for Vascular Translational Science, University Paris, Paris, France
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Katharina Schregel
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Cho NS, Wang C, Van Dyk K, Sanvito F, Oshima S, Yao J, Lai A, Salamon N, Cloughesy TF, Nghiemphu PL, Ellingson BM. Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma. AJNR Am J Neuroradiol 2024:ajnr.A8327. [PMID: 38719607 DOI: 10.3174/ajnr.a8327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 05/01/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND AND PURPOSE Resting-state functional MRI (rs-fMRI) can be used to estimate functional connectivity (FC) between different brain regions, which may be of value for identifying cognitive impairment in patients with brain tumors. Unfortunately, neither rs-fMRI nor neurocognitive assessments are routinely assessed clinically, mostly due to limitations in examination time and cost. Since DSC perfusion MRI is often used clinically to assess tumor vascularity and similarly uses a gradient-echo-EPI sequence for T2*-sensitivity, we theorized a "pseudo-rs-fMRI" signal could be derived from DSC perfusion to simultaneously quantify FC and perfusion metrics, and these metrics can be used to estimate cognitive impairment in patients with brain tumors. MATERIALS AND METHODS Twenty-four consecutive patients with gliomas were enrolled in a prospective study that included DSC perfusion MRI, resting-sate functional MRI (rs-fMRI), and neurocognitive assessment. Voxelwise modeling of contrast bolus dynamics during DSC acquisition was performed and then subtracted from the original signal to generate a residual "pseudo-rs-fMRI" signal. Following the preprocessing of pseudo-rs-fMRI, full rs-fMRI, and a truncated version of the full rs-fMRI (first 100 timepoints) data, the default mode, motor, and language network maps were generated with atlas-based ROIs, Dice scores were calculated for the resting-state network maps from pseudo-rs-fMRI and truncated rs-fMRI using the full rs-fMRI maps as reference. Seed-to-voxel and ROI-to-ROI analyses were performed to assess FC differences between cognitively impaired and nonimpaired patients. RESULTS Dice scores for the group-level and patient-level (mean±SD) default mode, motor, and language network maps using pseudo-rs-fMRI were 0.905/0.689 ± 0.118 (group/patient), 0.973/0.730 ± 0.124, and 0.935/0.665 ± 0.142, respectively. There was no significant difference in Dice scores between pseudo-rs-fMRI and the truncated rs-fMRI default mode (P = .97) or language networks (P = .30), but there was a difference in motor networks (P = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), P = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), P = .0013, P = .29 compared with pseudo-rs-fMRI). CONCLUSIONS DSC perfusion MRI-derived pseudo-rs-fMRI data can be used to perform typical rs-fMRI FC analyses that may identify cognitive decline in patients with brain tumors while still simultaneously performing perfusion analyses.
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Affiliation(s)
- Nicholas S Cho
- From the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Medical Scientist Training Program (N.S.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Chencai Wang
- From the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Kathleen Van Dyk
- Department of Psychiatry and Biobehavioral Sciences (K.V.D, B.M.E.), David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, California
| | - Francesco Sanvito
- From the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Sonoko Oshima
- From the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jingwen Yao
- From the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Albert Lai
- UCLA Neuro-Oncology Program (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Noriko Salamon
- Department of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Benjamin M Ellingson
- From the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Department of Psychiatry and Biobehavioral Sciences (K.V.D, B.M.E.), David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, California
- Department of Neurosurgery (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Jekel L, Amiruddin R, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Calabrese E, Chiang V, Chung V, Conte GMM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Link KE, Liu X, Maleki N, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Tahon NH, Nada A, Velichko YS, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Nada A, Pedersen GC, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón F, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Fehringer P, Ghonim M, Ghonim M, Gkampenis A, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Yordanov N, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KCJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Muntenu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhermesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Willms K, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Bakas S, Rudie JD, Aboian M. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ARXIV 2024:arXiv:2306.00838v2. [PMID: 37396600 PMCID: PMC10312806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.
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Affiliation(s)
| | - Anastasia Janas
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Ujjwal Baid
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Rachit Saluja
- Department of Electical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Nader Ashraf
- ImagineQuant, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Leon Jekel
- DKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, Essen, Germany
| | - Raisa Amiruddin
- ImagineQuant, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maruf Adewole
- Medical Artificial Intelligence Lab, Crestview Radiology, Lagos, Nigeria
| | | | - Udunna Anazodo
- Montreal Neurological Institute, McGill University, Montreal, Canada
- Medical Artificial Intelligence (MAI) lab, Crestview Radiology, Lagos, Nigeria
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Syed Muhammad Anwar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, D.C., USA
| | | | - Evan Calabrese
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Veronica Chiang
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | | | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
| | - Ariana Familiar
- Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Zhifan Jiang
- Children’s National Hospital, Washington, D.C., USA
| | - Elaine Johanson
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Anahita Fathi Kazerooni
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Division of Neurosurgery, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Data-Driven Discovery in Biomedicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Kiril Krantchev
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dominic LaBella
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | - Hongwei Bran Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, D.C., USA
- Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
| | | | - Xinyang Liu
- Children’s National Hospital, Washington, D.C., USA
| | - Nazanin Maleki
- ImagineQuant, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Bjoern H Menze
- Biomedical Image Analysis & Machine Learning, Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Harrison Moy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Klara Osenberg
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Russel Takeshi Shinohara
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Yuri S. Velichko
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA
| | - Chunhao Wang
- Duke University School of Medicine, Durham, NC, USA
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | | | - Umber Shafique
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
| | - Klara Willms
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Arman Avesta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Satrajit Chakrabarty
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
- GE HealthCare, San Ramon, CA, USA
| | - Nicolo Gennaro
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA
| | | | - Manpreet Kaur
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Jan Lost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ryan Maresca
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Sarah Merkaj
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | | | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Niklas Tillmans
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | | | | | - Scott Floyd
- Duke University Medical Center, Durham, NC, USA
| | - Andreas Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, CA, USA
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jaeyoung Cho
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Justin Cramer
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
| | | | | | - Anthony Kam
- Loyola University Medical Center, Hines, IL, USA
| | | | - Lillian Lai
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | | | - Fatima Memon
- Carolina Radiology Associates, Myrtle Beach, SC, USA
- McLeod Regional Medical Center, Florence, SC, USA
- Medical University of South Carolina, Charleston, SC, USA
| | | | | | - Tiffany Y. So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR
| | - Gerard Thompson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Department of Clinical Neurosciences, NHS Lothian, Edinburgh, United Kingdom
| | - Lei Wu
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - E. Brooke Schrickel
- Department of Radiology, Ohio State University College of medicine, Columbus, OH, USA
| | - Anu Bansal
- Albert Einstein Medical Center, Hartford, CT, USA
| | - Frederik Barkhof
- Amsterdam UMC, location Vrije Universiteir, the Netherlands
- University College London, United Kingdom
| | | | - Sammy Chu
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Jason Druzgal
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | | | - Luciano Farage
- Centro Universitario Euro-Americana (UNIEURO), Brasília, DF, Brazil
| | - Fabricio Feltrin
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Amy Fong
- Southern District Health Board, Dunedin, New Zealand
| | - Steve H. Fung
- Department of Radiology, Houston Methodist, Houston, TX, USA
| | - R. Ian Gray
- University of Tennessee medical Center, Knoxville, TN, USA
| | - Ichiro Ikuta
- Mayo Clinic, Department of Radiology, Section of Neuroradiology, Phoenix, AZ, USA
| | - Michael Iv
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Alida A. Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- Mental Health and Neuroscience research institute, Maastricht University, Maastricht, the Netherlands
| | - Amit Mahajan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - David Joyner
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Chase Krumpelman
- Department of Radiology, University of Northwestern, Chicago, IL, USA
| | | | | | - Mate E. Maros
- Departments of Neuroradiology & Biomedical Informatics, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Elka Miller
- Department of Diagnostic and Interventional Radiology, SickKids Hospital, University of Toronto, Canada
| | - Fanny Morón
- Department of Radiology, Baylor College of medicine, Houston, TX, USA
| | | | - Ozkan Ozsarlak
- Department of Radiology, AZ Monica, Antwerp Area, Belgium
| | - Uresh Patel
- Medicolegal Imaging Experts LLC, Mercer Island, WA, USA
| | - Saurabh Rohatgi
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Atin Saha
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Anousheh Sayah
- MedStar Georgetown University Hospital, Washington, D.C., USA
| | - Eric D. Schwartz
- Department of Radiology, St.Elizabeth’s Medical Center, Boston, MA, USA
- Department of Radiology, Tufts University School of Medicine, Boston, MA, USA
| | - Robert Shih
- Walter Reed National Military Medical Center, Bethesda, MD, USA
| | | | | | | | - Jewels Valerie
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Brent D. Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | | | - Robert Young
- George Washington University, Washington, D.C., USA
| | - Vahe M. Zohrabian
- Northwell Health, Zucker Hofstra School of Medicine at Northwell, North Shore University Hospital, Hempstead, New York, NY, USA
| | - Aynur Azizova
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | | | - Pascal Fehringer
- Faculty of Medicine, Jena University Hospital, Friedrich Schiller University Jena, Jena, Germany
| | - Mohanad Ghonim
- Department of Radiology, Ain Shams University, Cairo, Egypt
| | - Mohamed Ghonim
- Department of Radiology, Ain Shams University, Cairo - Egypt
| | | | | | - Luca Pasquini
- Radiology Department, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | | | - Gagandeep Singh
- Columbia University Irving Medical Center, New York, NY, USA
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | - Nikolay Yordanov
- Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Francesco Dellepiane
- Functional and Interventional Neuroradiology Unit, Bambino Gesù Children’s Hospital, Rome, Italy
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Víctor M. Pérez-García
- Mathematical Oncology Laboratory & Department of Mathematics, University of Castilla-La Mancha, Spain
| | - Hesham Elhalawani
- Department of Radiation Oncology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sanaria Al-Rubaiey
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Rui Duarte Armindo
- Department of Neuroradiology, Western Lisbon Hospital Centre (CHLO), Portugal
| | | | | | - Mohamed Badawy
- Diagnostic Radiology Department, Wayne State University, Detroit, MI
| | - Jeroen Bisschop
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Jan Bukatz
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Jim Chen
- Department of Radiology/Division of Neuroradiology, San Diego Veterans Administration Medical Center/UC San Diego Health System, San Diego, CA, USA
| | - Petra Cimflova
- Department of Radiology, University of Calgary, Calgary, Canada
| | - Felix Corr
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, Kalkara, Malta
| | | | - Lisa Deptula
- Ross University School of Medicine, Bridgetown, Barbados
| | | | | | | | - Alexandra Frick
- Department of Neurosurgery, Vivantes Klinikum Neukölln, Berlin, Germany
| | | | | | - Fátima Hierro
- Neuroradiology Department, Pedro Hispano Hospital, Matosinhos, Portugal
| | - Rasmus Holmboe Dahl
- Department of Radiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Sarah Maria Jacobs
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Sedat G. Kandemirli
- Department of Radiology, University of Iowa Hospital and Clinics, Iowa City, IA, USA
| | - Katharina Kersting
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Laura Kida
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Sofia Kollia
- National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | | | - Xiao Li
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
| | - Ahmed Abouelatta
- Department of Diagnostic and Interventional Radiology, Cairo University, Cairo, Egypt
| | | | - Ruxandra-Catrinel Maria-Zamfirescu
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Marcela Marsiglia
- Department of Radiology, Brigham and Women’s Hospital, Massachusetts General Hospital, Boston, MA, USA
| | | | - Mark McArthur
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Maire McHugh
- Department of Radiology Manchester NHS Foundation Trust, North West School of Radiology, Manchester, United Kingdom
| | - Mana Moassefi
- Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Khanak K. Nandolia
- Department of Radiodiagnosis, All India Institute of Medical Sciences Rishikesh, India
| | - Syed Raza Naqvi
- Windsor Regional Hospital, Western University, Ontario, Canada
| | - Yalda Nikanpour
- Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Mostafa Alnoury
- Department of Radiology, University of Pennsylvania, PA, USA
| | | | - Francesca Pappafava
- Department of Medicine and Surgery, Università degli Studi di Perugia, Italy
| | - Markand D. Patel
- Department of Neuroradiology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Samantha Petrucci
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Eric Rawie
- Department of Radiology, Michigan Medicine, Ann Arbor, MI, USA
| | - Scott Raymond
- Department of Radiology, University of Vermont Medical Center, Burlington, VT, USA
| | - Borna Roohani
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Sadeq Sabouhi
- Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Zoe Shaked
- Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | | | - Talissa Altes
- Radiology Department, University of Missouri, Columbia, MO, USA
| | | | | | | | | | - Abdul Rahman Tarabishy
- Department of NeuroRadiology, Rockefeller Neuroscience Institute, West Virginia University. Morgantown, WV, USA
| | | | - Sebastiano Vacca
- University of Cagliari, School of Medicine and Surgery, Cagliari, Italy
| | - George K. Vilanilam
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Daniel Warren
- Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - David Weiss
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Klara Willms
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Fikadu Worede
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Wondwossen Lerebo
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | | | - Sarthak Pati
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center For Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA
- Medical Working Group, MLCommons, San Fransisco, CA, USA
| | | | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA
- Department of Neurological Surgery, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Jeffrey D. Rudie
- Department of Radiology, University of California San Diego, CA, USA
- Department of Radiology, Scripps Clinic Medical Group, CA, USA
| | - Mariam Aboian
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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9
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Everson RG, Hugo W, Sun L, Antonios J, Lee A, Ding L, Bu M, Khattab S, Chavez C, Billingslea-Yoon E, Salazar A, Ellingson BM, Cloughesy TF, Liau LM, Prins RM. TLR agonists polarize interferon responses in conjunction with dendritic cell vaccination in malignant glioma: a randomized phase II Trial. Nat Commun 2024; 15:3882. [PMID: 38719809 PMCID: PMC11078958 DOI: 10.1038/s41467-024-48073-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
In this randomized phase II clinical trial, we evaluated the effectiveness of adding the TLR agonists, poly-ICLC or resiquimod, to autologous tumor lysate-pulsed dendritic cell (ATL-DC) vaccination in patients with newly-diagnosed or recurrent WHO Grade III-IV malignant gliomas. The primary endpoints were to assess the most effective combination of vaccine and adjuvant in order to enhance the immune potency, along with safety. The combination of ATL-DC vaccination and TLR agonist was safe and found to enhance systemic immune responses, as indicated by increased interferon gene expression and changes in immune cell activation. Specifically, PD-1 expression increases on CD4+ T-cells, while CD38 and CD39 expression are reduced on CD8+ T cells, alongside an increase in monocytes. Poly-ICLC treatment amplifies the induction of interferon-induced genes in monocytes and T lymphocytes. Patients that exhibit higher interferon response gene expression demonstrate prolonged survival and delayed disease progression. These findings suggest that combining ATL-DC with poly-ICLC can induce a polarized interferon response in circulating monocytes and CD8+ T cells, which may represent an important blood biomarker for immunotherapy in this patient population.Trial Registration: ClinicalTrials.gov Identifier: NCT01204684.
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Affiliation(s)
- Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Willy Hugo
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, Division of Dermatology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Parker Institute for Cancer Immunotherapy, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Lu Sun
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Joseph Antonios
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Alexander Lee
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Lizhong Ding
- Department of Medicine, Division of Dermatology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Melissa Bu
- Department of Medicine, Division of Dermatology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Sara Khattab
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Carolina Chavez
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Emma Billingslea-Yoon
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | | | - Benjamin M Ellingson
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Timothy F Cloughesy
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Neurology/Neuro-Oncology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA.
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA.
| | - Robert M Prins
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA.
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA.
- Parker Institute for Cancer Immunotherapy, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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10
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Sanvito F, Raymond C, Cho NS, Yao J, Hagiwara A, Orpilla J, Liau LM, Everson RG, Nghiemphu PL, Lai A, Prins R, Salamon N, Cloughesy TF, Ellingson BM. Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI. Eur Radiol 2024; 34:3087-3101. [PMID: 37882836 PMCID: PMC11045669 DOI: 10.1007/s00330-023-10215-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/05/2023] [Accepted: 07/27/2023] [Indexed: 10/27/2023]
Abstract
OBJECTIVE To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection. MATERIALS AND METHODS Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression). RESULTS In IDH wild-type gliomas (IDHwt-i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09-0.14). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%. CONCLUSIONS Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR1,ss), with a single contrast injection. CLINICAL RELEVANCE STATEMENT Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture. KEY POINTS • Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Viale Camillo Golgi 19, 27100, Pavia, Italy
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Radiology, Juntendo University School of Medicine, Bunkyo City, 2-Chōme-1-1 Hongō, Tokyo, 113-8421, Japan
| | - Joey Orpilla
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Robert Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
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11
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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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12
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Vaz-Salgado MÁ, García BC, Pérez IF, Munárriz BJ, Domarco PS, González AH, Villar MV, Caro RL, Delgado MLV, Sánchez JMS. SEOM-GEINO clinical guidelines for grade 2 gliomas (2023). Clin Transl Oncol 2024:10.1007/s12094-024-03456-x. [PMID: 38662171 DOI: 10.1007/s12094-024-03456-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/08/2024] [Indexed: 04/26/2024]
Abstract
The 2021 World Health Organization (WHO) classification has updated the definition of grade 2 gliomas and the presence of isocitrate dehydrogenase (IDH) mutation has been deemed the cornerstone of diagnosis. Though slow-growing and having a low proliferative index, grade 2 gliomas are incurable by surgery and complementary treatments are vital to improving prognosis. This guideline provides recommendations on the multidisciplinary treatment of grade 2 astrocytomas and oligodendrogliomas based on the best evidence available.
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Affiliation(s)
- María Ángeles Vaz-Salgado
- Medical Oncology Department, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (Irycis) CIBERONC, Madrid, Spain.
| | - Belén Cigarral García
- Medical Oncology Department, Complejo Asistencial Universitario de Salamanca, Salamanca, Spain
| | - Isaura Fernández Pérez
- Medical Oncology Department, Hospital Alvaro Cunqueiro-Complejo Hospitalario Universitario de Vigo, Pontevedra, Spain
| | | | - Paula Sampedro Domarco
- Medical Oncology Department, Complexo Hospitalario Universitario de Ourense (CHUO), Orense, Spain
| | - Ainhoa Hernández González
- Medical Oncology Department, Hospital Germans Trias I Pujol(ICO)-Badalona, Instituto Catalán de Oncología, Barcelona, Spain
| | - María Vieito Villar
- Medical Oncology Department, Hospital Universitario Vall D'Hebron, Barcelona, Spain
| | - Raquel Luque Caro
- Medical Oncology Department, Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria Ibs.Granada, Granada, Spain
| | | | - Juan Manuel Sepúlveda Sánchez
- Neuro-Oncology Unit, HM Universitario Sanchinarro-CIOCC, Madrid, Spain.
- Medical Oncology Department, Hospital Universitario 12 de Octubre, Instituto de Investigación 12 de Octubre (I+12), Madrid, Spain.
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13
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Falcó-Roget J, Cacciola A, Sambataro F, Crimi A. Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations. Commun Biol 2024; 7:419. [PMID: 38582867 PMCID: PMC10998892 DOI: 10.1038/s42003-024-06119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.
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Affiliation(s)
- Joan Falcó-Roget
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy
| | - Fabio Sambataro
- Department of Neuroscience, University of Padova, Padua, Italy
| | - Alessandro Crimi
- Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland.
- Faculty of Computer Science, AGH University of Krakow, Kraków, Poland.
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14
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Foltyn-Dumitru M, Schell M, Rastogi A, Sahm F, Kessler T, Wick W, Bendszus M, Brugnara G, Vollmuth P. Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes. Eur Radiol 2024; 34:2782-2790. [PMID: 37672053 PMCID: PMC10957611 DOI: 10.1007/s00330-023-10034-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. METHODS Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160). RESULTS Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. CONCLUSION Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. CLINICAL RELEVANCE STATEMENT Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. KEY POINTS • MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Division of Medical Image Computing (MIC), German Cancer Research Center (DFKZ), Heidelberg, Germany.
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15
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Yan Q, Yan X, Yang X, Li S, Song J. The use of PET/MRI in radiotherapy. Insights Imaging 2024; 15:63. [PMID: 38411742 PMCID: PMC10899128 DOI: 10.1186/s13244-024-01627-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 01/21/2024] [Indexed: 02/28/2024] Open
Abstract
Positron emission tomography/magnetic resonance imaging (PET/MRI) is a hybrid imaging technique that quantitatively combines the metabolic and functional data from positron emission tomography (PET) with anatomical and physiological information from MRI. As PET/MRI technology has advanced, its applications in cancer care have expanded. Recent studies have demonstrated that PET/MRI provides unique advantages in the field of radiotherapy and has become invaluable in guiding precision radiotherapy techniques. This review discusses the rationale and clinical evidence supporting the use of PET/MRI for radiation positioning, target delineation, efficacy evaluation, and patient surveillance.Critical relevance statement This article critically assesses the transformative role of PET/MRI in advancing precision radiotherapy, providing essential insights into improved radiation positioning, target delineation, efficacy evaluation, and patient surveillance in clinical radiology practice.Key points• The emergence of PET/MRI will be a key bridge for precise radiotherapy.• PET/MRI has unique advantages in the whole process of radiotherapy.• New tracers and nanoparticle probes will broaden the use of PET/MRI in radiation.• PET/MRI will be utilized more frequently for radiotherapy.
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Affiliation(s)
- Qi Yan
- Cancer Center, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Taiyuan, China
| | - Xia Yan
- Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory for Translational Nuclear Medicine and Precision Protection, Taiyuan, China
| | - Xin Yang
- Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Sijin Li
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, China.
| | - Jianbo Song
- Cancer Center, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences Tongji Shanxi Hospital, Taiyuan, China.
- Shanxi Provincial Key Laboratory for Translational Nuclear Medicine and Precision Protection, Taiyuan, China.
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16
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Liu H, Ni Z, Nie D, Shen D, Wang J, Tang Z. Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:1199-1210. [PMID: 38315584 DOI: 10.1109/tip.2024.3359815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Many deep learning based methods have been proposed for brain tumor segmentation. Most studies focus on deep network internal structure to improve the segmentation accuracy, while valuable external information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists often screen lesion regions with normal appearance as reference in mind, in this paper, we propose a novel deep framework for brain tumor segmentation, where normal brain images are adopted as reference to compare with tumor brain images in a learned feature space. In this way, features at tumor regions, i.e., tumor-related features, can be highlighted and enhanced for accurate tumor segmentation. It is known that routine tumor brain images are multimodal, while normal brain images are often monomodal. This causes the feature comparison a big issue, i.e., multimodal vs. monomodal. To this end, we present a new feature alignment module (FAM) to make the feature distribution of monomodal normal brain images consistent/inconsistent with multimodal tumor brain images at normal/tumor regions, making the feature comparison effective. Both public (BraTS2022) and in-house tumor brain image datasets are used to evaluate our framework. Experimental results demonstrate that for both datasets, our framework can effectively improve the segmentation accuracy and outperforms the state-of-the-art segmentation methods. Codes are available at https://github.com/hb-liu/Normal-Brain-Boost-Tumor-Segmentation.
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17
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Lauer EM, Riegler E, Mutter JA, Alig SK, Bleul S, Kuehn J, Ranganathan L, Klingler C, Demerath T, Würtemberger U, Rau A, Weiß J, Eisenblaetter M, Bamberg F, Prinz M, Finke J, Duyster J, Illerhaus G, Diehn M, Alizadeh AA, Schorb E, Reinacher PC, Scherer F. Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas. Neuro Oncol 2024; 26:374-386. [PMID: 37713267 PMCID: PMC10836777 DOI: 10.1093/neuonc/noad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment. METHODS We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment. RESULTS At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL. CONCLUSIONS We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.
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Affiliation(s)
- Eliza M Lauer
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ella Riegler
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jurik A Mutter
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Divisions of Oncology and Hematology, Department of Medicine, Stanford University, Stanford, CA, USA
| | | | - Sabine Bleul
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julia Kuehn
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lavanya Ranganathan
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian Klingler
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Theo Demerath
- Department of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany
| | - Urs Würtemberger
- Department of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany
- Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jakob Weiß
- Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michel Eisenblaetter
- Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Prinz
- Institute of Neuropathology, Medical Faculty, University of Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Jürgen Finke
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Justus Duyster
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gerald Illerhaus
- Department of Hematology/Oncology and Palliative Care, Klinikum Stuttgart, Stuttgart, Germany
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA
| | - Ash A Alizadeh
- Divisions of Oncology and Hematology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Elisabeth Schorb
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter C Reinacher
- Department of Stereotactic and Functional Neurosurgery, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Fraunhofer Institute for Laser Technology (ILT), Aachen, Germany
| | - Florian Scherer
- Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Cite Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
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18
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Park YW, Kim S, Han K, Ahn SS, Moon JH, Kim EH, Kim J, Kang SG, Kim SH, Lee SK, Chang JH. Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification. Eur Radiol 2024; 34:1376-1387. [PMID: 37608093 DOI: 10.1007/s00330-023-10125-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/22/2023] [Accepted: 07/01/2023] [Indexed: 08/24/2023]
Abstract
OBJECTIVES Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification. METHODS This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor. RESULTS Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05). CONCLUSIONS GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended. CLINICAL RELEVANCE STATEMENT Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas. KEY POINTS The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sooyon Kim
- Department of Statistics and Data Science, Yonsei University, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jinna Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
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19
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Fenwick N, Weston R, Wheatley K, Hodgson J, Marshall L, Elliott M, Makin G, Ng A, Brennan B, Lowis S, Adamski J, Kilday JP, Cox R, Gattens M, Moore A, Trahair T, Ronghe M, Campbell M, Campbell H, Williams MW, Kirby M, Van Eijkelenburg N, Keely J, Scarpa U, Stavrou V, Fultang L, Booth S, Cheng P, De Santo C, Mussai F. PARC: a phase I/II study evaluating the safety and activity of pegylated recombinant human arginase BCT-100 in relapsed/refractory cancers of children and young adults. Front Oncol 2024; 14:1296576. [PMID: 38357205 PMCID: PMC10864630 DOI: 10.3389/fonc.2024.1296576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
Background The survival for many children with relapsed/refractory cancers remains poor despite advances in therapies. Arginine metabolism plays a key role in the pathophysiology of a number of pediatric cancers. We report the first in child study of a recombinant human arginase, BCT-100, in children with relapsed/refractory hematological, solid or CNS cancers. Procedure PARC was a single arm, Phase I/II, international, open label study. BCT-100 was given intravenously over one hour at weekly intervals. The Phase I section utilized a modified 3 + 3 design where escalation/de-escalation was based on both the safety profile and the complete depletion of arginine (defined as adequate arginine depletion; AAD <8μM arginine in the blood after 4 doses of BCT-100). The Phase II section was designed to further evaluate the clinical activity of BCT-100 at the pediatric RP2D determined in the Phase I section, by recruitment of patients with pediatric cancers into 4 individual groups. A primary evaluation of response was conducted at eight weeks with patients continuing to receive treatment until disease progression or unacceptable toxicity. Results 49 children were recruited globally. The Phase I cohort of the trial established the Recommended Phase II Dose of 1600U/kg iv weekly in children, matching that of adults. BCT-100 was very well tolerated. No responses defined as a CR, CRi or PR were seen in any cohort within the defined 8 week primary evaluation period. However a number of these relapsed/refractory patients experienced prolonged radiological SD. Conclusion Arginine depletion is a clinically safe and achievable strategy in children with cancer. The RP2D of BCT-100 in children with relapsed/refractory cancers is established at 1600U/kg intravenously weekly and can lead to sustained disease stability in this hard to treat population. Clinical trial registration EudraCT, 2017-002762-44; ISRCTN, 21727048; and ClinicalTrials.gov, NCT03455140.
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Affiliation(s)
- Nicola Fenwick
- Children’s Cancer Trials Team, Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, United Kingdom
| | - Rebekah Weston
- Children’s Cancer Trials Team, Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, United Kingdom
| | - Keith Wheatley
- Children’s Cancer Trials Team, Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, United Kingdom
| | - Jodie Hodgson
- Children’s Cancer Trials Team, Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, United Kingdom
| | | | - Martin Elliott
- Leeds Teaching Hospital, St James University Hospital, Leeds, United Kingdom
| | - Guy Makin
- Royal Manchester Children’s Hospital, Manchester, United Kingdom
| | - Antony Ng
- Bristol Royal Hospital for Children, Bristol, United Kingdom
| | | | - Stephen Lowis
- Bristol Royal Hospital for Children, Bristol, United Kingdom
| | - Jenny Adamski
- Birmingham Children’s Hospital, Birmingham, United Kingdom
| | - John Paul Kilday
- Royal Manchester Children’s Hospital, Manchester, United Kingdom
| | - Rachel Cox
- Bristol Royal Hospital for Children, Bristol, United Kingdom
| | - Mike Gattens
- Addenbrookes Hospital, Cambridge, United Kingdom
| | - Andrew Moore
- Queensland Children’s Hospital, Brisbane, QLD, Australia
| | - Toby Trahair
- Sydney Children’s Hospital, Sydney, NSW, Australia
| | - Milind Ronghe
- Royal Hospital for Children, Glasgow, United Kingdom
| | | | - Helen Campbell
- Royal Manchester Children’s Hospital, Manchester, United Kingdom
| | | | - Maria Kirby
- Michael Rice Cancer Centre, Women’s and Children’s Hospital, North Adelaide, SA, Australia
| | | | - Jennifer Keely
- Children’s Cancer Trials Team, Cancer Research UK Clinical Trials Unit (CRCTU), University of Birmingham, Birmingham, United Kingdom
| | - Ugo Scarpa
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
| | - Victoria Stavrou
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
| | - Livingstone Fultang
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
| | - Sarah Booth
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
| | - Paul Cheng
- Bio-Cancer Treatment International, Hong Kong Science Park, Hong Kong, Hong Kong SAR, China
| | - Carmela De Santo
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
| | - Francis Mussai
- Birmingham Children’s Hospital, Birmingham, United Kingdom
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20
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Karlberg A, Pedersen LK, Vindstad BE, Skjulsvik AJ, Johansen H, Solheim O, Skogen K, Kvistad KA, Bogsrud TV, Myrmel KS, Giskeødegård GF, Ingebrigtsen T, Berntsen EM, Eikenes L. Diagnostic accuracy of anti-3-[ 18F]-FACBC PET/MRI in gliomas. Eur J Nucl Med Mol Imaging 2024; 51:496-509. [PMID: 37776502 PMCID: PMC10774221 DOI: 10.1007/s00259-023-06437-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/06/2023] [Indexed: 10/02/2023]
Abstract
PURPOSE The primary aim was to evaluate whether anti-3-[18F]FACBC PET combined with conventional MRI correlated better with histomolecular diagnosis (reference standard) than MRI alone in glioma diagnostics. The ability of anti-3-[18F]FACBC to differentiate between molecular and histopathological entities in gliomas was also evaluated. METHODS In this prospective study, patients with suspected primary or recurrent gliomas were recruited from two sites in Norway and examined with PET/MRI prior to surgery. Anti-3-[18F]FACBC uptake (TBRpeak) was compared to histomolecular features in 36 patients. PET results were then added to clinical MRI readings (performed by two neuroradiologists, blinded for histomolecular results and PET data) to assess the predicted tumor characteristics with and without PET. RESULTS Histomolecular analyses revealed two CNS WHO grade 1, nine grade 2, eight grade 3, and 17 grade 4 gliomas. All tumors were visible on MRI FLAIR. The sensitivity of contrast-enhanced MRI and anti-3-[18F]FACBC PET was 61% (95%CI [45, 77]) and 72% (95%CI [58, 87]), respectively, in the detection of gliomas. Median TBRpeak was 7.1 (range: 1.4-19.2) for PET positive tumors. All CNS WHO grade 1 pilocytic astrocytomas/gangliogliomas, grade 3 oligodendrogliomas, and grade 4 glioblastomas/astrocytomas were PET positive, while 25% of grade 2-3 astrocytomas and 56% of grade 2-3 oligodendrogliomas were PET positive. Generally, TBRpeak increased with malignancy grade for diffuse gliomas. A significant difference in PET uptake between CNS WHO grade 2 and 4 gliomas (p < 0.001) and between grade 3 and 4 gliomas (p = 0.002) was observed. Diffuse IDH wildtype gliomas had significantly higher TBRpeak compared to IDH1/2 mutated gliomas (p < 0.001). Adding anti-3-[18F]FACBC PET to MRI improved the accuracy of predicted glioma grades, types, and IDH status, and yielded 13.9 and 16.7 percentage point improvement in the overall diagnoses for both readers, respectively. CONCLUSION Anti-3-[18F]FACBC PET demonstrated high uptake in the majority of gliomas, especially in IDH wildtype gliomas, and improved the accuracy of preoperatively predicted glioma diagnoses. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov ID: NCT04111588, URL: https://clinicaltrials.gov/study/NCT04111588.
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Affiliation(s)
- Anna Karlberg
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway.
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
| | | | - Benedikte Emilie Vindstad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Jarstein Skjulsvik
- Department of Pathology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Faculty of Medical and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Håkon Johansen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway
| | - Ole Solheim
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Karoline Skogen
- Department of Radiology and Nuclear Medicine, Oslo University Hospitals, Oslo, Norway
| | - Kjell Arne Kvistad
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway
| | - Trond Velde Bogsrud
- PET-Centre, University Hospital of North Norway, Tromsø, Norway
- Department of Nuclear Medicine and PET-Centre, Aarhus University Hospital, Aarhus, Denmark
| | | | - Guro F Giskeødegård
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tor Ingebrigtsen
- Department of Neurosurgery, University Hospital of North Norway, Tromsø, Norway
- Department of Clinical Medicine, Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway
| | - Erik Magnus Berntsen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Prinsesse Kristinas gate 3, N-7030, Trondheim, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
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21
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Lewis EM, Mao L, Wang L, Swanson KR, Barajas RF, Li J, Tran NL, Hu LS, Plaisier CL. Revealing the biology behind MRI signatures in high grade glioma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.08.23299733. [PMID: 38168377 PMCID: PMC10760280 DOI: 10.1101/2023.12.08.23299733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.
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Affiliation(s)
- Erika M Lewis
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
| | - Lingchao Mao
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Kristin R Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, 85054, USA
- Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Ramon F Barajas
- Advanced Imaging Research Center, Oregon Health & Sciences University, USA
- Department of Radiology, Neuroradiology Section, Oregon Health & Sciences University, USA
- Knight Cancer Institute, Oregon Health & Sciences University, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Nhan L Tran
- Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85054, USA
- Department of Cancer Biology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Leland S Hu
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, 85054, USA
- Department of Radiology, Mayo Clinic, Phoenix, AZ, 85054, USA
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Christopher L Plaisier
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA
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22
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Sanvito F, Kaufmann TJ, Cloughesy TF, Wen PY, Ellingson BM. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. FRONTIERS IN RADIOLOGY 2023; 3:1267615. [PMID: 38152383 PMCID: PMC10751345 DOI: 10.3389/fradi.2023.1267615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA, United States
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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23
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Hu JY, Vaziri S, Bøgh N, Kim Y, Autry AW, Bok RA, Li Y, Laustsen C, Xu D, Larson PEZ, Chang S, Vigneron DB, Gordon JW. Investigating cerebral perfusion with high resolution hyperpolarized [1- 13 C]pyruvate MRI. Magn Reson Med 2023; 90:2233-2241. [PMID: 37665726 PMCID: PMC10543485 DOI: 10.1002/mrm.29844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE To investigate high-resolution hyperpolarized (HP) 13 C pyruvate MRI for measuring cerebral perfusion in the human brain. METHODS HP [1-13 C]pyruvate MRI was acquired in five healthy volunteers with a multi-resolution EPI sequence with 7.5 × 7.5 mm2 resolution for pyruvate. Perfusion parameters were calculated from pyruvate MRI using block-circulant singular value decomposition and compared to relative cerebral blood flow calculated from arterial spin labeling (ASL). To examine regional perfusion patterns, correlations between pyruvate and ASL perfusion were performed for whole brain, gray matter, and white matter voxels. RESULTS High resolution 7.5 × 7.5 mm2 pyruvate images were used to obtain relative cerebral blood flow (rCBF) values that were significantly positively correlated with ASL rCBF values (r = 0.48, 0.20, 0.28 for whole brain, gray matter, and white matter voxels respectively). Whole brain voxels exhibited the highest correlation between pyruvate and ASL perfusion, and there were distinct regional patterns of relatively high ASL and low pyruvate normalized rCBF found across subjects. CONCLUSION Acquiring HP 13 C pyruvate metabolic images at higher resolution allows for finer spatial delineation of brain structures and can be used to obtain cerebral perfusion parameters. Pyruvate perfusion parameters were positively correlated to proton ASL perfusion values, indicating a relationship between the two perfusion measures. This HP 13 C study demonstrated that hyperpolarized pyruvate MRI can assess cerebral metabolism and perfusion within the same study.
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Affiliation(s)
- Jasmine Y. Hu
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Sana Vaziri
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Nikolaj Bøgh
- MR Research Center, Department of Clinical Medicine, Aarhus
University, Aarhus, Denmark
| | - Yaewon Kim
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Adam W. Autry
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Robert A. Bok
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
| | - Yan Li
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Christoffer Laustsen
- MR Research Center, Department of Clinical Medicine, Aarhus
University, Aarhus, Denmark
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Peder E. Z. Larson
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Susan Chang
- Department of Neurological Surgery, University of
California San Francisco, San Francisco, California, USA
| | - Daniel B. Vigneron
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering,
University of California, San Francisco and University of California, Berkeley,
California, USA
| | - Jeremy W. Gordon
- Department of Radiology and Biomedical Imaging, University
of California San Francisco, San Francisco, California, USA
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24
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Sun W, Wang C, Tian C, Li X, Hu X, Liu S. Nanotechnology for brain tumor imaging and therapy based on π-conjugated materials: state-of-the-art advances and prospects. Front Chem 2023; 11:1301496. [PMID: 38025074 PMCID: PMC10663370 DOI: 10.3389/fchem.2023.1301496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
In contemporary biomedical research, the development of nanotechnology has brought forth numerous possibilities for brain tumor imaging and therapy. Among these, π-conjugated materials have garnered significant attention as a special class of nanomaterials in brain tumor-related studies. With their excellent optical and electronic properties, π-conjugated materials can be tailored in structure and nature to facilitate applications in multimodal imaging, nano-drug delivery, photothermal therapy, and other related fields. This review focuses on presenting the cutting-edge advances and application prospects of π-conjugated materials in brain tumor imaging and therapeutic nanotechnology.
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Affiliation(s)
- Wenshe Sun
- Department of Interventional Medical Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Qingdao Cancer Institute, Qingdao University, Qingdao, China
| | - Congxiao Wang
- Department of Interventional Medical Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Chuan Tian
- Department of Interventional Medical Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xueda Li
- Department of Interventional Medical Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiaokun Hu
- Department of Interventional Medical Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shifeng Liu
- Department of Interventional Medical Center, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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25
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Pemberton HG, Wu J, Kommers I, Müller DMJ, Hu Y, Goodkin O, Vos SB, Bisdas S, Robe PA, Ardon H, Bello L, Rossi M, Sciortino T, Nibali MC, Berger MS, Hervey-Jumper SL, Bouwknegt W, Van den Brink WA, Furtner J, Han SJ, Idema AJS, Kiesel B, Widhalm G, Kloet A, Wagemakers M, Zwinderman AH, Krieg SM, Mandonnet E, Prados F, de Witt Hamer P, Barkhof F, Eijgelaar RS. Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms. Sci Rep 2023; 13:18911. [PMID: 37919354 PMCID: PMC10622563 DOI: 10.1038/s41598-023-44794-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/12/2023] [Indexed: 11/04/2023] Open
Abstract
This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jiaming Wu
- Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Ivar Kommers
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Domenique M J Müller
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Yipeng Hu
- Centre for Medical Image Computing (CMIC), University College London, London, UK
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Pierre A Robe
- Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hilko Ardon
- Department of Neurosurgery, St. Elisabeth Hospital, Tilburg, The Netherlands
| | - Lorenzo Bello
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Rossi
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Tommaso Sciortino
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Conti Nibali
- Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Shawn L Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Wim Bouwknegt
- Department of Neurosurgery, Medical Center Slotervaart, Amsterdam, The Netherlands
| | | | - Julia Furtner
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Seunggu J Han
- Department of Neurological Surgery, Stanford University, Stanford, USA
| | - Albert J S Idema
- Department of Neurosurgery, Northwest Clinics, Alkmaar, The Netherlands
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University Vienna, Vienna, Austria
| | - Alfred Kloet
- Department of Neurosurgery, Medical Center Haaglanden, The Hague, The Netherlands
| | - Michiel Wagemakers
- Department of Neurosurgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Aeilko H Zwinderman
- Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, Amsterdam, The Netherlands
| | - Sandro M Krieg
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | | | - Ferran Prados
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Institute of Neurology, University College London, London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Philip de Witt Hamer
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Roelant S Eijgelaar
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
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26
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Sollmann N, Zhang H, Kloth C, Zimmer C, Wiestler B, Rosskopf J, Kreiser K, Schmitz B, Beer M, Krieg SM. Modern preoperative imaging and functional mapping in patients with intracranial glioma. ROFO-FORTSCHR RONTG 2023; 195:989-1000. [PMID: 37224867 DOI: 10.1055/a-2083-8717] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Magnetic resonance imaging (MRI) in therapy-naïve intracranial glioma is paramount for neuro-oncological diagnostics, and it provides images that are helpful for surgery planning and intraoperative guidance during tumor resection, including assessment of the involvement of functionally eloquent brain structures. This study reviews emerging MRI techniques to depict structural information, diffusion characteristics, perfusion alterations, and metabolism changes for advanced neuro-oncological imaging. In addition, it reflects current methods to map brain function close to a tumor, including functional MRI and navigated transcranial magnetic stimulation with derived function-based tractography of subcortical white matter pathways. We conclude that modern preoperative MRI in neuro-oncology offers a multitude of possibilities tailored to clinical needs, and advancements in scanner technology (e. g., parallel imaging for acceleration of acquisitions) make multi-sequence protocols increasingly feasible. Specifically, advanced MRI using a multi-sequence protocol enables noninvasive, image-based tumor grading and phenotyping in patients with glioma. Furthermore, the add-on use of preoperatively acquired MRI data in combination with functional mapping and tractography facilitates risk stratification and helps to avoid perioperative functional decline by providing individual information about the spatial location of functionally eloquent tissue in relation to the tumor mass. KEY POINTS:: · Advanced preoperative MRI allows for image-based tumor grading and phenotyping in glioma.. · Multi-sequence MRI protocols nowadays make it possible to assess various tumor characteristics (incl. perfusion, diffusion, and metabolism).. · Presurgical MRI in glioma is increasingly combined with functional mapping to identify and enclose individual functional areas.. · Advancements in scanner technology (e. g., parallel imaging) facilitate increasing application of dedicated multi-sequence imaging protocols.. CITATION FORMAT: · Sollmann N, Zhang H, Kloth C et al. Modern preoperative imaging and functional mapping in patients with intracranial glioma. Fortschr Röntgenstr 2023; 195: 989 - 1000.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, München, Germany
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States
| | - Haosu Zhang
- Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, München, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Klinikum rechts der Isar, Technical University of Munich, München, Germany
| | - Johannes Rosskopf
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Section of Neuroradiology, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Kornelia Kreiser
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Radiology and Neuroradiology, Universitäts- und Rehabilitationskliniken Ulm, Ulm, Germany
| | - Bernd Schmitz
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Section of Neuroradiology, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Sandro M Krieg
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, München, Germany
- Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
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27
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Liu Y, De Feyter HM, Corbin ZA, Fulbright RK, McIntyre S, Nixon TW, de Graaf RA. Parallel detection of multi-contrast MRI and Deuterium Metabolic Imaging (DMI) for time-efficient characterization of neurological diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.02.23296408. [PMID: 37873422 PMCID: PMC10593017 DOI: 10.1101/2023.10.02.23296408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Deuterium Metabolic Imaging (DMI) is a novel method that can complement traditional anatomical magnetic resonance imaging (MRI) of the brain. DMI relies on the MR detection of metabolites that become labeled with deuterium (2H) after administration of a deuterated substrate and can provide images with highly specific metabolic information. However, clinical adoption of DMI is complicated by its relatively long scan time. Here, we demonstrate a strategy to interleave DMI data acquisition with MRI that results in a comprehensive neuro-imaging protocol without adding scan time. The interleaved MRI-DMI routine includes four essential clinical MRI scan types, namely T1-weighted MP-RAGE, FLAIR, T2-weighted Imaging (T2W) and susceptibility weighted imaging (SWI), interwoven with DMI data acquisition. Phantom and in vivo human brain data show that MR image quality, DMI sensitivity, as well as information content are preserved in the MRI-DMI acquisition method. The interleaved MRI-DMI technology provides full flexibility to upgrade traditional MRI protocols with DMI, adding unique metabolic information to existing types of anatomical image contrast, without extra scan time.
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Affiliation(s)
- Yanning Liu
- Department of Biomedical Engineering, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - Henk M. De Feyter
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - Zachary A. Corbin
- Department of Neurology, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - Robert K. Fulbright
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - Scott McIntyre
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - Terence W. Nixon
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
| | - Robin A. de Graaf
- Department of Biomedical Engineering, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, United States
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28
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Strowd R, Ellingson B, Raymond C, Yao J, Wen PY, Ahluwalia M, Piotrowski A, Desai A, Clarke JL, Lieberman FS, Desideri S, Nabors LB, Ye X, Grossman S. Activity of a first-in-class oral HIF2-alpha inhibitor, PT2385, in patients with first recurrence of glioblastoma. J Neurooncol 2023; 165:101-112. [PMID: 37864646 PMCID: PMC10863646 DOI: 10.1007/s11060-023-04456-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/19/2023] [Indexed: 10/23/2023]
Abstract
INTRODUCTION Hypoxia inducible factor 2-alpha (HIF2α) mediates cellular responses to hypoxia and is over-expressed in glioblastoma (GBM). PT2385 is an oral HIF2α inhibitor with in vivo activity against GBM. METHODS A two-stage single-arm open-label phase II study of adults with GBM at first recurrence following chemoradiation with measurable disease was conducted through the Adult Brain Tumor Consortium. PT2385 was administered at the phase II dose (800 mg b.i.d.). The primary outcome was objective radiographic response (ORR = complete response + partial response, CR + PR); secondary outcomes were safety, overall survival (OS), and progression free survival (PFS). Exploratory objectives included pharmacokinetics (day 15 Cmin), pharmacodynamics (erythropoietin, vascular endothelial growth factor), and pH-weighted amine- chemical exchange saturation transfer (CEST) MRI to quantify tumor acidity at baseline and explore associations with drug response. Stage 1 enrolled 24 patients with early stoppage for ≤ 1 ORR. RESULTS Of the 24 enrolled patients, median age was 62.1 (38.7-76.7) years, median KPS 80, MGMT promoter was methylated in 46% of tumors. PT2385 was well tolerated. Grade ≥ 3 drug-related adverse events were hypoxia (n = 2), hyponatremia (2), lymphopenia (1), anemia (1), and hyperglycemia (1). No objective radiographic responses were observed; median PFS was 1.8 months (95% CI 1.6-2.5) and OS was 7.7 months (95% CI 4.9-12.6). Drug exposure varied widely and did not differ by corticosteroid use (p = 0.12), antiepileptics (p = 0.09), or sex (p = 0.37). Patients with high systemic exposure had significantly longer PFS (6.7 vs 1.8 months, p = 0.009). Baseline acidity by pH-weighted CEST MRI correlated significantly with treatment duration (R2 = 0.49, p = 0.017). Non-enhancing infiltrative disease with high acidity gave rise to recurrence. CONCLUSIONS PT2385 monotherapy had limited activity in first recurrent GBM. Drug exposure was variable. Signals of activity were observed in GBM patients with high systemic exposure and acidic lesions on CEST imaging. A second-generation HIF2α inhibitor is being studied.
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Affiliation(s)
- Roy Strowd
- Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston Salem, NC, 27104, USA.
| | | | | | - Jingwen Yao
- University of California Los Angeles, Los Angeles, CA, USA
| | | | | | | | - Arati Desai
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - L Burt Nabors
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xiaobu Ye
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins, Baltimore, MD, USA
| | - Stuart Grossman
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins, Baltimore, MD, USA
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Onishi S, Yamasaki F, Amatya VJ, Takayasu T, Yonezawa U, Taguchi A, Ozono I, Khairunnisa NI, Takeshima Y, Horie N. Residual diffusion-weighted imaging hyperintense signal in primary central nervous system lymphoma can predict early recurrence. J Neurooncol 2023; 165:171-179. [PMID: 37831389 DOI: 10.1007/s11060-023-04473-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND The treatment response of primary central nervous system lymphomas (PCNSLs) is mainly evaluated using postcontrast T1-weighted imaging (T1WI). Because poorly enhanced lesions may contain residual tumors, the combination of evaluation methods will potentially improve the accuracy of determining treatment effectiveness. In this study, we evaluated the usefulness of diffusion-weighted imaging (DWI) in predicting recurrence among patients with PCNSL who achieved complete response (CR)/unconfirmed CR (CRu). METHODS Fifty-four patients newly diagnosed with PCNSL who were treated at our institution and achieved CR/CRu at the end of treatment were included in this study. The patients were divided into two groups according to the presence or absence of residual DWI hyperintense signal at the tumor site at the end of treatment. Kaplan-Meier analysis was performed to analyze the median overall survival (OS) and progression-free survival (PFS). RESULTS The mean age of the 54 patients was 66.4 ± 13.3 years. The induction therapies were HD-MTX in 20 patients, R-MPV in 29 patients, and other chemotherapies in five patients. Radiotherapy was performed in 35 patients, high-dose cytarabine therapy in 14 patients, and autologous hematopoietic stem cell transplantation in one patient, and of the 54 patients, 10 had no consolidation therapy. The residual DWI hyperintense signal sign was observed in 18 patients. The R-MPV regimen was statistically associated with a lower rate of residual DWI hyperintense signal (p = 0.0453). The median PFS was statistically shorter in the residual DWI hyperintense signal group than in the non-residual DWI hyperintense signal group (14.0 months vs. 85.1 months) (p < 0.0001, log-rank test). CONCLUSION A residual DWI hyperintense signal at the end of treatment was statistically associated with shorter PFS. Among patients who achieved CR/CRu evaluated based on postcontrast T1WI, DWI could be a valuable additional sequence to predict the early recurrence of PCNSL.
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Affiliation(s)
- Shumpei Onishi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Fumiyuki Yamasaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
| | - Vishwa Jeet Amatya
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takeshi Takayasu
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Ushio Yonezawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Akira Taguchi
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Iori Ozono
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Novita Ikbar Khairunnisa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yukio Takeshima
- Department of Pathology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
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30
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Cohen O, Otazo R. Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule. NMR IN BIOMEDICINE 2023; 36:e4954. [PMID: 37070221 PMCID: PMC10896067 DOI: 10.1002/nbm.4954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 05/06/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule.
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Affiliation(s)
- Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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31
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Veit-Haibach P, Ahlström H, Boellaard R, Delgado Bolton RC, Hesse S, Hope T, Huellner MW, Iagaru A, Johnson GB, Kjaer A, Law I, Metser U, Quick HH, Sattler B, Umutlu L, Zaharchuk G, Herrmann K. International EANM-SNMMI-ISMRM consensus recommendation for PET/MRI in oncology. Eur J Nucl Med Mol Imaging 2023; 50:3513-3537. [PMID: 37624384 PMCID: PMC10547645 DOI: 10.1007/s00259-023-06406-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023]
Abstract
PREAMBLE The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The merged International Society for Magnetic Resonance in Medicine (ISMRM) is an international, nonprofit, scientific association whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field.The ISMRM was founded in 1994 through the merger of the Society of Magnetic Resonance in Medicine and the Society of Magnetic Resonance Imaging. SNMMI, ISMRM, and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine and/or magnetic resonance imaging. The SNMMI, ISMRM, and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and/or magnetic resonance imaging and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each practice guideline, representing a policy statement by the SNMMI/EANM/ISMRM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI, ISMRM, and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging and magnetic resonance imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized. These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI, the ISMRM, and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines. The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment. Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.
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Affiliation(s)
- Patrick Veit-Haibach
- Joint Department Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, Toronto General Hospital, 1 PMB-275, 585 University Avenue, Toronto, Ontario, M5G 2N2, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden
- Antaros Medical AB, BioVenture Hub, 431 53, Mölndal, Sweden
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roberto C Delgado Bolton
- Department of Diagnostic Imaging (Radiology) and Nuclear Medicine, University Hospital San Pedro and Centre for Biomedical Research of La Rioja (CIBIR), Logroño, La Rioja, Spain
| | - Swen Hesse
- Department of Nuclear Medicine, University of Leipzig Medical Center, Leipzig, Germany
| | - Thomas Hope
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Andrei Iagaru
- Department of Radiology, Division of Nuclear Medicine, Stanford University Medical Center, Stanford, CA, USA
| | - Geoffrey B Johnson
- Division of Nuclear Medicine, Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Andreas Kjaer
- Department of Clinical Physiology, Nuclear Medicine & PET and Cluster for Molecular Imaging, Rigshospitalet and University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, Copenhagen, Denmark
| | - Ur Metser
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Harald H Quick
- High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Bernhard Sattler
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Greg Zaharchuk
- Division of Neuroradiology, Department of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA, 94305-5105, USA
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany.
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Everson RG, Hugo W, Sun L, Antonios J, Lee A, Ding L, Bu M, Khattab S, Chavez C, Billingslea-Yoon E, Salazar A, Ellingson BM, Cloughesy TF, Liau LM, Prins RM. Dendritic Cell Vaccination in Conjunction with a TLR Agonist Polarizes Interferon Immune Responses in Malignant Glioma Patients. RESEARCH SQUARE 2023:rs.3.rs-3287211. [PMID: 37790490 PMCID: PMC10543402 DOI: 10.21203/rs.3.rs-3287211/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Autologous tumor lysate-pulsed dendritic cell (ATL-DC) vaccination is a promising immunotherapy for patients with high grade gliomas, but responses have not been demonstrated in all patients. To determine the most effective combination of autologous tumor lysate-pulsed DC vaccination, with or without the adjuvant toll-like receptor (TLR) agonists poly-ICLC or resiquimod, we conducted a Phase 2 clinical trial in 23 patients with newly diagnosed or recurrent WHO Grade III-IV malignant gliomas. We then performed deep, high-dimensional immune profiling of these patients to better understand how TLR agonists may influence the systemic immune responses induced by ATL-DC vaccination. Bulk RNAseq data demonstrated highly significant upregulation of type 1 and type 2 interferon gene expression selectively in patients who received adjuvant a TLR agonist together with ATL-DC. CyTOF analysis of patient peripheral blood mononuclear cells (PBMCs) showed increased expression of PD-1 on CD4+ T-cells, decreases in CD38 and CD39 on CD8+ T cells and elevated proportion of monocytes after ATL-DC + TLR agonist administration. In addition, scRNA-seq demonstrated a higher expression fold change of IFN-induced genes with poly-ICLC treatment in both peripheral blood monocytes and T lymphocytes. Patients who had higher expression of interferon response genes lived significantly longer and had longer time to progression compared to those with lower expression. The results suggest that ATL-DC in conjunction with adjuvant poly-ICLC induces a polarized interferon response in circulating monocytes and specific activation of a CD8+ T cell population, which may represent an important blood biomarker for immunotherapy in this patient population. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01204684.
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Affiliation(s)
- Richard G. Everson
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Richard Everson and Willy Hugo contributed equally to this work as first authors
| | - Willy Hugo
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Department of Medicine, Division of Dermatology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Parker Institute for Cancer Immunotherapy, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Richard Everson and Willy Hugo contributed equally to this work as first authors
| | - Lu Sun
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Joseph Antonios
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Alexander Lee
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Department of Molecular and Medical Pharmacology , David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Lizhong Ding
- Department of Medicine, Division of Dermatology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Melissa Bu
- Department of Medicine, Division of Dermatology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Sarah Khattab
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Carolina Chavez
- Department of Molecular and Medical Pharmacology , David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Emma Billingslea-Yoon
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | | | - Benjamin M. Ellingson
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Timothy F. Cloughesy
- Department of Molecular and Medical Pharmacology , David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Department of Neurology/Neuro-Oncology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Linda M. Liau
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
| | - Robert M. Prins
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Parker Institute for Cancer Immunotherapy, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
- Department of Molecular and Medical Pharmacology , David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, 90095, U.S.A
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Chang Q, Yan Z, Zhou M, Qu H, He X, Zhang H, Baskaran L, Al'Aref S, Li H, Zhang S, Metaxas DN. Mining multi-center heterogeneous medical data with distributed synthetic learning. Nat Commun 2023; 14:5510. [PMID: 37679325 PMCID: PMC10484909 DOI: 10.1038/s41467-023-40687-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 08/03/2023] [Indexed: 09/09/2023] Open
Abstract
Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.
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Affiliation(s)
- Qi Chang
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA
| | | | - Mu Zhou
- SenseBrain Research, Princeton, NJ, USA
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Hui Qu
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA
| | - Xiaoxiao He
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA
| | - Han Zhang
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA
| | - Lohendran Baskaran
- Department of Cardiovascular Medicine, National Heart Centre Singapore, and Duke-National University Of Singapore, Singapore, Singapore
| | - Subhi Al'Aref
- Department of Medicine, Division of Cardiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Hongsheng Li
- Chinese University of Hong Kong, Hong Kong SAR, China.
- Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong SAR, China.
| | - Shaoting Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China.
- Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong SAR, China.
- SenseTime, Shanghai, China.
| | - Dimitris N Metaxas
- Department of Computer Science, Rutgers University, Piscataway, NJ, USA.
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Kim JE, Park JE, Park SY, Kim YH, Hong CK, Kim JH, Kim HS. Defining subventricular zone involvement to predict the survival of patients in isocitrate dehydrogenase-wild type glioblastoma: validation in a prospective registry. Eur Radiol 2023; 33:6448-6458. [PMID: 37060448 DOI: 10.1007/s00330-023-09625-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 02/11/2023] [Accepted: 02/24/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVES The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma. METHODS This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram. RESULTS SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94-0.99; p = .011) and definition 2 (HR: 0.96, 0.94-0.98, p < .001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94-0.99, p = .008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3 years (AUC: 0.735-0.738 vs. 0.687-0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%). CONCLUSION SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility. KEY POINTS • Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. • Longer SVD was independently associated with longer overall survival. • Adding SVD improved time-dependent specificity for survival model in a prospective registry.
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Affiliation(s)
- Ji Eun Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-gu, Seoul, 05505, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-gu, Seoul, 05505, Korea.
| | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Korea
| | - Young-Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Chang-Ki Hong
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-gu, Seoul, 05505, Korea
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Jabehdar Maralani P, Stewart J, Hiremath S, Lawrence L, Chan R, Lau A, Chen H, Chan A, Zeng LK, Tseng CL, Myrehaug S, Soliman H, Detsky J, Heyn C, Lim Fat M, Lipsman N, Sahgal A. Relationship between apparent diffusion coefficient and survival as a function of distance from gross tumor volume on radiation planning MRI in newly diagnosed glioblastoma. J Neurooncol 2023; 164:597-605. [PMID: 37707752 DOI: 10.1007/s11060-023-04440-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/26/2023] [Indexed: 09/15/2023]
Abstract
PURPOSE To investigate the changes in apparent diffusion coefficient (ADC) within incrementally-increased margins beyond the gross tumor volume (GTV) on post-operative radiation planning MRI and their prognostic utility in glioblastoma. METHODS Radiation planning MRIs of adult patients with newly diagnosed glioblastoma from 2017 to 2020 were assessed. The ADC values were normalized to contralateral normal white matter (nADC). Using 1 mm isotropic incremental margin increases from the GTV, the nADC values were calculated at each increment. Age, ECOG performance status, extent of resection and MGMT promoter methylation status were obtained from medical records. Using univariate and multivariable Cox regression analysis, association of nADC to progression-free and overall survival (PFS, OS) was assessed at each increment. RESULTS Seventy consecutive patients with mean age of 53.6 ± 10.3 years, were evaluated. The MGMT promoter was methylated in 31 (44.3%), unmethylated in 36 (51.6%) and unknown in 3 (4.3%) patients. 11 (16%) underwent biopsy, 41 (44%) subtotal resection and 18 (26%) gross total resection. For each 1 mm increase in distance from GTV, the nADC decreased by 0.16% (p < 0.0001). At 1-5 mm increment, the nADC was associated with OS (p < 0.01). From 6 to 11 mm increment the nADC was associated with OS with the p-value gradually increasing from 0.018 to 0.046. nADC was not associated with PFS. CONCLUSION The nADC values at 1-11 mm increments from the GTV margin were associated with OS. Future prospective multicenter studies are needed to validate the findings and to pave the way for the utilization of ADC for margin reduction in radiation planning.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Shivaprakash Hiremath
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Liam Lawrence
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Rachel Chan
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Angus Lau
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Aimee Chan
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Liang K Zeng
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Chinthaka Heyn
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - MaryJane Lim Fat
- Division of Neurology, Department of Medicine, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Nir Lipsman
- Division of Neurosurgery, Department of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
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Ocaña-Tienda B, Pérez-Beteta J, Jiménez-Sánchez J, Molina-García D, Ortiz de Mendivil A, Asenjo B, Albillo D, Pérez-Romasanta LA, Valiente M, Zhu L, García-Gómez P, González-Del Portillo E, Llorente M, Carballo N, Arana E, Pérez-García VM. Growth exponents reflect evolutionary processes and treatment response in brain metastases. NPJ Syst Biol Appl 2023; 9:35. [PMID: 37479705 PMCID: PMC10361973 DOI: 10.1038/s41540-023-00298-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023] Open
Abstract
Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.
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Affiliation(s)
| | | | | | | | | | - Beatriz Asenjo
- Hospital Regional Universitario de Málaga, Málaga, Spain
| | | | | | - Manuel Valiente
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Lucía Zhu
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Pedro García-Gómez
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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Solar P, Valekova H, Marcon P, Mikulka J, Barak M, Hendrych M, Stransky M, Siruckova K, Kostial M, Holikova K, Brychta J, Jancalek R. Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics. Sci Rep 2023; 13:11459. [PMID: 37454179 PMCID: PMC10349862 DOI: 10.1038/s41598-023-38542-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs' compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.
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Affiliation(s)
- Peter Solar
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Hana Valekova
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petr Marcon
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Jan Mikulka
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Martin Barak
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Michal Hendrych
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- First Department of Pathology, St. Anne's University Hospital, Brno, Czech Republic
| | - Matyas Stransky
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Katerina Siruckova
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Martin Kostial
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka, 12, 616 00, Brno, Czech Republic
| | - Klara Holikova
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Medical Imaging, St. Anne's University Hospital, Brno, Czech Republic
| | - Jindrich Brychta
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic
| | - Radim Jancalek
- Department of Neurosurgery, St. Anne's University Hospital, Pekarska 53, 656 91, Brno, Czech Republic.
- Faculty of Medicine, Masaryk University, Brno, Czech Republic.
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Ferreri AJM, Calimeri T, Cwynarski K, Dietrich J, Grommes C, Hoang-Xuan K, Hu LS, Illerhaus G, Nayak L, Ponzoni M, Batchelor TT. Primary central nervous system lymphoma. Nat Rev Dis Primers 2023; 9:29. [PMID: 37322012 PMCID: PMC10637780 DOI: 10.1038/s41572-023-00439-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2023] [Indexed: 06/17/2023]
Abstract
Primary central nervous system lymphoma (PCNSL) is a diffuse large B cell lymphoma in which the brain, spinal cord, leptomeninges and/or eyes are exclusive sites of disease. Pathophysiology is incompletely understood, although a central role seems to comprise immunoglobulins binding to self-proteins expressed in the central nervous system (CNS) and alterations of genes involved in B cell receptor, Toll-like receptor and NF-κB signalling. Other factors such as T cells, macrophages or microglia, endothelial cells, chemokines, and interleukins, probably also have important roles. Clinical presentation varies depending on the involved regions of the CNS. Standard of care includes methotrexate-based polychemotherapy followed by age-tailored thiotepa-based conditioned autologous stem cell transplantation and, in patients unsuitable for such treatment, consolidation with whole-brain radiotherapy or single-drug maintenance. Personalized treatment, primary radiotherapy and only supportive care should be considered in unfit, frail patients. Despite available treatments, 15-25% of patients do not respond to chemotherapy and 25-50% relapse after initial response. Relapse rates are higher in older patients, although the prognosis of patients experiencing relapse is poor independent of age. Further research is needed to identify diagnostic biomarkers, treatments with higher efficacy and less neurotoxicity, strategies to improve the penetration of drugs into the CNS, and roles of other therapies such as immunotherapies and adoptive cell therapies.
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Affiliation(s)
| | - Teresa Calimeri
- Lymphoma Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Kate Cwynarski
- Department of Haematology, University College Hospital, London, UK
| | - Jorg Dietrich
- Cancer and Neurotoxicity Clinic and Brain Repair Research Program, Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Christian Grommes
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Khê Hoang-Xuan
- APHP, Groupe Hospitalier Salpêtrière, Sorbonne Université, IHU, ICM, Service de Neurologie 2, Paris, France
| | - Leland S Hu
- Department of Radiology, Neuroradiology Division, Mayo Clinic, Phoenix, AZ, USA
| | - Gerald Illerhaus
- Clinic of Hematology, Oncology and Palliative Care, Klinikum Stuttgart, Stuttgart, Germany
| | - Lakshmi Nayak
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Maurilio Ponzoni
- Pathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Ateneo Vita-Salute San Raffaele, Milan, Italy
| | - Tracy T Batchelor
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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39
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Franco D, Granata V, Fusco R, Grassi R, Nardone V, Lombardi L, Cappabianca S, Conforti R, Briganti F, Grassi R, Caranci F. Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01655-0. [PMID: 37289266 DOI: 10.1007/s11547-023-01655-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). METHODS GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. RESULTS A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. CONCLUSIONS AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.
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Affiliation(s)
- Donatella Franco
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Research & Development and Medical Oncology Division, Igea SpA, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Valerio Nardone
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Laura Lombardi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Salvatore Cappabianca
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renata Conforti
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Francesco Briganti
- Advanced Biomedical Sciences Department, Federico II University, Naples, Italy
| | - Roberto Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Ferdinando Caranci
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
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Patel RV, Yao S, Huang RY, Bi WL. Application of radiomics to meningiomas: A systematic review. Neuro Oncol 2023; 25:1166-1176. [PMID: 36723606 PMCID: PMC10237421 DOI: 10.1093/neuonc/noad028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management. METHODS We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS). RESULTS A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range -8 to 36). CONCLUSIONS There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials.
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Affiliation(s)
- Ruchit V Patel
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Raymond Y Huang
- Division of Neuroradiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
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41
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Ellingson BM, Wen PY, Chang SM, van den Bent M, Vogelbaum MA, Li G, Li S, Kim J, Youssef G, Wick W, Lassman AB, Gilbert MR, de Groot JF, Weller M, Galanis E, Cloughesy TF. Objective response rate targets for recurrent glioblastoma clinical trials based on the historic association between objective response rate and median overall survival. Neuro Oncol 2023; 25:1017-1028. [PMID: 36617262 PMCID: PMC10237425 DOI: 10.1093/neuonc/noad002] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Indexed: 01/09/2023] Open
Abstract
Durable objective response rate (ORR) remains a meaningful endpoint in recurrent cancer; however, the target ORR for single-arm recurrent glioblastoma trials has not been based on historic information or tied to patient outcomes. The current study reviewed 68 treatment arms comprising 4793 patients in past trials in recurrent glioblastoma in order to judiciously define target ORRs for use in recurrent glioblastoma trials. ORR was estimated at 6.1% [95% CI 4.23; 8.76%] for cytotoxic chemothera + pies (ORR = 7.59% for lomustine, 7.57% for temozolomide, 0.64% for irinotecan, and 5.32% for other agents), 3.37% for biologic agents, 7.97% for (select) immunotherapies, and 26.8% for anti-angiogenic agents. ORRs were significantly correlated with median overall survival (mOS) across chemotherapy (R2= 0.4078, P < .0001), biologics (R2= 0.4003, P = .0003), and immunotherapy trials (R2= 0.8994, P < .0001), but not anti-angiogenic agents (R2= 0, P = .8937). Pooling data from chemotherapy, biologics, and immunotherapy trials, a meta-analysis indicated a strong correlation between ORR and mOS (R2= 0.3900, P < .0001; mOS [weeks] = 1.4xORR + 24.8). Assuming an ineffective cytotoxic (control) therapy has ORR = 7.6%, the average ORR for lomustine and temozolomide trials, a sample size of ≥40 patients with target ORR>25% is needed to demonstrate statistical significance compared to control with a high level of confidence (P < .01) and adequate power (>80%). Given this historic data and potential biases in patient selection, we recommend that well-controlled, single-arm phase II studies in recurrent glioblastoma should have a target ORR >25% (which translates to a median OS of approximately 15 months) and a sample size of ≥40 patients, in order to convincingly demonstrate antitumor activity. Crucially, this response needs to have sufficient durability, which was not addressed in the current study.
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Affiliation(s)
- Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Los Angeles, California, USA
- UCLA Neuro-Oncology Program, Los Angeles, California, USA
- Department of Radiological Sciences, Los Angeles, California, USA
- Department of Psychiatry and Biobehavioral Sciences, Los Angeles, California, USA
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Susan M Chang
- Division of Neuro-Oncology, University of California San Francisco, San Francisco, California, USA
| | - Martin van den Bent
- Brain Tumor Center at Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Gang Li
- Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Shanpeng Li
- Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Jiyoon Kim
- Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Gilbert Youssef
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Wick
- Neurology Clinic, University of Heidelberg and Clinical Cooperation Unit Neuro-oncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andrew B Lassman
- Division of Neuro-Oncology, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, Herbert Irving Comprehensive Cancer Center, New York-Presbyterian Hospital, New York, New York, USA
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA
| | - John F de Groot
- Division of Neuro-Oncology, University of California San Francisco, San Francisco, California, USA
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Evanthia Galanis
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Los Angeles, California, USA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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Cho NS, Sanvito F, Thakuria S, Wang C, Hagiwara A, Nagaraj R, Oshima S, Lopez Kolkovsky AL, Lu J, Raymond C, Liau LM, Everson RG, Patel KS, Kim W, Yang I, Bergsneider M, Nghiemphu PL, Lai A, Nathanson DA, Cloughesy TF, Ellingson BM. Multi-nuclear sodium, diffusion, and perfusion MRI in human gliomas. J Neurooncol 2023; 163:417-427. [PMID: 37294422 PMCID: PMC10322966 DOI: 10.1007/s11060-023-04363-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 06/02/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE There is limited knowledge about the associations between sodium and proton MRI measurements in brain tumors. The purpose of this study was to quantify intra- and intertumoral correlations between sodium, diffusion, and perfusion MRI in human gliomas. METHODS Twenty glioma patients were prospectively studied on a 3T MRI system with multinuclear capabilities. Three mutually exclusive tumor volumes of interest (VOIs) were segmented: contrast-enhancing tumor (CET), T2/FLAIR hyperintense non-enhancing tumor (NET), and necrosis. Median and voxel-wise associations between apparent diffusion coefficient (ADC), normalized relative cerebral blood volume (nrCBV), and normalized sodium measurements were quantified for each VOI. RESULTS Both relative sodium concentration and ADC were significantly higher in areas of necrosis compared to NET (P = 0.003 and P = 0.008, respectively) and CET (P = 0.02 and P = 0.02). Sodium concentration was higher in CET compared to NET (P = 0.04). Sodium and ADC were higher in treated compared to treatment-naïve gliomas within NET (P = 0.006 and P = 0.01, respectively), and ADC was elevated in CET (P = 0.03). Median ADC and sodium concentration were positively correlated across patients in NET (r = 0.77, P < 0.0001) and CET (r = 0.84, P < 0.0001), but not in areas of necrosis (r = 0.45, P = 0.12). Median nrCBV and sodium concentration were negatively correlated across patients in areas of NET (r=-0.63, P = 0.003). Similar associations were observed when examining voxel-wise correlations within VOIs. CONCLUSION Sodium MRI is positively correlated with proton diffusion MRI measurements in gliomas, likely reflecting extracellular water. Unique areas of multinuclear MRI contrast may be useful in future studies to understand the chemistry of the tumor microenvironment.
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Affiliation(s)
- Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shruti Thakuria
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Raksha Nagaraj
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sonoko Oshima
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alfredo L Lopez Kolkovsky
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
| | - Jianwen Lu
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Won Kim
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Isaac Yang
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marvin Bergsneider
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL) Professor of Radiology, Psychiatry, and Neurosurgery Departments of Radiological Sciences, Psychiatry, and Neurosurgery David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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Jaju A, Li Y, Dahmoush H, Gottardo NG, Laughlin S, Mirsky D, Panigrahy A, Sabin ND, Shaw D, Storm PB, Poussaint TY, Patay Z, Bhatia A. Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper. Pediatr Blood Cancer 2023; 70 Suppl 4:e30147. [PMID: 36519599 PMCID: PMC10466217 DOI: 10.1002/pbc.30147] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 12/16/2022]
Abstract
Tumors of the central nervous system are the most common solid malignancies in children and the most common cause of pediatric cancer-related mortality. Imaging plays a central role in diagnosis, staging, treatment planning, and response assessment of pediatric brain tumors. However, the substantial variability in brain tumor imaging protocols across institutions leads to variability in patient risk stratification and treatment decisions, and complicates comparisons of clinical trial results. This White Paper provides consensus-based imaging recommendations for evaluating pediatric patients with primary brain tumors. The proposed brain magnetic resonance imaging protocol recommendations balance advancements in imaging techniques with the practicality of deployment across most imaging centers.
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Affiliation(s)
- Alok Jaju
- Department of Medical Imaging, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Yi Li
- UCSF Department of Radiology and Biomedical Imaging, San Francisco, California, USA
| | - Hisham Dahmoush
- Department of Radiology, Lucile Packard Children's Hospital at Stanford, Palo Alto, California, USA
| | - Nicholas G Gottardo
- Department of Paediatric and Adolescent Oncology and Haematology, Perth Children's Hospital, Brain Tumour Research Programme, Telethon Kids Institute, Perth, Western Australia, Australia
| | - Suzanne Laughlin
- Department of Diagnostic Imaging, The Hospital for Sick Children and Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - David Mirsky
- Department of Radiology, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Ashok Panigrahy
- Department of Radiology, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Noah D Sabin
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Dennis Shaw
- Department of Radiology, Seattle Children's Hospital, Seattle, Washington, USA
| | - Phillip B Storm
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Tina Young Poussaint
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Zoltan Patay
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Aashim Bhatia
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Wollring MM, Werner JM, Bauer EK, Tscherpel C, Ceccon GS, Lohmann P, Stoffels G, Kabbasch C, Goldbrunner R, Fink GR, Langen KJ, Galldiks N. Prediction of response to lomustine-based chemotherapy in glioma patients at recurrence using MRI and FET PET. Neuro Oncol 2023; 25:984-994. [PMID: 36215231 PMCID: PMC10158105 DOI: 10.1093/neuonc/noac229] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND We evaluated O-(2-[18F]fluoroethyl)-l-tyrosine (FET) PET and MRI for early response assessment in recurrent glioma patients treated with lomustine-based chemotherapy. METHODS Thirty-six adult patients with WHO CNS grade 3 or 4 gliomas (glioblastoma, 69%) at recurrence (median number of recurrences, 1; range, 1-3) were retrospectively identified. Besides MRI, serial FET PET scans were performed at baseline and early after chemotherapy initiation (not later than two cycles). Tumor-to-brain ratios (TBR), metabolic tumor volumes (MTV), the occurrence of new distant hotspots with a mean TBR >1.6 at follow-up, and the dynamic parameter time-to-peak were derived from all FET PET scans. PET parameter thresholds were defined using ROC analyses to predict PFS of ≥6 months and OS of ≥12 months. MRI response assessment was based on RANO criteria. The predictive values of FET PET parameters and RANO criteria were subsequently evaluated using univariate and multivariate survival estimates. RESULTS After treatment initiation, the median follow-up time was 11 months (range, 3-71 months). Relative changes of TBR, MTV, and RANO criteria predicted a significantly longer PFS (all P ≤ .002) and OS (all P ≤ .045). At follow-up, the occurrence of new distant hotspots (n ≥ 1) predicted a worse outcome, with significantly shorter PFS (P = .005) and OS (P < .001). Time-to-peak changes did not predict a significantly longer survival. Multivariate survival analyses revealed that new distant hotspots at follow-up FET PET were most potent in predicting non-response (P < .001; HR, 8.578). CONCLUSIONS Data suggest that FET PET provides complementary information to RANO criteria for response evaluation of lomustine-based chemotherapy early after treatment initiation.
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Affiliation(s)
- Michael M Wollring
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Jan-Michael Werner
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Elena K Bauer
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Caroline Tscherpel
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Garry S Ceccon
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
- Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Christoph Kabbasch
- Institute of Radiology, Division of Neuroradiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Roland Goldbrunner
- Department of General Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
- Department of Nuclear Medicine, RWTH Aachen University Hospital, Aachen, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
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Goncalves Filho ALM, Awan KM, Conklin J, Ngamsombat C, Cauley SF, Setsompop K, Liu W, Splitthoff DN, Lo WC, Kirsch JE, Schaefer PW, Rapalino O, Huang SY. Validation of a highly accelerated post-contrast wave-controlled aliasing in parallel imaging (CAIPI) 3D-T1 MPRAGE compared to standard 3D-T1 MPRAGE for detection of intracranial enhancing lesions on 3-T MRI. Eur Radiol 2023; 33:2905-2915. [PMID: 36460923 PMCID: PMC9718459 DOI: 10.1007/s00330-022-09265-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVES High-resolution post-contrast T1-weighted imaging is a workhorse sequence in the evaluation of neurological disorders. The T1-MPRAGE sequence has been widely adopted for the visualization of enhancing pathology in the brain. However, this three-dimensional (3D) acquisition is lengthy and prone to motion artifact, which often compromises diagnostic quality. The goal of this study was to compare a highly accelerated wave-controlled aliasing in parallel imaging (CAIPI) post-contrast 3D T1-MPRAGE sequence (Wave-T1-MPRAGE) with the standard 3D T1-MPRAGE sequence for visualizing enhancing lesions in brain imaging at 3 T. METHODS This study included 80 patients undergoing contrast-enhanced brain MRI. The participants were scanned with a standard post-contrast T1-MPRAGE sequence (acceleration factor [R] = 2 using GRAPPA parallel imaging technique, acquisition time [TA] = 5 min 18 s) and a prototype post-contrast Wave-T1-MPRAGE sequence (R = 4, TA = 2 min 32 s). Two neuroradiologists performed a head-to-head evaluation of both sequences and rated the visualization of enhancement, sharpness, noise, motion artifacts, and overall diagnostic quality. A 15% noninferiority margin was used to test whether post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE. Inter-rater and intra-rater agreement were calculated. Quantitative assessment of CNR/SNR was performed. RESULTS Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE for delineating enhancing lesions with unanimous agreement in all cases between raters. Wave-T1-MPRAGE was noninferior in the perception of noise (p < 0.001), motion artifact (p < 0.001), and overall diagnostic quality (p < 0.001). CONCLUSION High-accelerated post-contrast Wave-T1-MPRAGE enabled a two-fold reduction in acquisition time compared to the standard sequence with comparable performance for visualization of enhancing pathology and equivalent perception of noise, motion artifacts and overall diagnostic quality without loss of clinically important information. KEY POINTS • Post-contrast wave-controlled aliasing in parallel imaging (CAIPI) T1-MPRAGE accelerated the acquisition of three-dimensional (3D) high-resolution post-contrast images by more than two-fold. • Post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE with unanimous agreement between reviewers (100% in 80 cases) for the visualization of intracranial enhancing lesions. • Wave-T1-MPRAGE was equivalent to the standard sequence in the perception of noise in 94% (75 of 80) of cases and was preferred in 16% (13 of 80) of cases for decreased motion artifact.
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Affiliation(s)
- Augusto Lio M Goncalves Filho
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Komal Manzoor Awan
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Nakhon Pathom, Thailand
| | - Stephen F Cauley
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Kawin Setsompop
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | | | - John E Kirsch
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Pamela W Schaefer
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, 55 Fruit St, GRB-273A, Boston, MA, 02114, USA.
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Chiu FY, Yen Y. Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 2023; 11:35. [PMID: 36991494 DOI: 10.1186/s40364-023-00476-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/16/2023] [Indexed: 03/31/2023] Open
Abstract
Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.
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Affiliation(s)
- Fang-Ying Chiu
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Center for Brain and Neurobiology Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Teaching and Research Headquarters for Sustainable Development Goals, Tzu Chi University, Hualien City, 970374, Taiwan.
| | - Yun Yen
- Center for Cancer Translational Research, Tzu Chi University, Hualien City, 970374, Taiwan.
- Ph.D. Program for Cancer Biology and Drug Discovery, Taipei Medical University, Taipei City, 110301, Taiwan.
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei City, 110301, Taiwan.
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei City, 110301, Taiwan.
- Cancer Center, Taipei Municipal WanFang Hospital, Taipei City, 116081, Taiwan.
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Wamelink IJHG, Hempel HL, van de Giessen E, Vries MHM, De Witt Hamer P, Barkhof F, Keil VC. The patients' experience of neuroimaging of primary brain tumors: a cross-sectional survey study. J Neurooncol 2023; 162:307-315. [PMID: 36977844 PMCID: PMC10167184 DOI: 10.1007/s11060-023-04290-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 03/04/2023] [Indexed: 03/30/2023]
Abstract
PURPOSE To gain insight into how patients with primary brain tumors experience MRI, follow-up protocols, and gadolinium-based contrast agent (GBCA) use. METHODS Primary brain tumor patients answered a survey after their MRI exam. Questions were analyzed to determine trends in patients' experience regarding the scan itself, follow-up frequency, and the use of GBCAs. Subgroup analysis was performed on sex, lesion grade, age, and the number of scans. Subgroup comparison was made using the Pearson chi-square test and the Mann-Whitney U-test for categorical and ordinal questions, respectively. RESULTS Of the 100 patients, 93 had a histopathologically confirmed diagnosis, and seven were considered to have a slow-growing low-grade tumor after multidisciplinary assessment and follow-up. 61/100 patients were male, with a mean age ± standard deviation of 44 ± 14 years and 46 ± 13 years for the females. Fifty-nine patients had low-grade tumors. Patients consistently underestimated the number of their previous scans. 92% of primary brain tumor patients did not experience the MRI as bothering and 78% would not change the number of follow-up MRIs. 63% of the patients would prefer GBCA-free MRI scans if diagnostically equally accurate. Women found the MRI and receiving intravenous cannulas significantly more uncomfortable than men (p = 0.003). Age, diagnosis, and the number of previous scans had no relevant impact on the patient experience. CONCLUSION Patients with primary brain tumors experienced current neuro-oncological MRI practice as positive. Especially women would, however, prefer GBCA-free imaging if diagnostically equally accurate. Patient knowledge of GBCAs was limited, indicating improvable patient information.
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Affiliation(s)
- Ivar J H G Wamelink
- Radiology & Nuclear Medicine Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands.
| | - Hugo L Hempel
- Radiology & Nuclear Medicine Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Elsmarieke van de Giessen
- Radiology & Nuclear Medicine Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Mark H M Vries
- Radiology & Nuclear Medicine Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Philip De Witt Hamer
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Radiology & Nuclear Medicine Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Vera C Keil
- Radiology & Nuclear Medicine Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, De Boelelaan 1117, Amsterdam, The Netherlands
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Ius T, Sabatino G, Panciani PP, Fontanella MM, Rudà R, Castellano A, Barbagallo GMV, Belotti F, Boccaletti R, Catapano G, Costantino G, Della Puppa A, Di Meco F, Gagliardi F, Garbossa D, Germanò AF, Iacoangeli M, Mortini P, Olivi A, Pessina F, Pignotti F, Pinna G, Raco A, Sala F, Signorelli F, Sarubbo S, Skrap M, Spena G, Somma T, Sturiale C, Angileri FF, Esposito V. Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review. J Neurooncol 2023; 162:267-293. [PMID: 36961622 PMCID: PMC10167129 DOI: 10.1007/s11060-023-04274-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/20/2023] [Indexed: 03/25/2023]
Abstract
PURPOSE The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal. METHODS A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation of EOR, surgery at tumor progression and surgery in elderly patients. RESULTS A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B). CONCLUSIONS A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity.
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Affiliation(s)
- Tamara Ius
- Division of Neurosurgery, Head-Neck and NeuroScience Department, University Hospital of Udine, Udine, Italy
| | - Giovanni Sabatino
- Institute of Neurosurgery, Fondazione Policlinico Gemelli, Catholic University, Rome, Italy
- Unit of Neurosurgery, Mater Olbia Hospital, Olbia, Italy
| | - Pier Paolo Panciani
- Division of Neurosurgery, Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
| | - Marco Maria Fontanella
- Department of Neuro-Oncology, University of Turin and City of Health and Science Hospital, 10094, Torino, Italy
| | - Roberta Rudà
- Department of Neuro-Oncology, University of Turin and City of Health and Science Hospital, 10094, Torino, Italy
- Neurology Unit, Hospital of Castelfranco Veneto, 31033, Castelfranco Veneto, Italy
| | - Antonella Castellano
- Department of Neuroradiology, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | - Giuseppe Maria Vincenzo Barbagallo
- Department of Medical and Surgical Sciences and Advanced Technologies (G.F. Ingrassia), Neurological Surgery, Policlinico "G. Rodolico - San Marco" University Hospital, University of Catania, Catania, Italy
- Interdisciplinary Research Center On Brain Tumors Diagnosis and Treatment, University of Catania, Catania, Italy
| | - Francesco Belotti
- Division of Neurosurgery, Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | | | - Giuseppe Catapano
- Division of Neurosurgery, Department of Neurological Sciences, Ospedale del Mare, Naples, Italy
| | | | - Alessandro Della Puppa
- Neurosurgical Clinical Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi Hospital, University of Florence, Florence, Italy
| | - Francesco Di Meco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Johns Hopkins Medical School, Baltimore, MD, USA
| | - Filippo Gagliardi
- Department of Neurosurgery and Gamma Knife Radiosurgery, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | - Diego Garbossa
- Department of Neuroscience "Rita Levi Montalcini," Neurosurgery Unit, University of Turin, Torino, Italy
| | | | - Maurizio Iacoangeli
- Department of Neurosurgery, Università Politecnica Delle Marche, Azienda Ospedali Riuniti, Ancona, Italy
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | | | - Federico Pessina
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Milan, Italy
- Neurosurgery Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Milan, Italy
| | - Fabrizio Pignotti
- Institute of Neurosurgery, Fondazione Policlinico Gemelli, Catholic University, Rome, Italy
- Unit of Neurosurgery, Mater Olbia Hospital, Olbia, Italy
| | - Giampietro Pinna
- Unit of Neurosurgery, Department of Neurosciences, Hospital Trust of Verona, 37134, Verona, Italy
| | - Antonino Raco
- Division of Neurosurgery, Department of NESMOS, AOU Sant'Andrea, Sapienza University, Rome, Italy
| | - Francesco Sala
- Department of Neurosciences, Biomedicines and Movement Sciences, Institute of Neurosurgery, University of Verona, 37134, Verona, Italy
| | - Francesco Signorelli
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, Neurosurgery Unit, University "Aldo Moro", 70124, Bari, Italy
| | - Silvio Sarubbo
- Department of Neurosurgery, Santa Chiara Hospital, Azienda Provinciale Per I Servizi Sanitari (APSS), Trento, Italy
| | - Miran Skrap
- Division of Neurosurgery, Head-Neck and NeuroScience Department, University Hospital of Udine, Udine, Italy
| | | | - Teresa Somma
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Università Degli Studi Di Napoli Federico II, Naples, Italy
| | | | | | - Vincenzo Esposito
- Department of Neurosurgery "Giampaolo Cantore"-IRCSS Neuromed, Pozzilli, Italy
- Department of Human, Neurosciences-"Sapienza" University of Rome, Rome, Italy
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Cho NS, Hagiwara A, Sanvito F, Ellingson BM. A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors. Neuroradiology 2023; 65:559-568. [PMID: 36301349 PMCID: PMC9905164 DOI: 10.1007/s00234-022-03072-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods. METHODS Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated. RESULTS Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar. CONCLUSION The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.
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Affiliation(s)
- Nicholas S Cho
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Francesco Sanvito
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Benjamin M Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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50
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Outcomes of intraventricular 131-I-omburtamab and external beam radiotherapy in patients with recurrent medulloblastoma and ependymoma. J Neurooncol 2023; 162:69-78. [PMID: 36853490 PMCID: PMC10050019 DOI: 10.1007/s11060-022-04235-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/30/2022] [Indexed: 03/01/2023]
Abstract
PURPOSE Intraventricular compartmental radioimmunotherapy (cRIT) with 131-I-omburtamab is a potential therapy for recurrent primary brain tumors that can seed the thecal space. These patients often previously received external beam radiotherapy (EBRT) to a portion or full craniospinal axis (CSI) as part of upfront therapy. Little is known regarding outcomes after re-irradiation as part of multimodality therapy including cRIT. This study evaluates predictors of response, patterns of failure, and radiologic events after cRIT. METHODS Patients with recurrent medulloblastoma or ependymoma who received 131-I-omburtamab on a prospective clinical trial were included. Extent of disease at cRIT initiation (no evidence of disease [NED] vs measurable disease [MD]) was assessed as associated with progression-free (PFS) and overall survival (OS) by Kaplan-Meier analysis. RESULTS All 27 patients (20 medulloblastoma, 7 ependymoma) had EBRT preceding cRIT: most (22, 81%) included CSI (median dose 2340 cGy, boost to 5400 cGy). Twelve (44%) also received EBRT at relapse as bridging to cRIT. There were no cases of radionecrosis. At cRIT initiation, 11 (55%) medulloblastoma and 3 (43%) ependymoma patients were NED, associated with improved PFS (p = 0.002) and OS (p = 0.048) in medulloblastoma. Most relapses were multifocal. With medium follow-up of 3.0 years (95% confidence interval, 1.8-7.4), 6 patients remain alive with NED. CONCLUSION For patients with medulloblastoma, remission at time of cRIT was associated with significantly improved survival outcomes. Relapses are often multifocal, particularly in the setting of measurable disease at cRIT initiation. EBRT is a promising tool to achieve NED status at cRIT initiation, with no cases of radiation necrosis.
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