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Li K, Zhu Q, Yang J, Zheng Y, Du S, Song M, Peng Q, Yang R, Liu Y, Qi L. Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges. Acad Radiol 2024; 31:3366-3383. [PMID: 38614827 DOI: 10.1016/j.acra.2024.03.019] [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: 12/10/2023] [Revised: 01/14/2024] [Accepted: 03/18/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP. MATERIALS AND METHODS This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics. RESULTS Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation. CONCLUSION Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.
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Affiliation(s)
- Kaishu Li
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China; Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China.; Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Qihui Zhu
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Junyi Yang
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Yin Zheng
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Siyuan Du
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Meihui Song
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Qian Peng
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Runwei Yang
- Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China
| | - Yawei Liu
- Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China
| | - Ling Qi
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China.
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Ziegenfeuter J, Delbridge C, Bernhardt D, Gempt J, Schmidt-Graf F, Hedderich D, Griessmair M, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Metz MC, Wiestler B. Resolving spatial response heterogeneity in glioblastoma. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06782-y. [PMID: 38837060 DOI: 10.1007/s00259-024-06782-y] [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: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. METHODS Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. RESULTS Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. CONCLUSION Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.
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Affiliation(s)
- Julian Ziegenfeuter
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany.
| | - Claire Delbridge
- Department of Pathology, Technical University of Munich, 81675, München, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Jens Gempt
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Friederike Schmidt-Graf
- Department of Neurology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Dennis Hedderich
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Michael Griessmair
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie Thomas
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Hanno S Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- TranslaTUM, Technical University of Munich, 81675, München, Germany
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3
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Ungan G, Pons-Escoda A, Ulinic D, Arús C, Ortega-Martorell S, Olier I, Vellido A, Majós C, Julià-Sapé M. Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous. NMR IN BIOMEDICINE 2024; 37:e5095. [PMID: 38213096 DOI: 10.1002/nbm.5095] [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: 06/15/2023] [Revised: 11/06/2023] [Accepted: 12/01/2023] [Indexed: 01/13/2024]
Abstract
The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow-up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast-enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow-up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast-enhancing regions with a blind-source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns-proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast-enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast-enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers.
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Affiliation(s)
- Gülnur Ungan
- Centro de Investigación Biomédica en Red (CIBER), Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Albert Pons-Escoda
- Grup de Neuro-oncologia, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Daniel Ulinic
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red (CIBER), Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | | | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University (LJMU), Liverpool, UK
| | - Alfredo Vellido
- Centro de Investigación Biomédica en Red (CIBER), Madrid, Spain
- IDEAI-UPC Research Center, UPC BarcelonaTech, Barcelona, Spain
| | - Carles Majós
- Centro de Investigación Biomédica en Red (CIBER), Madrid, Spain
- Grup de Neuro-oncologia, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red (CIBER), Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
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Valerius AR, Webb MJ, Hammad N, Sener U, Malani R. Cerebrospinal Fluid Liquid Biopsies in the Evaluation of Adult Gliomas. Curr Oncol Rep 2024; 26:377-390. [PMID: 38488990 DOI: 10.1007/s11912-024-01517-6] [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] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
PURPOSE OF REVIEW This review aims to discuss recent research regarding the biomolecules explored in liquid biopsies and their potential clinical uses for adult-type diffuse gliomas. RECENT FINDINGS Evaluation of tumor biomolecules via cerebrospinal fluid (CSF) is an emerging technology in neuro-oncology. Studies to date have already identified various circulating tumor DNA, extracellular vesicle, micro-messenger RNA and protein biomarkers of interest. These biomarkers show potential to assist in multiple avenues of central nervous system (CNS) tumor evaluation, including tumor differentiation and diagnosis, treatment selection, response assessment, detection of tumor progression, and prognosis. In addition, CSF liquid biopsies have the potential to better characterize tumor heterogeneity compared to conventional tissue collection and CNS imaging. Current imaging modalities are not sufficient to establish a definitive glioma diagnosis and repeated tissue sampling via conventional biopsy is risky, therefore, there is a great need to improve non-invasive and minimally invasive sampling methods. CSF liquid biopsies represent a promising, minimally invasive adjunct to current approaches which can provide diagnostic and prognostic information as well as aid in response assessment.
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Affiliation(s)
| | - Mason J Webb
- Department of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Nouran Hammad
- Jordan University of Science and Technology School of Medicine, Irbid, Jordan
| | - Ugur Sener
- Department of Neurology, Department of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Rachna Malani
- University of UT - Huntsman Cancer Institute (Department of Neurosurgery), Salt Lake City, UT, USA
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Yamazaki M, Takamatsu S, Iwata Y, Sakurai T, Taka M, Kobayashi S, Gabata T, Mizuno E. Notch appearance as a novel radiological predictor of transient expansion and good outcome of expanding schwannoma after radiotherapy. Discov Oncol 2024; 15:79. [PMID: 38503989 PMCID: PMC10951174 DOI: 10.1007/s12672-024-00936-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
OBJECTIVES Schwannoma expansion after radiotherapy has not been well-studied despite the clinical importance of distinguishing transient increase from permanent expansion. Thus, this study aimed to identify the underlying mechanism and novel radiological predictors of schwannoma expansion after radiotherapy. MATERIALS & METHODS We retrospectively examined the therapeutic effects of radiotherapy on schwannomas and magnetic resonance images of 43 patients with vestibular schwannomas who underwent stereotactic radiotherapy or radiosurgery at our facility between June 1, 2012 and September 1, 2018. Based on the size change pattern, the treated tumors were classified into six groups, including transient-expansion and consistent-increase groups. The apparent diffusion coefficient (ADC) ratio and appearance of any notch were included as evaluation items based on our hypothesis that transient expansion is due to edema with increased extracellular free water. A log-rank test was performed to evaluate the relationship between the local control rate and radiological signs. RESULTS The mean overall 5-year local control rate was 90%, and the median follow-up period was 62 (24-87) months. Approximately 28% of the tumors showed transient expansion; all ADC ratios synchronized with size change, and 75% showed a new notch appearance. Approximately 9% of tumors showed consistent increase, with no notch on the outline. The log-rank test revealed a difference in the local control rate with or without notch appearance in expanding irradiated schwannomas. All tumors with notch appearance showed a significant regression 5 years after radiation. CONCLUSIONS New notch appearance on the outline could indicate favorable long-term outcomes of expanding schwannomas post-treatment. CLINICAL RELEVANCE STATEMENT Notch appearance can help differentiate a transient schwannoma from a real tumor expansion, and it is a novel predictor of better outcomes of expanding schwannomas after radiotherapy.
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Affiliation(s)
- Masahiro Yamazaki
- Department of Radiology, Kanazawa University School of Medical Science, Kanazawa City, Japan.
| | - Shigeyuki Takamatsu
- Department of Radiology, Kanazawa University School of Medical Science, Kanazawa City, Japan
| | - Yuta Iwata
- Toyama CyberKnife Center, Toyama City, Japan
| | - Takayuki Sakurai
- Department of Radiology, Kanazawa University School of Medical Science, Kanazawa City, Japan
| | - Masashi Taka
- Toyama Prefectural Central Hospital, Toyama City, Japan
| | - Satoshi Kobayashi
- Department of Radiology, Kanazawa University School of Medical Science, Kanazawa City, Japan
| | - Toshifumi Gabata
- Department of Radiology, Kanazawa University School of Medical Science, Kanazawa City, Japan
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Kong X, Mao Y, Xi F, Li Y, Luo Y, Ma J. Nomograms Based on MRI Radiomics for Differential Diagnosis and Predicting BRAFV600E Expression in Pleomorphic Xanthoastrocytoma and Ganglioglioma. Acad Radiol 2024; 31:1069-1081. [PMID: 37741731 DOI: 10.1016/j.acra.2023.08.031] [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: 07/23/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/25/2023]
Abstract
RATIONALE AND OBJECTIVES This study was designed to investigate the value of nomograms based on MRI radiomics and clinical semantic features in identifying pleomorphic xanthoastrocytoma (PXA) and ganglioglioma (GG) as well as predicting BRAFV600E expression. MATERIALS AND METHODS This study included 265 patients histologically diagnosed with PXA (n = 113) and GG (n = 152). T1WI, T2WI, and CET1 sequences were utilized to extract radiomics features. Univariate analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator were used for dimensionality reduction and feature selection. Following this, logistic regression was utilized to establish the radiomics model. Univariate and multivariate analyses of clinical semantic features were applied, and clinical models were constructed. The nomograms were established by merging radiomics and clinical features. Furthermore, ROC curve analysis was used for examining the model performance, whereas the decision curve analysis (DCA) examined the clinical utility of the nomograms. RESULTS Nomograms achieved the best predictive efficacy compared to clinical and radiomics models alone. Concerning the differentiation between PXA and GG, the area under the curve (AUC) values of the nomogram were 0.879 (0.828-0.930) and 0.887 (0.805-0.969) for the training and testing cohorts, respectively. For predicting BRAFV600E expression, the AUC values of the nomogram were 0.873 (0.811-0.936) and 0.851 (0.740-0.963) for the training and testing cohorts, respectively. DCA confirmed the clinical utility of the nomograms. CONCLUSION Nomograms based on radiomics and clinical semantic features were noninvasive tools for differential diagnosis of PXA and GG and predicting BRAFV600E expression, which may be helpful for assessing patient prognosis and developing individualized treatment strategies.
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Affiliation(s)
- Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yu Mao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fengjun Xi
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [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: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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Martucci M, Russo R, Giordano C, Schiarelli C, D’Apolito G, Tuzza L, Lisi F, Ferrara G, Schimperna F, Vassalli S, Calandrelli R, Gaudino S. Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay. Cancers (Basel) 2023; 15:3790. [PMID: 37568606 PMCID: PMC10417432 DOI: 10.3390/cancers15153790] [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: 06/15/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment's lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.
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Affiliation(s)
- Matia Martucci
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Rosellina Russo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Carolina Giordano
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Chiara Schiarelli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Gabriella D’Apolito
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Laura Tuzza
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Francesca Lisi
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Giuseppe Ferrara
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Francesco Schimperna
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Stefania Vassalli
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
| | - Rosalinda Calandrelli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
| | - Simona Gaudino
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico “A. Gemelli” IRCCS, 00168 Rome, Italy; (R.R.); (C.G.); (C.S.); (G.D.); (R.C.); (S.G.)
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (L.T.); (F.L.); (G.F.); (F.S.); (S.V.)
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Deng S, Zhu Y. Prediction of Glioma Grade by Tumor Heterogeneity Radiomic Analysis Based on Multiparametric MRI. INT J COMPUT INT SYS 2023. [DOI: 10.1007/s44196-023-00230-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023] Open
Abstract
AbstractPredicting glioma grade plays a pivotal role in treatment and prognosis. However, several current methods for grading depend on the characteristics of the whole tumor. Predicting grade by analyzing tumor subregions has not been thoroughly investigated, which aims to improve the prediction performance. To predict glioma grade via analysis of tumor heterogeneity with features extracted from tumor subregions, it is mainly divided into four magnetic resonance imaging (MRI) sequences, including T2-weighted (T2), fluid-attenuated inversion recovery (FLAIR), pre-gadolinium T1-weighted (T1), and post-gadolinium T1-weighted methods. This study included the data of 97 patients with glioblastomas and 42 patients with low-grade gliomas before surgery. Three subregions, including enhanced tumor (ET), non-enhanced tumor, and peritumoral edema, were obtained based on segmentation labels generated by the GLISTRBoost algorithm. One hundred radiomic features were extracted from each subregion. Feature selection was performed using the cross-validated recursive feature elimination with a support vector machine (SVM) algorithm. SVM classifiers with grid search were established to predict glioma grade based on unparametric and multiparametric MRI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the classifiers, and the performance of the subregions was compared with the results of the whole tumor. In uniparametric analysis, the features from the ET subregion yielded a higher AUC value of 0.8697, 0.8474, and 0.8474 than those of the whole tumor of FLAIR, T1, and T2. In multiparametric analysis, the ET subregion achieved the best performance (AUC = 0.8755), which was higher than the uniparametric results. Radiomic features from the tumor subregion can potentially be used as clinical markers to improve the predictive accuracy of glioma grades.
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Cruz N, Herculano-Carvalho M, Roque D, Faria CC, Cascão R, Ferreira HA, Reis CP, Matela N. Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma. Pharmaceutics 2023; 15:pharmaceutics15030928. [PMID: 36986790 PMCID: PMC10054750 DOI: 10.3390/pharmaceutics15030928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/25/2023] [Accepted: 03/10/2023] [Indexed: 03/15/2023] Open
Abstract
Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease. Standard of care in newly diagnosed glioblastoma is maximal tumour resection followed by initial concomitant radiotherapy and temozolomide (TMZ) and then further chemotherapy with TMZ. Imaging techniques are key not only to diagnose the extent of the affected tissue but also for surgery planning and even for intraoperative use. Eligible patients may combine TMZ with tumour treating fields (TTF) therapy, which delivers low-intensity and intermediate-frequency electric fields to arrest tumour growth. Nonetheless, the blood–brain barrier (BBB) and systemic side effects are obstacles to successful chemotherapy in GBM; thus, more targeted, custom therapies such as immunotherapy and nanotechnological drug delivery systems have been undergoing research with varying degrees of success. This review proposes an overview of the pathophysiology, possible treatments, and the most (not all) representative examples of the latest advancements.
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Affiliation(s)
- Nuno Cruz
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- iMED.ULisboa, Research Institute for Medicines, Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal
| | - Manuel Herculano-Carvalho
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisboa, Portugal
| | - Diogo Roque
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisboa, Portugal
| | - Cláudia C. Faria
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
- Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHULN), 1649-028 Lisboa, Portugal
| | - Rita Cascão
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal
| | - Hugo Alexandre Ferreira
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Catarina Pinto Reis
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- iMED.ULisboa, Research Institute for Medicines, Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal
- Correspondence: (C.P.R.); (N.M.); Tel.: +351-217-946-400 (ext. 14244) (C.P.R.); Fax: +351-217-946-470 (C.P.R.)
| | - Nuno Matela
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Correspondence: (C.P.R.); (N.M.); Tel.: +351-217-946-400 (ext. 14244) (C.P.R.); Fax: +351-217-946-470 (C.P.R.)
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Sidibe I, Tensaouti F, Gilhodes J, Cabarrou B, Filleron T, Desmoulin F, Ken S, Noël G, Truc G, Sunyach MP, Charissoux M, Magné N, Lotterie JA, Roques M, Péran P, Cohen-Jonathan Moyal E, Laprie A. Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis. Radiother Oncol 2023; 181:109486. [PMID: 36706959 DOI: 10.1016/j.radonc.2023.109486] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND PURPOSE To investigate the feasibility of using a multiapproach analysis combining clinical data, diffusion- and perfusion-weighted imaging, and 3D magnetic resonance spectroscopic imaging to distinguish true tumor progression (TP) from pseudoprogression (PSP) in patients with glioblastoma. MATERIALS AND METHODS Progression was suspected within 6 months of radiotherapy in 46 of the 180 patients included in the Phase-III SpectroGlio trial (NCT01507506). Choline/creatine (Cho/Cr), choline/N-acetyl aspartate (Cho/NAA) and lactate/N-acetyl aspartate (Lac/NAA) ratios were extracted. Apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were calculated. ADC, relative CBV values and tumor volume (TV) were collected at relapse. Differences between TP and PSP were evaluated using Mann-Whitney tests, and p values were adjusted with Bonferroni correction. RESULTS Patients with suspected progression underwent a new MRI scan 1 month after the first one. Of these, 28 were classified as PSP, and 18 as TP. After a median follow-up of 41 months, median overall survival was higher in PSP than in TP (25.2 vs 20.3 months; p = 0.0092). Lac/NAA and Cho/Cr ratios were higher in TP than in PSP (1.2 vs 0.5; p = 0.006; and 3 vs 2.2; p = 0.021). After multivariate regression analysis, TV was the most significant predictor of TP vs PSP, and the only one retained in the model (p = 0.028). CONCLUSION Three spectroscopic ratios could be used to differentiate PSP from TP. TV at relapse was the most predictive factor in the multivariate analysis, and overall survival was higher in PSP than in TP.
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Affiliation(s)
- Ingrid Sidibe
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Fatima Tensaouti
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Julia Gilhodes
- Biostatistics Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France
| | - Bastien Cabarrou
- Biostatistics Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France
| | - Thomas Filleron
- Biostatistics Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France
| | - Franck Desmoulin
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Soleakhena Ken
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Radiation Oncology Department, Toulouse Center for Cancer Research & INSERM, Toulouse, France
| | - Georges Noël
- Radiation Oncology Department, ICANS, Strasbourg, France
| | - Gilles Truc
- Radiation Oncology Department, Georges-François Leclerc Center, Dijon, France
| | | | | | - Nicolas Magné
- Radiation Oncology Department, Lucien Neuwirth Loire Cancer Institute, Saint-Priest-en-Jarez, France
| | - Jean-Albert Lotterie
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Margaux Roques
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Patrice Péran
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France
| | - Elizabeth Cohen-Jonathan Moyal
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Radiation Oncology Department, Toulouse Center for Cancer Research & INSERM, Toulouse, France
| | - Anne Laprie
- Radiation Oncology Department, Claudius Regaud Institute/Toulouse University Cancer Institute - Oncopôle, Toulouse, France; Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier & INSERM, Toulouse, France.
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Ramakrishnan D, von Reppert M, Krycia M, Sala M, Mueller S, Aneja S, Nabavizadeh A, Galldiks N, Lohmann P, Raji C, Ikuta I, Memon F, Weinberg BD, Aboian MS. Evolution and implementation of radiographic response criteria in neuro-oncology. Neurooncol Adv 2023; 5:vdad118. [PMID: 37860269 PMCID: PMC10584081 DOI: 10.1093/noajnl/vdad118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.
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Affiliation(s)
- Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mark Krycia
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Sanjay Aneja
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ali Nabavizadeh
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
| | - Cyrus Raji
- Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brent D Weinberg
- Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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Zhao M, Ma H, Cheng P, Yang H, Zhao Y, Han Q. Apatinib combined with temozolomide treatment for pseudoprogression in glioblastoma: A case report. Medicine (Baltimore) 2022; 101:e32156. [PMID: 36626518 PMCID: PMC9750629 DOI: 10.1097/md.0000000000032156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
RATIONALE Glioblastoma is the most common malignant tumor of the central nervous system, which originates from glial cells and corresponding precursors. Due to its strong invasion and rapid growth, the prognosis of patients after treatment is very poor and easy to relapse. PATIENT CONCERNS In August 2015, a 48 years old man with a relapse of glioblastoma. DIAGNOSES The patient was diagnosed by computed tomography, magnetic resonance imaging, and pathological biopsy in this case report. INTERVENTIONS The patient underwent 2 surgeries, radiotherapy, and multiple regular chemotherapy sessions over the next 6 years. Apatinib, an inhibitor of vascular endothelial growth factor receptor 2 was given to treat recurrent glioma. OUTCOMES It was found that radiotherapy combined with temozolomide administration often increased the size of the original lesion or produced a new glioblastoma lesion. The lesion development was similar to tumor progression, which was called pseudoprogression. And it significantly prolonged the survival of this patient. LESSONS Surgery, radiotherapy and chemotherapy with apatinib and temozolomide are effective to treat the patients with pseudoprogression in glioblastoma.
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Affiliation(s)
- Mingming Zhao
- First Ward of Cancer Center, People’s Hospital of Henan University, Zhengzhou, China
| | - Haodong Ma
- First Ward of Cancer Center, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Peng Cheng
- First Ward of Cancer Center, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Hongjie Yang
- First Ward of Cancer Center, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yang Zhao
- First Ward of Cancer Center, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qian Han
- First Ward of Cancer Center, Henan Provincial People’s Hospital, Zhengzhou, China
- * Correspondence: Qian Han, First Ward of Cancer Center, Henan Provincial People’s Hospital, 7 Weiwu Road, Jinshui District, Zhengzhou 450003, China (e-mail: )
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El-Abtah ME, Talati P, Fu M, Chun B, Clark P, Peters A, Ranasinghe A, He J, Rapalino O, Batchelor TT, Gilberto Gonzalez R, Curry WT, Dietrich J, Gerstner ER, Ratai EM. Magnetic resonance spectroscopy outperforms perfusion in distinguishing between pseudoprogression and disease progression in patients with glioblastoma. Neurooncol Adv 2022; 4:vdac128. [PMID: 36071927 PMCID: PMC9446677 DOI: 10.1093/noajnl/vdac128] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
There is a need to establish biomarkers that distinguish between pseudoprogression (PsP) and true tumor progression in patients with glioblastoma (GBM) treated with chemoradiation.
Methods
We analyzed magnetic resonance spectroscopic imaging (MRSI) and dynamic susceptibility contrast (DSC) MR perfusion data in patients with GBM with PsP or disease progression after chemoradiation. MRSI metabolites of interest included intratumoral choline (Cho), myo-inositol (mI), glutamate + glutamine (Glx), lactate (Lac), and creatine on the contralateral hemisphere (c-Cr). Student T-tests and area under the ROC curve analyses were used to detect group differences in metabolic ratios and their ability to predict clinical status, respectively.
Results
28 subjects (63 ± 9 years, 19 men) were evaluated. Subjects with true progression (n = 20) had decreased enhancing region mI/c-Cr (P = .011), a marker for more aggressive tumors, compared to those with PsP, which predicted tumor progression (AUC: 0.84 [0.76, 0.92]). Those with true progression had elevated Lac/Glx (P = .0009), a proxy of the Warburg effect, compared to those with PsP which predicted tumor progression (AUC: 0.84 [0.75, 0.92]). Cho/c-Cr did not distinguish between PsP and true tumor progression. Despite rCBV (AUC: 0.70 [0.60, 0.80]) and rCBF (AUC: 0.75 [0.65, 0.84]) being individually predictive of tumor response, they added no additional predictive value when combined with MRSI metabolic markers.
Conclusions
Incorporating enhancing lesion MRSI measures of mI/c-Cr and Lac/Glx into brain tumor imaging protocols can distinguish between PsP and true progression and inform patient management decisions.
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Affiliation(s)
- Mohamed E El-Abtah
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Pratik Talati
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
- Department of Neurosurgery, Massachusetts General Hospital , Boston, Massachusetts , USA
| | - Melanie Fu
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Benjamin Chun
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Patrick Clark
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Anna Peters
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Anthony Ranasinghe
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Julian He
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital , Boston, Massachusetts , USA
- Harvard Medical School , Boston, Massachusetts , USA
| | - Tracy T Batchelor
- Harvard Medical School , Boston, Massachusetts , USA
- Brigham and Women’s Hospital, Neurosciences Center , Boston, Massachusetts , USA
| | - R Gilberto Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
- Department of Radiology, Massachusetts General Hospital , Boston, Massachusetts , USA
- Harvard Medical School , Boston, Massachusetts , USA
| | - William T Curry
- Department of Neurosurgery, Massachusetts General Hospital , Boston, Massachusetts , USA
- Harvard Medical School , Boston, Massachusetts , USA
- Massachusetts General Hospital Cancer Center , Boston, Massachusetts , USA
| | - Jorg Dietrich
- Harvard Medical School , Boston, Massachusetts , USA
- Massachusetts General Hospital Cancer Center , Boston, Massachusetts , USA
| | - Elizabeth R Gerstner
- Harvard Medical School , Boston, Massachusetts , USA
- Massachusetts General Hospital Cancer Center , Boston, Massachusetts , USA
| | - Eva-Maria Ratai
- Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts , USA
- Department of Radiology, Massachusetts General Hospital , Boston, Massachusetts , USA
- Harvard Medical School , Boston, Massachusetts , USA
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