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Bobholz SA, Lowman AK, Connelly JM, Duenweg SR, Winiarz A, Nath B, Kyereme F, Brehler M, Bukowy J, Coss D, Lupo JM, Phillips JJ, Ellingson BM, Krucoff MO, Mueller WM, Banerjee A, LaViolette PS. Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement. Neurosurgery 2024:00006123-990000000-01091. [PMID: 38501824 DOI: 10.1227/neu.0000000000002898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/09/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures. METHODS A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability. RESULTS Tumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027). CONCLUSION This study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures.
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Affiliation(s)
- Samuel A Bobholz
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Allison K Lowman
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Jennifer M Connelly
- Department of Neurology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Savannah R Duenweg
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Michael Brehler
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee , Wisconsin , USA
| | - Dylan Coss
- Department of Pathology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco , California , USA
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco and Berkeley , California , USA
| | - Joanna J Phillips
- Department of Neurological Surgery, University of California, San Francisco , California , USA
- Department of Pathology, University of California, San Francisco , California , USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles , California , USA
| | - Max O Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Wade M Mueller
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Anjishnu Banerjee
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
- Department of Biophysics, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee , Wisconsin , USA
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2
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Saatian B, Deshpande K, Herrera R, Sedighi S, Eisenbarth R, Iyer M, Das D, Julian A, Martirosian V, Lowman A, LaViolette P, Remsik J, Boire A, Sankey E, Fecci PE, Shiroishi MS, Chow F, Hurth K, Neman J. Breast-to-brain metastasis is exacerbated with chemotherapy through blood-cerebrospinal fluid barrier and induces Alzheimer's-like pathology. J Neurosci Res 2023; 101:1900-1913. [PMID: 37787045 PMCID: PMC10769085 DOI: 10.1002/jnr.25249] [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: 03/17/2022] [Revised: 08/09/2023] [Accepted: 09/04/2023] [Indexed: 10/04/2023]
Abstract
Control of breast-to-brain metastasis remains an urgent unmet clinical need. While chemotherapies are essential in reducing systemic tumor burden, they have been shown to promote non-brain metastatic invasiveness and drug-driven neurocognitive deficits through the formation of neurofibrillary tangles (NFT), independently. Now, in this study, we investigated the effect of chemotherapy on brain metastatic progression and promoting tumor-mediated NFT. Results show chemotherapies increase brain-barrier permeability and facilitate enhanced tumor infiltration, particularly through the blood-cerebrospinal fluid barrier (BCSFB). This is attributed to increased expression of matrix metalloproteinase 9 (MMP9) which, in turn, mediates loss of Claudin-6 within the choroid plexus cells of the BCSFB. Importantly, increased MMP9 activity in the choroid epithelium following chemotherapy results in cleavage and release of Tau from breast cancer cells. This cleaved Tau forms tumor-derived NFT that further destabilize the BCSFB. Our results underline for the first time the importance of the BCSFB as a vulnerable point of entry for brain-seeking tumor cells post-chemotherapy and indicate that tumor cells themselves contribute to Alzheimer's-like tauopathy.
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Affiliation(s)
- B Saatian
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - K Deshpande
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - R Herrera
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - S Sedighi
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - R Eisenbarth
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - M Iyer
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
| | - D Das
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
| | - A Julian
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - V Martirosian
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
| | - A Lowman
- Department of Radiology and Biomedical Engineering, Medical College of Wisconsin
| | - P LaViolette
- Department of Radiology and Biomedical Engineering, Medical College of Wisconsin
| | - J Remsik
- Department of Neurology, Memorial Sloan Kettering Cancer Center
| | - A Boire
- Department of Neurology, Memorial Sloan Kettering Cancer Center
| | - E Sankey
- Department of Neurosurgery, Duke University School of Medicine
| | - PE Fecci
- Department of Neurosurgery, Duke University School of Medicine
| | - MS Shiroishi
- Brain Tumor Center, University of Southern California
- Department of Pathology, Keck School of Medicine, University of Southern California
| | - F Chow
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
- Norris Comprehensive Cancer Center, University of Southern California
| | - K Hurth
- Brain Tumor Center, University of Southern California
- Department of Neuroscience and Physiology, Keck School of Medicine, University of Southern California
- Norris Comprehensive Cancer Center, University of Southern California
| | - J Neman
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California
- Brain Tumor Center, University of Southern California
- Department of Neuroscience and Physiology, Keck School of Medicine, University of Southern California
- Department of Radiology, Keck School of Medicine, University of Southern California
- Norris Comprehensive Cancer Center, University of Southern California
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3
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Lovibond S, Gewirtz AN, Pasquini L, Krebs S, Graham MS. The promise of metabolic imaging in diffuse midline glioma. Neoplasia 2023; 39:100896. [PMID: 36944297 PMCID: PMC10036941 DOI: 10.1016/j.neo.2023.100896] [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: 10/14/2022] [Revised: 02/10/2023] [Accepted: 03/13/2023] [Indexed: 03/23/2023]
Abstract
Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27-altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.
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Affiliation(s)
- Samantha Lovibond
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandra N Gewirtz
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Luca Pasquini
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Krebs
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Radiochemistry and Imaging Sciences Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Maya S Graham
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Puac-Polanco P, Zakhari N, Miller J, McComiskey D, Thornhill RE, Jansen GH, Nair VJ, Nguyen TB. Diagnostic Accuracy of Centrally Restricted Diffusion Sign in Cerebral Metastatic Disease: Differentiating Radiation Necrosis from Tumor Recurrence. Can Assoc Radiol J 2023; 74:100-109. [PMID: 35848632 DOI: 10.1177/08465371221115341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Purpose: The centrally restricted diffusion sign of diffusion-weighted imaging (DWI) is associated with radiation necrosis (RN) in treated gliomas. Our goal was to evaluate its diagnostic accuracy to distinguish RN from tumor recurrence (TR) in treated brain metastases. Methods: Retrospective study of consecutive patients with brain metastases who developed a newly centrally necrotic lesion after radiotherapy (RT). One reader placed regions of interest (ROI) in the enhancing solid lesion and the non-enhancing central necrosis on the apparent diffusion coefficient (ADC) map. Two readers qualitatively assessed the presence of the centrally restricted diffusion sign. The final diagnosis was made by histopathology (n = 39) or imaging follow-up (n = 2). Differences between groups were assessed by Fisher's exact or Mann-Whitney U tests. Diagnostic accuracy and inter-reader agreement were evaluated using receiver operating characteristic (ROC) curve analysis and kappa scores. Results: Forty-one lesions (32 predominant RN; 9 predominant TR) were analyzed. An ADC value ≤ 1220 × 10-6 mm2/s (sensitivity 74%, specificity 89%, area under the curve [AUC] .85 [95% confidence interval {CI}, .70-.94] P < .0001) from the necrosis and an ADC necrosis/enhancement ratio ≤1.37 (sensitivity 74%, specificity 89%, AUC .82 [95% CI, .67-.93] P < .0001) provided the highest performance for RN diagnosis. The qualitative centrally restricted diffusion sign had a sensitivity of 69% (95% CI, .50-.83), specificity of 77% (95% CI, .40-.96), and a moderate (k = .49) inter-reader agreement for RN diagnosis. Conclusions: Radiation necrosis is associated with lower ADC values in the central necrosis than TR. A moderate interobserver agreement might limit the qualitative assessment of the centrally restricted diffusion sign.
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Affiliation(s)
- Paulo Puac-Polanco
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada
| | - Nader Zakhari
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada
| | - Jacob Miller
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada
| | - David McComiskey
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada
| | - Rebecca E Thornhill
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada
| | - Gerard H Jansen
- Department of Pathology and Laboratory Medicine, The Ottawa Hospital, 6363University of Ottawa, Ottawa, ON, Canada
| | - Vimoj J Nair
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada.,The Ottawa Hospital Research Institute (OHRI)
| | - Thanh Binh Nguyen
- Department of Radiology, Radiation Oncology and Medical Physics, 6363University of Ottawa, Ottawa, ON, Canada.,The Ottawa Hospital Research Institute (OHRI)
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5
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Brabec J, Friedjungová M, Vašata D, Englund E, Bengzon J, Knutsson L, Szczepankiewicz F, van Westen D, Sundgren PC, Nilsson M. Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology. Neuroimage Clin 2023; 37:103365. [PMID: 36898293 PMCID: PMC10020119 DOI: 10.1016/j.nicl.2023.103365] [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: 11/25/2022] [Revised: 02/08/2023] [Accepted: 02/25/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PURPOSE To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters. MATERIALS AND METHODS We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively. RESULTS Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. CONCLUSION Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Magda Friedjungová
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | - Daniel Vašata
- Faculty of Information Technology, Czech Technical University in Prague, Prague, Czech Republic
| | | | - Johan Bengzon
- Neurosurgery, Clinical Sciences, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Pia C Sundgren
- Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden; Lund University Bioimaging Centre, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Markus Nilsson
- Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden; Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden
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Brabec J, Durmo F, Szczepankiewicz F, Brynolfsson P, Lampinen B, Rydelius A, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding. Front Neurosci 2022; 16:842242. [PMID: 35527815 PMCID: PMC9069143 DOI: 10.3389/fnins.2022.842242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation. Purpose To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter. Materials and Methods Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff). Results The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected. Conclusion The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.
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Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Lund University, Lund, Sweden
- *Correspondence: Jan Brabec,
| | - Faris Durmo
- Diagnostic Radiology, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Diagnostic Radiology, Lund University, Lund, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Patrik Brynolfsson
- Division of Medical Radiation Physics, Department of Translational Medicine, Lund University, Lund, Sweden
| | - Björn Lampinen
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anna Rydelius
- Department of Neurology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Pia C. Sundgren
- Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University Bioimaging Center, Lund University, Lund, Sweden
- Department of Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
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Bobholz SA, Lowman AK, Brehler M, Kyereme F, Duenweg SR, Sherman J, McGarry SD, Cochran EJ, Connelly J, Mueller WM, Agarwal M, Banerjee A, LaViolette PS. Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins. AJNR Am J Neuroradiol 2022; 43:682-688. [PMID: 35422419 PMCID: PMC9089258 DOI: 10.3174/ajnr.a7477] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 02/07/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Currently, contrast-enhancing margins on T1WI are used to guide treatment of gliomas, yet tumor invasion beyond the contrast-enhancing region is a known confounding factor. Therefore, this study used postmortem tissue samples aligned with clinically acquired MRIs to quantify the relationship between intensity values and cellularity as well as to develop a radio-pathomic model to predict cellularity using MR imaging data. MATERIALS AND METHODS This single-institution study used 93 samples collected at postmortem examination from 44 patients with brain cancer. Tissue samples were processed, stained with H&E, and digitized for nuclei segmentation and cell density calculation. Pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC images were collected from each patient's final acquisition before death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed-effects models were used to assess the relationship between single-image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 × 5 voxel tiles from each image, with a two-thirds to one-third train-test split for validation. RESULTS Single-image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship in patients with glioblastoma. The radio-pathomic model accurately predicted cellularity in the test set (root mean squared error = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast-enhancing region. CONCLUSIONS A radio-pathomic model for cellularity trained with tissue samples acquired at postmortem examination is able to identify regions of hypercellular tumor beyond traditional imaging signatures.
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Affiliation(s)
- S A Bobholz
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | | | - M Brehler
- Radiology (A.L., M.B., M.A., P.S.L.)
| | | | - S R Duenweg
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | - J Sherman
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | - S D McGarry
- From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.)
| | | | | | | | - M Agarwal
- Radiology (A.L., M.B., M.A., P.S.L.)
| | | | - P S LaViolette
- Radiology (A.L., M.B., M.A., P.S.L.)
- Biomedical Engineering (P.S.L.), Medical College of Wisconsin, Milwaukee, Wisconsin
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Wu C, Zheng H, Li J, Zhang Y, Duan S, Li Y, Wang D. MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain. Eur Radiol 2022; 32:1813-1822. [PMID: 34655310 DOI: 10.1007/s00330-021-08234-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/11/2021] [Accepted: 07/26/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To develop a nomogram based on MRI radiomics and clinical features for preoperatively predicting H3K27M mutation in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. METHODS The institutional database was reviewed to identify patients with pHGGs with a midline location of the brain who underwent tumor biopsy with preoperative MRI scans between June 2016 and June 2021. A total of 107 patients with pHGGs, including 79 patients with H3K27M mutation, were consecutively included and randomly divided into training and test sets. Radiomics features were extracted from fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) and post-contrast T1-weighted images, and apparent diffusion coefficient (ADC) maps. The minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were performed for radiomics signature construction. Clinical and radiological features were analyzed to select clinical predictors. A nomogram was then developed by incorporating the radiomics signature and selected clinical predictors. RESULTS Nine radiomics features were selected to construct the radiomics signature, which showed a favorable discriminatory ability in training and test sets with an area under the curve (AUC) of 0.95 and 0.92, respectively. Ring enhancement was identified as an independent clinical predictor (p < 0.01). The nomogram, constructed with radiomics signature and ring enhancement, showed good calibration and discrimination in training and testing sets (AUC: 0.95 and 0.90 respectively). CONCLUSIONS The nomogram which combined radiomics signature and ring enhancement had a satisfactory ability to predict H3K27M mutation in pHGGs with a midline of the brain. KEY POINTS • Conventional MRI features were not powerful enough to predict H3K27M mutation status in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. • An MRI-based radiomics signature showed satisfactory ability to predict H3K27M mutation status of pHGGs located in the midline of the brain. • Associating the radiomics signature with clinical factors improved predictive performance.
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Affiliation(s)
- Chenqing Wu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Hui Zheng
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Yuzhen Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Shaofeng Duan
- GE Healthcare, Pudong New Town, No.1 Huatuo Road, Shanghai, 210000, China
| | - Yuhua Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China.
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9
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Do YA, Cho SJ, Choi BS, Baik SH, Bae YJ, Sunwoo L, Jung C, Kim JH. Predictive Accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q non-codeleted Low Grade Glioma: An updated Systematic Review and Meta-analysis. Neurooncol Adv 2022; 4:vdac010. [PMID: 35198981 PMCID: PMC8859831 DOI: 10.1093/noajnl/vdac010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies. Methods A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity. Results For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28–53%), and the pooled specificity was 100% (95% CI 95–100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors. Conclusions The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.
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Affiliation(s)
- Yoon Ah Do
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Se Jin Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Sung Hyun Baik
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Cheolkyu Jung
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
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10
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Lombardi G, Spimpolo A, Berti S, Campi C, Anglani MG, Simeone R, Evangelista L, Causin F, Zorzi G, Gorgoni G, Caccese M, Padovan M, Zagonel V, Cecchin D. PET/MR in recurrent glioblastoma patients treated with regorafenib: [ 18F]FET and DWI-ADC for response assessment and survival prediction. Br J Radiol 2022; 95:20211018. [PMID: 34762492 PMCID: PMC8722234 DOI: 10.1259/bjr.20211018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Objective: The use of regorafenib in recurrent glioblastoma patients has been recently approved by the Italian Medicines Agency (AIFA) and added to the National Comprehensive Cancer Network (NCCN) 2020 guidelines as a preferred regimen. Given its complex effects at the molecular level, the most appropriate imaging tools to assess early response to treatment is still a matter of debate. Diffusion-weighted imaging and O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography ([18F]FET PET) are promising methodologies providing additional information to the currently used RANO criteria. The aim of this study was to evaluate the variations in diffusion-weighted imaging/apparent diffusion coefficient (ADC) and [18F]FET PET-derived parameters in patients who underwent PET/MR at both baseline and after starting regorafenib. Methods: We retrospectively reviewed 16 consecutive GBM patients who underwent [18F]FET PET/MR before and after two cycles of regorafenib. Patients were sorted into stable (SD) or progressive disease (PD) categories in accordance with RANO criteria. We were also able to analyze four SD patients who underwent a third PET/MR after another four cycles of regorafenib. [18F]FET uptake greater than 1.6 times the mean background activity was used to define an area to be superimposed on an ADC map at baseline and after treatment. Several metrics were then derived and compared. Log-rank test was applied for overall survival analysis. Results: Percentage difference in FET volumes correlates with the corresponding percentage difference in ADC (R = 0.54). Patients with a twofold increase in FET after regorafenib showed a significantly higher increase in ADC pathological volume than the remaining subjects (p = 0.0023). Kaplan–Meier analysis, performed to compare the performance in overall survival prediction, revealed that the percentage variations of FET- and ADC-derived metrics performed at least as well as RANO criteria (p = 0.02, p = 0.024 and p = 0.04 respectively) and in some cases even better. TBR Max and TBR mean are not able to accurately predict overall survival. Conclusion In recurrent glioblastoma patients treated with regorafenib, [18F]FET and ADC metrics, are able to predict overall survival and being obtained from completely different measures as compared to RANO, could serve as semi-quantitative independent biomarkers of response to treatment. Advances in knowledge Simultaneous evaluation of [18F]FET and ADC metrics using PET/MR allows an early and reliable identification of response to treatment and predict overall survival.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Alessandro Spimpolo
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Sara Berti
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Cristina Campi
- Department of Mathematics, University of Genoa, Genoa, Italy
| | | | - Rossella Simeone
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
| | - Francesco Causin
- Neuroradiology Unit, Azienda Ospedaliera di Padova, Padua, Italy
| | - Giovanni Zorzi
- Department of Neurosciences (DNS), University of Padua, Padua, Italy
| | - Giancarlo Gorgoni
- Radiopharmacy, Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of Oncology - IRCCS, Padua, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, Padua University Hospital, Padua, Italy
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11
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Matsuno A, Takaya Y, Chen H, TEN H, Hoya K, Jiang CL, Oyama KI, Onoda K. Evaluation of the malignant potential of gliomas using diffusion-weighted and gadolinium-enhanced magnetic resonance imaging. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2021.9050023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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12
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Pasquini L, Napolitano A, Lucignani M, Tagliente E, Dellepiane F, Rossi-Espagnet MC, Ritrovato M, Vidiri A, Villani V, Ranazzi G, Stoppacciaro A, Romano A, Di Napoli A, Bozzao A. AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well? Front Oncol 2021; 11:601425. [PMID: 34888226 PMCID: PMC8649764 DOI: 10.3389/fonc.2021.601425] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 11/02/2021] [Indexed: 12/30/2022] Open
Abstract
Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Francesco Dellepiane
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Maria Camilla Rossi-Espagnet
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Matteo Ritrovato
- Unit of Health Technology Assessment (HTA), Biomedical Technology Risk Manager, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Veronica Villani
- Neuro-Oncology Unit, Regina Elena National Cancer Institute, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), Rome, Italy
| | - Giulio Ranazzi
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonella Stoppacciaro
- Department of Clinical and Molecular Medicine, Surgical Pathology Units, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Radiology Department, Castelli Romani Hospital, Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, Neuroscience, Mental Health and Sensory Organs (NESMOS) Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
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Magnetic Resonance Relaxometry for Tumor Cell Density Imaging for Glioma: An Exploratory Study via 11C-Methionine PET and Its Validation via Stereotactic Tissue Sampling. Cancers (Basel) 2021; 13:cancers13164067. [PMID: 34439221 PMCID: PMC8393497 DOI: 10.3390/cancers13164067] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/01/2021] [Accepted: 08/11/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary To test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and 11C-methionine (MET) on positron emission tomography (PET). Moreover, we compared T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. A T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated high MET uptake. The stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850–3200 ms significantly indicated higher TCD while the T2-relaxation time and ADC did not significantly correlate with the stereotactic tissue sampling findings. However, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET. Abstract One of the most crucial yet challenging issues for glioma patient care is visualizing non-contrast-enhancing tumor regions. In this study, to test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and 11C-methionine (MET) on positron emission tomography (PET). Moreover, we compared the T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. Regions that presented a T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated a high MET uptake. In addition, the stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850–3200 ms significantly indicated a higher TCD (p = 0.04). However, ADC was unable to show a significant correlation with MET uptake or with TCD. Finally, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET.
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14
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Pasquini L, Di Napoli A, Napolitano A, Lucignani M, Dellepiane F, Vidiri A, Villani V, Romano A, Bozzao A. Glioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection. J Neuroimaging 2021; 31:1192-1200. [PMID: 34231927 DOI: 10.1111/jon.12903] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Glioblastoma (GBM) is an aggressive primary CNS neoplasm with poor overall survival (OS) despite standard of care. On MRI, GBM is usually characterized by an enhancing portion (CET) (surgery target) and a nonenhancing surrounding (NET). Extent of resection is a long debated issue in GBM, with recent evidence suggesting that both CET and NET should be resected in <65 years old patients, regardless of other risk factors (i.e., molecular biomarkers). Our aim was to test a radiomic model for patient survival stratification in <65 years old patients, by analyzing MRI features of NET, to aid tumor resection. METHODS Sixty-eight <65 years old GBM patients, with extensive CET resection, were selected. Resection was evaluated by manually segmenting CET on volumetric T1-weighted MRI pre and postsurgery (within 72 h). All patients underwent the same treatment protocol including chemoradiation. NET radiomic features were extracted with a custom version of Pyradiomics. Feature selection was performed with principal component analysis (PCA) and its effect on survival tested with Cox regression model. Twelve months OS discrimination was tested by t-test followed by logistic regression. Statistical significance was set at p<0.05. The most relevant features were identified from the component matrix. RESULTS Five PCA components (PC1-5) explained 90% of the variance. PC5 resulted significant in the Cox model (p = 0.002; exp(B) = 0.686), at t-test (p = 0.002) and logistic regression analysis (p = 0.006). Apparent diffusion coefficient (ADC)-based features were the most significant for patient survival stratification. CONCLUSIONS ADC radiomic features on NET predict survival after standard therapy and could be used to improve patient selection for more extensive surgery.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA.,Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy.,Radiology Department, Castelli Romani Hospital, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Francesco Dellepiane
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, IRCCS, Rome, Italy
| | - Veronica Villani
- Neuro-Oncology Unit, Regina Elena National Cancer Institute, IRCCS, Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, NESMOS Department, Sant'Andrea Hospital, La Sapienza University, Rome, Italy
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15
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Pasquini L, Napolitano A, Tagliente E, Dellepiane F, Lucignani M, Vidiri A, Ranazzi G, Stoppacciaro A, Moltoni G, Nicolai M, Romano A, Di Napoli A, Bozzao A. Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM. J Pers Med 2021; 11:290. [PMID: 33918828 PMCID: PMC8069494 DOI: 10.3390/jpm11040290] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022] Open
Abstract
Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy; (E.T.); (M.L.)
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy; (E.T.); (M.L.)
| | - Francesco Dellepiane
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy; (E.T.); (M.L.)
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi 53, 00144 Rome, Italy;
| | - Giulio Ranazzi
- Surgical Pathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (G.R.); (A.S.)
| | - Antonella Stoppacciaro
- Surgical Pathology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (G.R.); (A.S.)
| | - Giulia Moltoni
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Matteo Nicolai
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Andrea Romano
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
| | - Alessandro Bozzao
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy; (L.P.); (F.D.); (G.M.); (M.N.); (A.R.); (A.D.N.); (A.B.)
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Li Y, Kim M, Lawrence TS, Parmar H, Cao Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. ACTA ACUST UNITED AC 2021; 6:34-43. [PMID: 32280748 PMCID: PMC7138521 DOI: 10.18383/j.tom.2020.00018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm2 were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (Vin), and extracellular (Dex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, Dex, and Vin from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). Vin values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Michelle Kim
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Theodore S Lawrence
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Hemant Parmar
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
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Abdeen N. Editorial for "Comparison between diffusion weighted MRI and 123 I-MIBG uptake in primary high risk neuroblastoma". J Magn Reson Imaging 2021; 53:1498-1499. [PMID: 33426752 DOI: 10.1002/jmri.27507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 11/07/2022] Open
Affiliation(s)
- Nishard Abdeen
- Department of Medical Imaging, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada
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Kamson D, Tsien C. Novel Magnetic Resonance Imaging and Positron Emission Tomography in the RT Planning and Assessment of Response of Malignant Gliomas. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Hu LS, Brat DJ, Bloch O, Ramkissoon S, Lesser GJ. The Practical Application of Emerging Technologies Influencing the Diagnosis and Care of Patients With Primary Brain Tumors. Am Soc Clin Oncol Educ Book 2020; 40:1-12. [PMID: 32324425 DOI: 10.1200/edbk_280955] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Over the past decade, a variety of new and innovative technologies has led to important advances in the diagnosis and management of patients with primary malignant brain tumors. New approaches to surgical navigation and tumor localization, advanced imaging to define tumor biology and treatment response, and the widespread adoption of a molecularly defined integrated diagnostic paradigm that complements traditional histopathologic diagnosis continue to impact the day-to-day care of these patients. In the neuro-oncology clinic, discussions with patients about the role of tumor treating fields (TTFields) and the incorporation of next-generation sequencing (NGS) data into therapeutic decision-making are now a standard practice. This article summarizes newer applications of technology influencing the pathologic, neuroimaging, neurosurgical, and medical management of patients with malignant primary brain tumors.
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Affiliation(s)
- Leland S Hu
- Neuroradiology Section, Department of Radiology, Mayo Clinic, Phoenix, AZ
| | - Daniel J Brat
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Orin Bloch
- Department of Neurologic Surgery, UC Davis Comprehensive Cancer Center, Sacramento, CA
| | - Shakti Ramkissoon
- Foundation Medicine, Inc., Morrisville, NC.,Comprehensive Cancer Center, Wake Forest Baptist Health, Winston-Salem, NC.,Department of Pathology, Wake Forest School of Medicine, Winston-Salem, NC
| | - Glenn J Lesser
- Comprehensive Cancer Center, Wake Forest Baptist Health, Winston-Salem, NC
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20
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Privitera L, Hales PW, Musleh L, Morris E, Sizer N, Barone G, Humphries P, Cross K, Biassoni L, Giuliani S. Comparison Between Diffusion-Weighted MRI and 123 I-mIBG Uptake in Primary High-Risk Neuroblastoma. J Magn Reson Imaging 2020; 53:1486-1497. [PMID: 33283381 PMCID: PMC8246892 DOI: 10.1002/jmri.27458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/15/2020] [Accepted: 11/17/2020] [Indexed: 01/03/2023] Open
Abstract
Background High‐risk neuroblastoma (HR‐NB) has a variable response to preoperative chemotherapy. It is not possible to differentiate viable vs. nonviable residual tumor before surgery. Purpose To explore the association between apparent diffusion coefficient (ADC) values from diffusion‐weighted magnetic resonance imaging (DW‐MRI), 123I‐meta‐iodobenzyl‐guanidine (123I‐mIBG) uptake, and histology before and after chemotherapy. Study Type Retrospective. Subjects Forty patients with HR‐NB. Field Strength/Sequence 1.5T axial DW‐MRI (b = 0,1000 s/mm2) and T2‐weighted sequences. 123I‐mIBG scintigraphy planar imaging (all patients), with additional 123I‐mIBG single‐photon emission computed tomography / computerized tomography (SPECT/CT) imaging (15 patients). Assessment ADC maps and 123I‐mIBG SPECT/CT images were coregistered to the T2‐weighted images. 123I‐mIBG uptake was normalized with a tumor‐to‐liver count ratio (TLCR). Regions of interest (ROIs) for primary tumor volume and different intratumor subregions were drawn. The lower quartile ADC value (ADC25prc) was used over the entire tumor volume and the overall level of 123I‐mIBG uptake was graded into avidity groups. Statistical Tests Analysis of variance (ANOVA) and linear regression were used to compare ADC and MIBG values before and after treatment. Threshold values to classify tumors as viable/necrotic were obtained using ROC analysis of ADC and TLCR values. Results No significant difference in whole‐tumor ADC25prc values were found between different 123I‐mIBG avidity groups pre‐ (P = 0.31) or postchemotherapy (P = 0.35). In the “intratumor” analysis, 5/15 patients (prechemotherapy) and 0/14 patients (postchemotherapy) showed a significant correlation between ADC and TLCR values (P < 0.05). Increased tumor shrinkage was associated with lower pretreatment tumor ADC25prc values (P < 0.001); no association was found with pretreatment 123I‐mIBG avidity (P = 0.17). Completely nonviable tumors had significantly lower postchemotherapy ADC25prc values than tumors with >10% viable tumor (P < 0.05). Both pre‐ and posttreatment TLCR values were significantly higher in patients with >50% viable tumor than those with 10–50% viable tumor (P < 0.05). Data Conclusion 123I‐mIBG avidity and ADC values are complementary noninvasive biomarkers of therapeutic response in HR‐NB. Level of Evidence 4. Technical Efficacy Stage 3.
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Affiliation(s)
- Laura Privitera
- Department of Specialist Neonatal and Paediatric Surgery, Great Ormond Street Hospital for Children, London, UK
| | - Patrick W Hales
- Developmental Imaging and Biophysics Section, University College London Great Ormond Street Insitute of Child Health, London, UK
| | - Layla Musleh
- Department of Specialist Neonatal and Paediatric Surgery, Great Ormond Street Hospital for Children, London, UK
| | - Elizabeth Morris
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK.,Nuclear Medicine Physics, Clinical Physics, Barts Health NHS Trust, London, UK
| | - Natalie Sizer
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK.,Nuclear Medicine Physics, Clinical Physics, Barts Health NHS Trust, London, UK
| | - Giuseppe Barone
- Department of Haematology and Oncology, Great Ormond Street Hospital for Children, London, UK
| | - Paul Humphries
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - Kate Cross
- Department of Specialist Neonatal and Paediatric Surgery, Great Ormond Street Hospital for Children, London, UK
| | - Lorenzo Biassoni
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - Stefano Giuliani
- Department of Specialist Neonatal and Paediatric Surgery, Great Ormond Street Hospital for Children, London, UK
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21
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Shankar A, Bomanji J, Hyare H. Hybrid PET-MRI Imaging in Paediatric and TYA Brain Tumours: Clinical Applications and Challenges. J Pers Med 2020; 10:jpm10040218. [PMID: 33182433 PMCID: PMC7711629 DOI: 10.3390/jpm10040218] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/29/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
Abstract
(1) Background: Standard magnetic resonance imaging (MRI) remains the gold standard for brain tumour imaging in paediatric and teenage and young adult (TYA) patients. Combining positron emission tomography (PET) with MRI offers an opportunity to improve diagnostic accuracy. (2) Method: Our single-centre experience of 18F-fluorocholine (FCho) and 18fluoro-L-phenylalanine (FDOPA) PET–MRI in paediatric/TYA neuro-oncology patients is presented. (3) Results: Hybrid PET–MRI shows promise in the evaluation of gliomas and germ cell tumours in (i) assessing early treatment response and (ii) discriminating tumour from treatment-related changes. (4) Conclusions: Combined PET–MRI shows promise for improved diagnostic and therapeutic assessment in paediatric and TYA brain tumours.
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Affiliation(s)
- Ananth Shankar
- Children and Young People’s Cancer Services, University College London hospitals NHS Foundation Trust, London NW1 2PG, UK
- Correspondence: ; Tel.: +44-20-3447-9950
| | - Jamshed Bomanji
- Department of Nuclear Medicine, University College London hospitals NHS Foundation Trust, London NW1 2PG, UK;
| | - Harpreet Hyare
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK;
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London WC1N 3BG, UK
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22
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Blystad I, Warntjes JBM, Smedby Ö, Lundberg P, Larsson EM, Tisell A. Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema. Sci Rep 2020; 10:17986. [PMID: 33093605 PMCID: PMC7581520 DOI: 10.1038/s41598-020-75105-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 10/12/2020] [Indexed: 11/09/2022] Open
Abstract
Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.
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Affiliation(s)
- I Blystad
- Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden. .,Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - J B M Warntjes
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Ö Smedby
- Department of Radiology in Linköping and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.,Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - P Lundberg
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiation Physics and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - E-M Larsson
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - A Tisell
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiation Physics and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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23
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Rogers HJ, Verhagen MV, Shelmerdine SC, Clark CA, Hales PW. An alternative approach to contrast-enhanced imaging: diffusion-weighted imaging and T 1-weighted imaging identifies and quantifies necrosis in Wilms tumour. Eur Radiol 2019; 29:4141-4149. [PMID: 30560365 PMCID: PMC6610268 DOI: 10.1007/s00330-018-5907-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/26/2018] [Accepted: 11/22/2018] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Volume of necrosis in Wilms tumour is informative of chemotherapy response. Contrast-enhanced T1-weighted MRI (T1w) provides a measure of necrosis using gadolinium. This study aimed to develop a non-invasive method of identifying non-enhancing (necrotic) tissue in Wilms tumour. METHODS In this single centre, retrospective study, post-chemotherapy MRI data from 34 Wilms tumour patients were reviewed (March 2012-March 2017). Cases with multiple b value diffusion-weighted imaging (DWI) and T1w imaging pre- and post-gadolinium were included. Fractional T1 enhancement maps were generated from the gadolinium T1w data. Multiple linear regression determined whether fitted parameters from a mono-exponential model (ADC) and bi-exponential model (IVIM - intravoxel incoherent motion) (D, D*, f) could predict fractional T1 enhancement in Wilms tumours, using normalised pre-gadolinium T1w (T1wnorm) signal as an additional predictor. Measured and predicted fractional enhancement values were compared using the Bland-Altman plot. An optimum threshold for separating necrotic and viable tissue using fractional T1 enhancement was established using ROC. RESULTS ADC and D (diffusion coefficient) provided the strongest predictors of fractional T1 enhancement in tumour tissue (p < 0.001). Using the ADC-T1wnorm model (adjusted R2 = 0.4), little bias (mean difference = - 0.093, 95% confidence interval = [- 0.52, 0.34]) was shown between predicted and measured values of fractional enhancement and analysed via the Bland-Altman plot. The optimal threshold for differentiating viable and necrotic tissue was 33% fractional T1 enhancement (based on measured values, AUC = 0.93; sensitivity = 85%; specificity = 90%). CONCLUSIONS Combining ADC and T1w imaging predicts enhancement in Wilms tumours and reliably identifies and measures necrotic tissue without gadolinium. KEY POINTS • Alternative method to identify necrotic tissue in Wilms tumour without using contrast agents but rather using diffusion and T 1 weighted MRI. • A method is presented to visualise and quantify necrotic tissue in Wilms tumour without contrast. • The proposed method has the potential to reduce costs and burden to Wilms tumour patients who undergo longitudinal follow-up imaging as contrast agents are not used.
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Affiliation(s)
- Harriet J Rogers
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK.
| | - Martijn V Verhagen
- Department of Radiology, Great Ormond Street Hospital For Children NHS Foundation Trust, London, WC1N 3JH, UK
| | - Susan C Shelmerdine
- Department of Radiology, Great Ormond Street Hospital For Children NHS Foundation Trust, London, WC1N 3JH, UK
| | - Christopher A Clark
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK
| | - Patrick W Hales
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, WC1N 1EH, UK
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24
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Lee CY, Kalra A, Spampinato MV, Tabesh A, Jensen JH, Helpern JA, de Fatima Falangola M, Van Horn MH, Giglio P. Early assessment of recurrent glioblastoma response to bevacizumab treatment by diffusional kurtosis imaging: a preliminary report. Neuroradiol J 2019; 32:317-327. [PMID: 31282311 DOI: 10.1177/1971400919861409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment. METHODS This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0-7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05. RESULTS Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = -0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D∥, D∥10% and D∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = -0.73, -0.83, -0.71 and -0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008). CONCLUSIONS This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.
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Affiliation(s)
- Chu-Yu Lee
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Amandeep Kalra
- 3 Department of Neuroscience, Medical University of South Carolina, USA.,4 Sarah Cannon Cancer Institute, USA
| | - Maria V Spampinato
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Ali Tabesh
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Jens H Jensen
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA
| | - Joseph A Helpern
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA.,5 Department of Neurology, Medical University of South Carolina, USA
| | - Maria de Fatima Falangola
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA
| | - Mark H Van Horn
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Pierre Giglio
- 3 Department of Neuroscience, Medical University of South Carolina, USA.,6 Department of Neurology, The Ohio State University Wexner Medical Center, USA
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25
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Jardim-Perassi BV, Huang S, Dominguez-Viqueira W, Poleszczuk J, Budzevich MM, Abdalah MA, Pillai SR, Ruiz E, Bui MM, Zuccari DAPC, Gillies RJ, Martinez GV. Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models. Cancer Res 2019; 79:3952-3964. [PMID: 31186232 DOI: 10.1158/0008-5472.can-19-0213] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 04/23/2019] [Accepted: 06/05/2019] [Indexed: 12/31/2022]
Abstract
It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. SIGNIFICANCE: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.
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Affiliation(s)
- Bruna V Jardim-Perassi
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida.,Faculdade de Medicina de Sao Jose do Rio Preto, Sao Jose do Rio Preto, Brazil
| | - Suning Huang
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida.,Guangxi Tumor Hospital, Nanning Guangxi, China
| | | | - Jan Poleszczuk
- Department of Integrative Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | | | - Mahmoud A Abdalah
- Image Response Assessment Team, Moffitt Cancer Center, Tampa, Florida
| | - Smitha R Pillai
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida
| | - Epifanio Ruiz
- Small Animal Imaging Laboratory, Moffitt Cancer Center, Tampa, Florida
| | - Marilyn M Bui
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, Florida
| | | | - Robert J Gillies
- Department of Cancer Physiology, Moffitt Cancer Center, Tampa, Florida.
| | - Gary V Martinez
- Small Animal Imaging Laboratory, Moffitt Cancer Center, Tampa, Florida.
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26
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Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J. Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning. AJNR Am J Neuroradiol 2019; 40:418-425. [PMID: 30819771 DOI: 10.3174/ajnr.a5981] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 12/13/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
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Affiliation(s)
- L S Hu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - H Yoon
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | | | - A C Dueck
- Department of Biostatistics (A.C.D.), Mayo Clinic in Arizona, Scottsdale, Arizona
| | | | | | - P Nakaji
- Neurosurgery (K.A.S., P.N., N.S.)
| | - Y Xu
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - L Wang
- Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | | | - A J Hawkins-Daarud
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - K W Singleton
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - P R Jackson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.)
| | - B J Anderies
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - B R Bendok
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - R S Zimmerman
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Quarles
- Neuroimaging Research (C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | | | - M M Mrugala
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - A Sharma
- Department of Neuro-Oncology (A.B.P.-U., M.M.M., A.S.)
| | - J M Hoxworth
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.)
| | - M G Sattur
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - N Sanai
- Neurosurgery (K.A.S., P.N., N.S.)
| | - P E Koulemberis
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - C Krishna
- Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J R Mitchell
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,H. Lee Moffitt Cancer Center and Research Institute (J.R.M.), Tampa, Florida
| | - T Wu
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
| | - N L Tran
- Department of Cancer Biology (N.L.T.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (A.J.H.-D., K.W.S., P.R.J, B.R.B., K.R.S.).,Department of Neurosurgery (B.J.A., B.R.B., R.S.Z., M.G.S., P.E.K., C.K., K.R.S.)
| | - J Li
- From the Department of Radiology (L.S.H., J.M.H., J.R.M., T.W., J.L.).,Arizona State University (H.Y., Y.X., L.W., T.W., J.L.), Tempe, Arizona
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Sarkaria JN, Hu LS, Parney IF, Pafundi DH, Brinkmann DH, Laack NN, Giannini C, Burns TC, Kizilbash SH, Laramy JK, Swanson KR, Kaufmann TJ, Brown PD, Agar NYR, Galanis E, Buckner JC, Elmquist WF. Is the blood-brain barrier really disrupted in all glioblastomas? A critical assessment of existing clinical data. Neuro Oncol 2019; 20:184-191. [PMID: 29016900 DOI: 10.1093/neuonc/nox175] [Citation(s) in RCA: 391] [Impact Index Per Article: 78.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The blood-brain barrier (BBB) excludes the vast majority of cancer therapeutics from normal brain. However, the importance of the BBB in limiting drug delivery and efficacy is controversial in high-grade brain tumors, such as glioblastoma (GBM). The accumulation of normally brain impenetrant radiographic contrast material in essentially all GBM has popularized a belief that the BBB is uniformly disrupted in all GBM patients so that consideration of drug distribution across the BBB is not relevant in designing therapies for GBM. However, contrary to this view, overwhelming clinical evidence demonstrates that there is also a clinically significant tumor burden with an intact BBB in all GBM, and there is little doubt that drugs with poor BBB permeability do not provide therapeutically effective drug exposures to this fraction of tumor cells. This review provides an overview of the clinical literature to support a central hypothesis: that all GBM patients have tumor regions with an intact BBB, and cure for GBM will only be possible if these regions of tumor are adequately treated.
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Affiliation(s)
- Jann N Sarkaria
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Leland S Hu
- Mayo Clinic, Scottsdale, Arizona (L.S.H., K.R.S.)
| | - Ian F Parney
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Deanna H Pafundi
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Debra H Brinkmann
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Nadia N Laack
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Caterina Giannini
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Terence C Burns
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Sani H Kizilbash
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Janice K Laramy
- University of Minnesota, Minneapolis, Minnesota (J.K.L., W.F.E.)
| | | | - Timothy J Kaufmann
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Paul D Brown
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | | | - Evanthia Galanis
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Jan C Buckner
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - William F Elmquist
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
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McGarry SD, Hurrell SL, Iczkowski KA, Hall W, Kaczmarowski AL, Banerjee A, Keuter T, Jacobsohn K, Bukowy JD, Nevalainen MT, Hohenwalter MD, See WA, LaViolette PS. Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer. Int J Radiat Oncol Biol Phys 2018; 101:1179-1187. [PMID: 29908785 PMCID: PMC6190585 DOI: 10.1016/j.ijrobp.2018.04.044] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 04/10/2018] [Accepted: 04/16/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization. METHODS AND MATERIALS Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer. RESULTS The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer. CONCLUSIONS We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy.
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Affiliation(s)
- Sean D McGarry
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sarah L Hurrell
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - William Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Amy L Kaczmarowski
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Anjishnu Banerjee
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Tucker Keuter
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kenneth Jacobsohn
- Department of Urological Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - John D Bukowy
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Marja T Nevalainen
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Pharmacology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Mark D Hohenwalter
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - William A See
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Urological Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Biomedical Engineering, Medical College of Wisconsin and Marquette University, Milwaukee, Wisconsin.
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Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study. Sci Rep 2018; 8:6108. [PMID: 29666413 PMCID: PMC5904150 DOI: 10.1038/s41598-018-24438-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 03/07/2018] [Indexed: 12/27/2022] Open
Abstract
We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.
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Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ. Evaluation of Glioblastoma Response to Therapy With Chemical Exchange Saturation Transfer. Int J Radiat Oncol Biol Phys 2018; 101:713-723. [PMID: 29893279 DOI: 10.1016/j.ijrobp.2018.03.057] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/02/2018] [Accepted: 03/27/2018] [Indexed: 11/25/2022]
Abstract
PURPOSE To monitor cellular and metabolic characteristics of glioblastoma (GBM) over the course of standard 6-week chemoradiation treatment with chemical exchange saturation transfer (CEST)-MRI; and to identify the earliest time point CEST could determine subsequent therapeutic response. METHODS AND MATERIALS Nineteen patients with newly diagnosed GBM were recruited, and CEST-MRI was acquired immediately before (Day0), 2 weeks (Day14) and 4 weeks (Day28) into treatment, and 1 month after the end of treatment (Day70). Several CEST metrics, including magnetization transfer ratio and area under the curve of CEST peaks corresponding to nuclear Overhauser effect (NOE) and amide protons (MTRNOE, MTRAmide, CESTNOE, and CESTAmide respectively), magnetization transfer (MT), and direct water effect were investigated. Lack of early progression was determined as no increase in tumor size or worsening of clinical symptoms according to routine post-chemoradiation serial structural MRI. RESULTS Changes in MTRNOE (nonprogressors = 1.35 ± 0.18, progressors = 0.97 ± 0.22, P = .006) and MTRAmide (nonprogressors = 1.25 ± 0.17, progressors = 0.99 ± 0.10, P = .017) between baseline (Day0) and Day14 resulted in the best separation of nonprogressors from progressors. Moreover, the baseline (Day0) MTRNOE (nonprogressors = 6.5% ± 1.6%, progressors = 9.1% ± 2.1%, P = .015), MTRAmide (nonprogressors = 6.7% ± 1.7%, progressors = 8.9% ± 1.9%, P = .028), MT (nonprogressors = 3.8% ± 0.9%, progressors = 5.4% ± 1.4%, P = .019), and CESTNOE (nonprogressors = 4.1%ċHz ± 1.7%ċHz, progressors = 6.1%ċHz ± 1.9%ċHz, P = .044) were able to identify progressors even before the start of the treatment. CONCLUSIONS Chemical exchange saturation transfer (CEST) provides imaging-based biomarkers of GBM response as early as 2 weeks into the treatment. Certain CEST metrics can characterize tumor aggressiveness and identify early progressors even before beginning the treatment. Such an early biomarker of response may allow for adjusting the GBM treatment plan for adaptive radiation therapy in early progressors and more confidently continuing standard adjuvant treatment for nonprogressors.
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Affiliation(s)
- Hatef Mehrabian
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
| | - Sten Myrehaug
- Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
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Hormuth DA, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE. Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys 2018; 100:1270-1279. [PMID: 29398129 PMCID: PMC5934308 DOI: 10.1016/j.ijrobp.2017.12.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/17/2017] [Accepted: 12/03/2017] [Indexed: 02/05/2023]
Abstract
PURPOSE To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy. METHODS AND MATERIALS Post-radiation therapy response is modeled using a cell death model (Md), a reduced proliferation rate model (Mp), and cell death and reduced proliferation model (Mdp). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number. RESULTS For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the Mdp and Mp models compared with the Md model. The Mdp model fit, however, had significantly lower sum squared error compared with the Mp and Md models. CONCLUSIONS The results of this study indicate that for both doses, the Mp and Mdp models result in accurate predictions of tumor growth, whereas the Md model poorly describes response to radiation therapy.
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Affiliation(s)
- David A Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas.
| | - Jared A Weis
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina; Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Stephanie L Barnes
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee; Department of Neurological Surgery, Vanderbilt University, Nashville, Tennessee
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas; Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas; Department of Internal Medicine, The University of Texas at Austin, Austin, Texas
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Thust SC, Heiland S, Falini A, Jäger HR, Waldman AD, Sundgren PC, Godi C, Katsaros VK, Ramos A, Bargallo N, Vernooij MW, Yousry T, Bendszus M, Smits M. Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice. Eur Radiol 2018. [PMID: 29536240 PMCID: PMC6028837 DOI: 10.1007/s00330-018-5314-5] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Objectives At a European Society of Neuroradiology (ESNR) Annual Meeting 2015 workshop, commonalities in practice, current controversies and technical hurdles in glioma MRI were discussed. We aimed to formulate guidance on MRI of glioma and determine its feasibility, by seeking information on glioma imaging practices from the European Neuroradiology community. Methods Invitations to a structured survey were emailed to ESNR members (n=1,662) and associates (n=6,400), European national radiologists’ societies and distributed via social media. Results Responses were received from 220 institutions (59% academic). Conventional imaging protocols generally include T2w, T2-FLAIR, DWI, and pre- and post-contrast T1w. Perfusion MRI is used widely (85.5%), while spectroscopy seems reserved for specific indications. Reasons for omitting advanced imaging modalities include lack of facility/software, time constraints and no requests. Early postoperative MRI is routinely carried out by 74% within 24–72 h, but only 17% report a percent measure of resection. For follow-up, most sites (60%) issue qualitative reports, while 27% report an assessment according to the RANO criteria. A minority of sites use a reporting template (23%). Conclusion Clinical best practice recommendations for glioma imaging assessment are proposed and the current role of advanced MRI modalities in routine use is addressed. Key Points • We recommend the EORTC-NBTS protocol as the clinical standard glioma protocol. • Perfusion MRI is recommended for diagnosis and follow-up of glioma. • Use of advanced imaging could be promoted with increased education activities. • Most response assessment is currently performed qualitatively. • Reporting templates are not widely used, and could facilitate standardisation. Electronic supplementary material The online version of this article (10.1007/s00330-018-5314-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- S C Thust
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, London, UK
- Imaging Department, University College London Hospital, London, UK
| | - S Heiland
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - A Falini
- Department of Neuroradiology, San Raffaele Scientific Institute, Milan, Italy
| | - H R Jäger
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, London, UK
- Imaging Department, University College London Hospital, London, UK
| | - A D Waldman
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK
| | - P C Sundgren
- Institution for Clinical Sciences/Radiology, Lund University, Lund, Sweden
- Centre for Imaging and Physiology, Skåne University hospital, Lund, Sweden
| | - C Godi
- Department of Neuroradiology, San Raffaele Scientific Institute, Milan, Italy
| | - V K Katsaros
- General Anti-Cancer and Oncological Hospital "Agios Savvas", Athens, Greece
- Central Clinic of Athens, Athens, Greece
- University of Athens, Athens, Greece
| | - A Ramos
- Hospital 12 de Octubre, Madrid, Spain
| | - N Bargallo
- Image Diagnostic Centre, Hospital Clinic de Barcelona, Barcelona, Spain
- Magnetic Resonance Core Facility, Institut per la Recerca Biomedica August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - M W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - T Yousry
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
| | - M Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - M Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
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Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ. Quantitative Magnetization Transfer in Monitoring Glioblastoma (GBM) Response to Therapy. Sci Rep 2018; 8:2475. [PMID: 29410469 PMCID: PMC5802834 DOI: 10.1038/s41598-018-20624-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 01/22/2018] [Indexed: 11/09/2022] Open
Abstract
Quantitative magnetization transfer (qMT) was used as a biomarker to monitor glioblastoma (GBM) response to chemo-radiation and identify the earliest time-point qMT could differentiate progressors from non-progressors. Nineteen GBM patients were recruited and MRI-scanned before (Day0), two weeks (Day14), and four weeks (Day28) into the treatment, and one month after the end of the treatment (Day70). Comprehensive qMT data was acquired, and a two-pool MT model was fit to the data. Response was determined at 3-8 months following the end of chemo-radiation. The amount of magnetization transfer ([Formula: see text]) was significantly lower in GBM compared to normal appearing white matter (p < 0.001). Statistically significant difference was observed in [Formula: see text] at Day0 between non-progressors (1.06 ± 0.24) and progressors (1.64 ± 0.48), with p = 0.006. Changes in several qMT parameters between Day14 and Day0 were able to differentiate the two cohorts with [Formula: see text] providing the best separation (relative [Formula: see text] = 1.34 ± 0.21, relative [Formula: see text] = 1.07 ± 0.08, p = 0.031). Thus, qMT characteristics of GBM are more sensitive to treatment effects compared to clinically used metrics. qMT could assess tumor aggressiveness and identify early progressors even before the treatment. Changes in qMT parameters within the first 14 days of the treatment were capable of separating early progressors from non-progressors, making qMT a promising biomarker to guide adaptive radiotherapy for GBM.
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Affiliation(s)
- Hatef Mehrabian
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
| | - Sten Myrehaug
- Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Neurosurgery and Pediatric Neurosurgery, Medical University, Lublin, Poland
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Pak RW, Hadjiabadi DH, Senarathna J, Agarwal S, Thakor NV, Pillai JJ, Pathak AP. Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors. J Cereb Blood Flow Metab 2017; 37:3475-3487. [PMID: 28492341 PMCID: PMC5669348 DOI: 10.1177/0271678x17707398] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Functional magnetic resonance imaging (fMRI) serves as a critical tool for presurgical mapping of eloquent cortex and changes in neurological function in patients diagnosed with brain tumors. However, the blood-oxygen-level-dependent (BOLD) contrast mechanism underlying fMRI assumes that neurovascular coupling remains intact during brain tumor progression, and that measured changes in cerebral blood flow (CBF) are correlated with neuronal function. Recent preclinical and clinical studies have demonstrated that even low-grade brain tumors can exhibit neurovascular uncoupling (NVU), which can confound interpretation of fMRI data. Therefore, to avoid neurosurgical complications, it is crucial to understand the biophysical basis of NVU and its impact on fMRI. Here we review the physiology of the neurovascular unit, how it is remodeled, and functionally altered by brain cancer cells. We first discuss the latest findings about the components of the neurovascular unit. Next, we synthesize results from preclinical and clinical studies to illustrate how brain tumor induced NVU affects fMRI data interpretation. We examine advances in functional imaging methods that permit the clinical evaluation of brain tumors with NVU. Finally, we discuss how the suppression of anomalous tumor blood vessel formation with antiangiogenic therapies can "normalize" the brain tumor vasculature, and potentially restore neurovascular coupling.
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Affiliation(s)
- Rebecca W Pak
- 1 Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, USA
| | - Darian H Hadjiabadi
- 1 Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, USA
| | - Janaka Senarathna
- 1 Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, USA
| | - Shruti Agarwal
- 2 Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, USA
| | - Nitish V Thakor
- 1 Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, USA
| | - Jay J Pillai
- 2 Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, USA
| | - Arvind P Pathak
- 1 Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, USA.,2 Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, USA.,3 Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, USA
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Hormuth DA, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V, Yankeelov TE. A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth. J R Soc Interface 2017; 14:rsif.2016.1010. [PMID: 28330985 DOI: 10.1098/rsif.2016.1010] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 02/24/2017] [Indexed: 12/18/2022] Open
Abstract
While gliomas have been extensively modelled with a reaction-diffusion (RD) type equation it is most likely an oversimplification. In this study, three mathematical models of glioma growth are developed and systematically investigated to establish a framework for accurate prediction of changes in tumour volume as well as intra-tumoural heterogeneity. Tumour cell movement was described by coupling movement to tissue stress, leading to a mechanically coupled (MC) RD model. Intra-tumour heterogeneity was described by including a voxel-specific carrying capacity (CC) to the RD model. The MC and CC models were also combined in a third model. To evaluate these models, rats (n = 14) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging over 10 days to estimate tumour cellularity. Model parameters were estimated from the first three imaging time points and then used to predict tumour growth at the remaining time points which were then directly compared to experimental data. The results in this work demonstrate that mechanical-biological effects are a necessary component of brain tissue tumour modelling efforts. The results are suggestive that a variable tissue carrying capacity is a needed model component to capture tumour heterogeneity. Lastly, the results advocate the need for additional effort towards capturing tumour-to-tissue infiltration.
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Affiliation(s)
- David A Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Stephanie L Barnes
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA.,Department of Neurological Surgery, Vanderbilt University, Nashville, TN, USA
| | - Erin C Rericha
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA
| | - Vito Quaranta
- Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA .,Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.,Internal Medicine, The University of Texas at Austin, Austin, TX, USA
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Hurrell SL, McGarry SD, Kaczmarowski A, Iczkowski KA, Jacobsohn K, Hohenwalter MD, Hall WA, See WA, Banerjee A, Charles DK, Nevalainen MT, Mackinnon AC, LaViolette PS. Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging. J Med Imaging (Bellingham) 2017; 5:011004. [PMID: 29098169 PMCID: PMC5658575 DOI: 10.1117/1.jmi.5.1.011004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 09/21/2017] [Indexed: 01/21/2023] Open
Abstract
Multiparametric magnetic resonance imaging (MP-MRI), including diffusion-weighted imaging, is commonly used to diagnose prostate cancer. This radiology–pathology study correlates prostate cancer grade and morphology with common b-value combinations for calculating apparent diffusion coefficient (ADC). Thirty-nine patients undergoing radical prostatectomy were recruited for MP-MRI prior to surgery. Diffusion imaging was collected with seven b-values, and ADC was calculated. Excised prostates were sliced in the same orientation as the MRI using 3-D printed slicing jigs. Whole-mount slides were digitized and annotated by a pathologist. Annotated samples were aligned to the MRI, and ADC values were extracted from annotated peripheral zone (PZ) regions. A receiver operating characteristic (ROC) analysis was performed to determine accuracy of tissue type discrimination and optimal ADC b-value combination. ADC significantly discriminates Gleason (G) G4-5 cancer from G3 and other prostate tissue types. The optimal b-values for discriminating high from low-grade and noncancerous tissue in the PZ are 50 and 2000, followed closely by 100 to 2000 and 0 to 2000. Optimal ADC cut-offs are presented for dichotomized discrimination of tissue types according to each b-value combination. Selection of b-values affects the sensitivity and specificity of ADC for discrimination of prostate cancer.
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Affiliation(s)
- Sarah L Hurrell
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Sean D McGarry
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - Amy Kaczmarowski
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth A Iczkowski
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Kenneth Jacobsohn
- Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Mark D Hohenwalter
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - William A Hall
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin, United States
| | - William A See
- Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Anjishnu Banerjee
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - David K Charles
- Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Marja T Nevalainen
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Pharmacology and Toxicology, Milwaukee, Wisconsin, United States
| | - Alexander C Mackinnon
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States
| | - Peter S LaViolette
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, Wisconsin, United States
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37
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Lee Y, Lee SS, Cheong H, Lee CK, Kim N, Son WC, Hong SM. Intravoxel incoherent motion MRI for monitoring the therapeutic response of hepatocellular carcinoma to sorafenib treatment in mouse xenograft tumor models. Acta Radiol 2017; 58:1045-1053. [PMID: 28273738 DOI: 10.1177/0284185116683576] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background With the introduction of targeted therapies, there has been a growing need for non-invasive imaging methods which accurately evaluate therapeutic effects and overcome the limitations of tumor size-based therapeutic response assessments. Purpose To assess diagnostic values of intra-voxel incoherent motion (IVIM) imaging in evaluating therapeutic effects of sorafenib on hepatocellular carcinoma (HCC) using mouse xenograft model. Material and Methods Twenty-four mice bearing Huh-7 were divided into a control group and two treatment groups received sorafenib doses of 5 mg/kg (5 mg-Tx) or 30 mg/kg (30 mg-Tx). IVIM imaging was performed using 10 b-values (0-900 s/mm2). The apparent diffusion coefficient (ADC), diffusion coefficient ( D), and perfusion fraction ( f) were measured for whole tumors and tumor periphery. Changes between baseline and post-treatment parameters ( Δ ADC, Δ D, and Δ f) were calculated, and these parameters were compared with microvessel density (MVD) and area of tumor cell death. Results The post-treatment f and Δ f for tumor periphery were significantly higher in control group, followed by 5 mg-Tx and 30 mg-Tx ( P < 0.001). MVD showed significant positive correlation with post-treatment f ( r = 0.584, P = 0.003) and negative correlation with D ( r = -0.495, P = 0.014) for tumor periphery, while no parameter showed significant correlation with area of tumor cell death. Conclusion The f is significantly correlated with MVD of HCC, and could potentially be used to evaluate the anti-angiogenic effects of sorafenib.
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Affiliation(s)
- Yedaun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
- Current address: Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Hyunhee Cheong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Chang Kyung Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Woo-Chan Son
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Seung Mo Hong
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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Taylor EN, Ding Y, Zhu S, Cheah E, Alexander P, Lin L, Aninwene GE, Hoffman MP, Mahajan A, Mohamed AS, McDannold N, Fuller CD, Chen CC, Gilbert RJ. Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma. Oncotarget 2017; 8:41815-41826. [PMID: 28404971 PMCID: PMC5522030 DOI: 10.18632/oncotarget.16296] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 01/24/2017] [Indexed: 01/22/2023] Open
Abstract
While it is recognized that the overall resistance of glioblastoma to treatment may be related to intra-tumor patterns of structural heterogeneity, imaging methods to assess such patterns remain rudimentary. METHODS We utilized a generalized Q-space imaging (GQI) algorithm to analyze magnetic resonance imaging (MRI) derived from a rodent model of glioblastoma and 2 clinical datasets to correlate GQI, histology, and survival. RESULTS In a rodent glioblastoma model, GQI demonstrated a poorly coherent core region, consisting of diffusion tracts <5 mm, surrounded by a shell of highly coherent diffusion tracts, 6-25 mm. Histologically, the core region possessed a high degree of necrosis, whereas the shell consisted of organized sheets of anaplastic cells with elevated mitotic index. These attributes define tumor architecture as the macroscopic organization of variably aligned tumor cells. Applied to MRI data from The Cancer Imaging Atlas (TCGA), the core-shell diffusion tract-length ratio (c/s ratio) correlated linearly with necrosis, which, in turn, was inversely associated with survival (p = 0.00002). We confirmed in an independent cohort of patients (n = 62) that the c/s ratio correlated inversely with survival (p = 0.0004). CONCLUSIONS The analysis of MR images by GQI affords insight into tumor architectural patterns in glioblastoma that correlate with biological heterogeneity and clinical outcome.
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Affiliation(s)
- Erik N. Taylor
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Yao Ding
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Shan Zhu
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Eric Cheah
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Phillip Alexander
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Leon Lin
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - George E. Aninwene
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Matthew P. Hoffman
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Anita Mahajan
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Abdallah S.R. Mohamed
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Nathan McDannold
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Clifton D. Fuller
- Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Clark C. Chen
- Center for Theoretical and Applied Neuro-Oncology and Department of Neurosurgery, University of California, San Diego, CA, USA
| | - Richard J. Gilbert
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
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Bowden SG, Neira JA, Gill BJA, Ung TH, Englander ZK, Zanazzi G, Chang PD, Samanamud J, Grinband J, Sheth SA, McKhann GM, Sisti MB, Canoll P, D’Amico RS, Bruce JN. Sodium Fluorescein Facilitates Guided Sampling of Diagnostic Tumor Tissue in Nonenhancing Gliomas. Neurosurgery 2017. [DOI: 10.1093/neuros/nyx271] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
BACKGROUND
Accurate tissue sampling in nonenhancing (NE) gliomas is a unique surgical challenge due to their intratumoral histological heterogeneity and absence of contrast enhancement as a guide for intraoperative stereotactic guidance. Instead, T2/fluid-attenuated inversion-recovery (FLAIR) hyperintensity on MRI is commonly used as an imaging surrogate for pathological tissue, but sampling from this region can yield nondiagnostic or underdiagnostic brain tissue. Sodium fluorescein is an intraoperative fluorescent dye that has a high predictive value for tumor identification in areas of contrast enhancement and NE in glioblastomas. However, the underlying histopathological alterations in fluorescent regions of NE gliomas remain undefined.
OBJECTIVE
To evaluate whether fluorescein can identify diagnostic tissue and differentiate regions with higher malignant potential during surgery for NE gliomas, thus improving sampling accuracy.
METHODS
Thirteen patients who presented with NE, T2/FLAIR hyperintense lesions suspicious for glioma received fluorescein (10%, 3 mg/kg intravenously) during surgical resection.
RESULTS
Patchy fluorescence was identified within the T2/FLAIR hyperintense area in 10 of 13 (77%) patients. Samples taken from fluorescent regions were more likely to demonstrate diagnostic glioma tissue and cytologic atypia (P < .05). Fluorescein demonstrated a 95% positive predictive value for the presence of diagnostic tissue. Samples from areas of fluorescence also demonstrated greater total cell density and higher Ki-67 labeling than nonfluorescent biopsies (P < .05).
CONCLUSION
Fluorescence in NE gliomas is highly predictive of diagnostic tumor tissue and regions of higher cell density and proliferative activity.
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Affiliation(s)
- Stephen G Bowden
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Justin A Neira
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Brian J A Gill
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Timothy H Ung
- Department of Neurological Surgery, University of Colorado, Aurora, Colorado
| | - Zachary K Englander
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - George Zanazzi
- Department of Pathology and Cell Biology, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Peter D Chang
- Department of Radiology, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Jorge Samanamud
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Jack Grinband
- Department of Radiology, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Sameer A Sheth
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Guy M McKhann
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Michael B Sisti
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Peter Canoll
- Department of Pathology and Cell Biology, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Randy S D’Amico
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
| | - Jeffrey N Bruce
- Department of Neurological Surgery, College of Physicians and Surgeons at Columbia University, New York, New York
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Abstract
OPINION STATEMENT With advances in treatments and survival of patients with glioblastoma (GBM), it has become apparent that conventional imaging sequences have significant limitations both in terms of assessing response to treatment and monitoring disease progression. Both 'pseudoprogression' after chemoradiation for newly diagnosed GBM and 'pseudoresponse' after anti-angiogenesis treatment for relapsed GBM are well-recognised radiological entities. This in turn has led to revision of response criteria away from the standard MacDonald criteria, which depend on the two-dimensional measurement of contrast-enhancing tumour, and which have been the primary measure of radiological response for over three decades. A working party of experts published RANO (Response Assessment in Neuro-oncology Working Group) criteria in 2010 which take into account signal change on T2/FLAIR sequences as well as the contrast-enhancing component of the tumour. These have recently been modified for immune therapies, which are associated with specific issues related to the timing of radiological response. There has been increasing interest in quantification and validation of physiological and metabolic parameters in GBM over the last 10 years utilising the wide range of advanced imaging techniques available on standard MRI platforms. Previously, MRI would provide structural information only on the anatomical location of the tumour and the presence or absence of a disrupted blood-brain barrier. Advanced MRI sequences include proton magnetic resonance spectroscopy (MRS), vascular imaging (perfusion/permeability) and diffusion imaging (diffusion weighted imaging/diffusion tensor imaging) and are now routinely available. They provide biologically relevant functional, haemodynamic, cellular, metabolic and cytoarchitectural information and are being evaluated in clinical trials to determine whether they offer superior biomarkers of early treatment response than conventional imaging, when correlated with hard survival endpoints. Multiparametric imaging, incorporating different combinations of these modalities, improves accuracy over single imaging modalities but has not been widely adopted due to the amount of post-processing analysis required, lack of clinical trial data, lack of radiology training and wide variations in threshold values. New techniques including diffusion kurtosis and radiomics will offer a higher level of quantification but will require validation in clinical trial settings. Given all these considerations, it is clear that there is an urgent need to incorporate advanced techniques into clinical trial design to avoid the problems of under or over assessment of treatment response.
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Apparent diffusion coefficient changes predict survival after intra-arterial bevacizumab treatment in recurrent glioblastoma. Neuroradiology 2017; 59:499-505. [PMID: 28343250 DOI: 10.1007/s00234-017-1820-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Accepted: 03/14/2017] [Indexed: 10/19/2022]
Abstract
PURPOSE Superselective intra-arterial cerebral infusion (SIACI) of bevacizumab (BV) has emerged as a novel therapy in the treatment of recurrent glioblastoma (GB). This study assessed the use of apparent diffusion coefficient (ADC) in predicting length of survival after SIACI BV and overall survival in patients with recurrent GB. METHODS Sixty-five patients from a cohort enrolled in a phase I/II trial of SIACI BV for treatment of recurrent GB were retrospectively included in this analysis. MR imaging with a diffusion-weighted (DWI) sequence was performed before and after treatment. ROIs were manually delineated on ADC maps corresponding to the enhancing and non-enhancing portions of the tumor. Cox and logistic regression analyses were performed to determine which ADC values best predicted survival. RESULTS The change in minimum ADC in the enhancing portion of the tumor after SIACI BV therapy was associated with an increased risk of death (hazard ratio = 2.0, 95% confidence interval(CI) [1.04-3.79], p = 0.038), adjusting for age, tumor size, BV dose, and prior IV BV treatments. Similarly, the change in ADC after SIACI BV therapy was associated with greater likelihood of surviving less than 1 year after therapy (odds ratio = 7.0, 95% CI [1.08-45.7], p = 0.04). Having previously received IV BV was associated with increased risk of death (OR 18, 95% CI [1.8-180.0], p = 0.014). CONCLUSION In patients with recurrent GB treated with SIACI BV, the change in ADC value after treatment is predictive of overall survival.
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42
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Chang PD, Malone HR, Bowden SG, Chow DS, Gill BJA, Ung TH, Samanamud J, Englander ZK, Sonabend AM, Sheth SA, McKhann GM, Sisti MB, Schwartz LH, Lignelli A, Grinband J, Bruce JN, Canoll P. A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies. AJNR Am J Neuroradiol 2017; 38:890-898. [PMID: 28255030 DOI: 10.3174/ajnr.a5112] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 12/09/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The complex MR imaging appearance of glioblastoma is a function of underlying histopathologic heterogeneity. A better understanding of these correlations, particularly the influence of infiltrating glioma cells and vasogenic edema on T2 and diffusivity signal in nonenhancing areas, has important implications in the management of these patients. With localized biopsies, the objective of this study was to generate a model capable of predicting cellularity at each voxel within an entire tumor volume as a function of signal intensity, thus providing a means of quantifying tumor infiltration into surrounding brain tissue. MATERIALS AND METHODS Ninety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. Signal intensity was plotted against cell density for each MR image. RESULTS T2-FLAIR (r = -0.61) and ADC (r = -0.63) sequences were inversely correlated with cell density. T1-postcontrast (r = 0.69) subtraction was directly correlated with cell density. Combining these relationships yielded a multiparametric model with improved correlation (r = 0.74), suggesting that each sequence offers different and complementary information. CONCLUSIONS Using localized biopsies, we have generated a model that illustrates a quantitative and significant relationship between MR signal and cell density. Projecting this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity in both the contrast-enhancing tumor core and nonenhancing margins of glioblastoma and may be used to guide extended surgical resection, localized biopsies, and radiation field mapping.
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Affiliation(s)
- P D Chang
- From the Departments of Radiology (P.D.C., L.H.S., A.L., J.G.)
| | - H R Malone
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.).,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - S G Bowden
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.).,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - D S Chow
- Department of Radiology (D.S.C.), University of San Francisco School of Medicine, San Francisco, California
| | - B J A Gill
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.).,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - T H Ung
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.).,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - J Samanamud
- Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - Z K Englander
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.).,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - A M Sonabend
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.).,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - S A Sheth
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.)
| | - G M McKhann
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.)
| | - M B Sisti
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.)
| | - L H Schwartz
- From the Departments of Radiology (P.D.C., L.H.S., A.L., J.G.)
| | - A Lignelli
- From the Departments of Radiology (P.D.C., L.H.S., A.L., J.G.)
| | - J Grinband
- From the Departments of Radiology (P.D.C., L.H.S., A.L., J.G.)
| | - J N Bruce
- Neurological Surgery (H.R.M., S.G.B., B.J.A.G., T.H.U., Z.K.E., A.M.S., S.A.S., G.M.M., M.B.S., J.N.B.) .,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
| | - P Canoll
- Pathology and Cell Biology (P.C.), College of Physicians and Surgeons at Columbia University, New York, New York .,Gabriele Bartoli Brain Tumor Laboratory and the Irving Cancer Research Center (H.R.M., S.G.B., B.J.A.G., T.H.U., J.S., Z.K.E., A.M.S., J.N.B., P.C.), New York, New York
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Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 2017; 59:135-145. [PMID: 28070598 DOI: 10.1007/s00234-016-1769-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 11/18/2016] [Indexed: 12/22/2022]
Abstract
INTRODUCTION We describe the imaging findings encountered in GBM patients receiving immune checkpoint blockade and assess the potential of quantitative MRI biomarkers to differentiate patients who derive therapeutic benefit from those who do not. METHODS A retrospective analysis was performed on longitudinal MRIs obtained on recurrent GBM patients enrolled on clinical trials. Among 10 patients with analyzable data, bidirectional diameters were measured on contrast enhanced T1 (pGd-T1WI) and volumes of interest (VOI) representing measurable abnormality suggestive of tumor were selected on pGdT1WI (pGdT1 VOI), FLAIR-T2WI (FLAIR VOI), and ADC maps. Intermediate ADC (IADC) VOI represented voxels within the FLAIR VOI having ADC in the range of highly cellular tumor (0.7-1.1 × 10-3 mm2/s) (IADC VOI). Therapeutic benefit was determined by tissue pathology and survival on trial. IADC VOI, pGdT1 VOI, FLAIR VOI, and RANO assessment results were correlated with patient benefit. RESULTS Five patients were deemed to have received therapeutic benefit and the other five patients did not. The average time on trial for the benefit group was 194 days, as compared to 81 days for the no benefit group. IADC VOI correlated well with the presence or absence of clinical benefit in 10 patients. Furthermore, pGd VOI, FLAIR VOI, and RANO assessment correlated less well with response. CONCLUSION MRI reveals an initial increase in volumes of abnormal tissue with contrast enhancement, edema, and intermediate ADC suggesting hypercellularity within the first 0-6 months of immunotherapy. Subsequent stabilization and improvement in IADC VOI appear to better predict ultimate therapeutic benefit from these agents than conventional imaging.
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Nguyen HS, Milbach N, Hurrell SL, Cochran E, Connelly J, Bovi JA, Schultz CJ, Mueller WM, Rand SD, Schmainda KM, LaViolette PS. Progressing Bevacizumab-Induced Diffusion Restriction Is Associated with Coagulative Necrosis Surrounded by Viable Tumor and Decreased Overall Survival in Patients with Recurrent Glioblastoma. AJNR Am J Neuroradiol 2016; 37:2201-2208. [PMID: 27492073 DOI: 10.3174/ajnr.a4898] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 06/23/2016] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND PURPOSE Patients with recurrent glioblastoma often exhibit regions of diffusion restriction following the initiation of bevacizumab therapy. Studies suggest that these regions represent either diffusion-restricted necrosis or hypercellular tumor. This study explored postmortem brain specimens and a population analysis of overall survival to determine the identity and implications of such lesions. MATERIALS AND METHODS Postmortem examinations were performed on 6 patients with recurrent glioblastoma on bevacizumab with progressively growing regions of diffusion restriction. ADC values were extracted from regions of both hypercellular tumor and necrosis. A receiver operating characteristic analysis was performed to define optimal ADC thresholds for differentiating tissue types. A retrospective population study was also performed comparing the overall survival of 64 patients with recurrent glioblastoma treated with bevacizumab. Patients were separated into 3 groups: no diffusion restriction, diffusion restriction that appeared and progressed within 5 months of bevacizumab initiation, and delayed or stable diffusion restriction. An additional analysis was performed assessing tumor O6-methylguanine-DNA-methyltransferase methylation. RESULTS The optimal ADC threshold for differentiation of hypercellularity and necrosis was 0.736 × 10-3mm2/s. Progressively expanding diffusion restriction was pathologically confirmed to be coagulative necrosis surrounded by viable tumor. Progressive lesions were associated with the worst overall survival, while stable lesions showed the greatest overall survival (P < .05). Of the 40% of patients with O6-methylguanine-DNA-methyltransferase methylated tumors, none developed diffusion-restricted lesions. CONCLUSIONS Progressive diffusion-restricted lesions were pathologically confirmed to be coagulative necrosis surrounded by viable tumor and associated with decreased overall survival. Stable lesions were, however, associated with increased overall survival. All lesions were associated with O6-methylguanine-DNA-methyltransferase unmethylated tumors.
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Affiliation(s)
- H S Nguyen
- From the Departments of Neurosurgery (H.S.N., W.M.M.)
| | - N Milbach
- Radiology (N.M., S.L.H., S.D.R., K.M.S., P.S.L.)
| | - S L Hurrell
- Radiology (N.M., S.L.H., S.D.R., K.M.S., P.S.L.)
| | | | | | - J A Bovi
- Radiation Oncology (J.A.B., C.J.S.)
| | | | - W M Mueller
- From the Departments of Neurosurgery (H.S.N., W.M.M.)
| | - S D Rand
- Radiology (N.M., S.L.H., S.D.R., K.M.S., P.S.L.)
| | - K M Schmainda
- Radiology (N.M., S.L.H., S.D.R., K.M.S., P.S.L.)
- Biophysics (K.M.S., P.S.L.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - P S LaViolette
- Radiology (N.M., S.L.H., S.D.R., K.M.S., P.S.L.)
- Biophysics (K.M.S., P.S.L.), Medical College of Wisconsin, Milwaukee, Wisconsin
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45
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McGarry SD, Hurrell SL, Kaczmarowski AL, Cochran EJ, Connelly J, Rand SD, Schmainda KM, LaViolette PS. Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. ACTA ACUST UNITED AC 2016; 2:223-228. [PMID: 27774518 PMCID: PMC5074084 DOI: 10.18383/j.tom.2016.00250] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Magnetic resonance imaging (MRI) is used to diagnose and monitor brain tumors. Extracting additional information from medical imaging and relating it to a clinical variable of interest is broadly defined as radiomics. Here, multiparametric MRI radiomic profiles (RPs) of de novo glioblastoma (GBM) brain tumors is related with patient prognosis. Clinical imaging from 81 patients with GBM before surgery was analyzed. Four MRI contrasts were aligned, masked by margins defined by gadolinium contrast enhancement and T2/fluid attenuated inversion recovery hyperintensity, and contoured based on image intensity. These segmentations were combined for visualization and quantification by assigning a 4-digit numerical code to each voxel to indicate the segmented RP. Each RP volume was then compared with overall survival. A combined classifier was then generated on the basis of significant RPs and optimized volume thresholds. Five RPs were predictive of overall survival before therapy. Combining the RP classifiers into a single prognostic score predicted patient survival better than each alone (P < .005). Voxels coded with 1 RP associated with poor prognosis were pathologically confirmed to contain hypercellular tumor. This study applies radiomic profiling to de novo patients with GBM to determine imaging signatures associated with poor prognosis at tumor diagnosis. This tool may be useful for planning surgical resection or radiation treatment margins.
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Affiliation(s)
- Sean D McGarry
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sarah L Hurrell
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Amy L Kaczmarowski
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - Jennifer Connelly
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Scott D Rand
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kathleen M Schmainda
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
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Kim M, Kim HS. Emerging Techniques in Brain Tumor Imaging: What Radiologists Need to Know. Korean J Radiol 2016; 17:598-619. [PMID: 27587949 PMCID: PMC5007387 DOI: 10.3348/kjr.2016.17.5.598] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/03/2016] [Indexed: 12/11/2022] Open
Abstract
Among the currently available brain tumor imaging, advanced MR imaging techniques, such as diffusion-weighted MR imaging and perfusion MR imaging, have been used for solving diagnostic challenges associated with conventional imaging and for monitoring the brain tumor treatment response. Further development of advanced MR imaging techniques and postprocessing methods may contribute to predicting the treatment response to a specific therapeutic regimen, particularly using multi-modality and multiparametric imaging. Over the next few years, new imaging techniques, such as amide proton transfer imaging, will be studied regarding their potential use in quantitative brain tumor imaging. In this review, the pathophysiologic considerations and clinical validations of these promising techniques are discussed in the context of brain tumor characterization and treatment response.
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Affiliation(s)
- Minjae Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea
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Eidel O, Neumann JO, Burth S, Kieslich PJ, Jungk C, Sahm F, Kickingereder P, Kiening K, Unterberg A, Wick W, Schlemmer HP, Bendszus M, Radbruch A. Automatic Analysis of Cellularity in Glioblastoma and Correlation with ADC Using Trajectory Analysis and Automatic Nuclei Counting. PLoS One 2016; 11:e0160250. [PMID: 27467557 PMCID: PMC4965093 DOI: 10.1371/journal.pone.0160250] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 07/15/2016] [Indexed: 11/18/2022] Open
Abstract
Objective Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting. Materials and Methods Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis. Results There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman’s ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman’s ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model. Conclusions Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas.
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Affiliation(s)
- Oliver Eidel
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Jan-Oliver Neumann
- Department of Neurosurgery, Division Stereotactic Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Sina Burth
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Pascal J. Kieslich
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Christine Jungk
- Department of Neurosurgery, Division Stereotactic Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Karl Kiening
- Department of Neurosurgery, Division Stereotactic Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, Division Stereotactic Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Wolfgang Wick
- Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- * E-mail:
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48
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Kinoshita M, Arita H, Okita Y, Kagawa N, Kishima H, Hashimoto N, Tanaka H, Watanabe Y, Shimosegawa E, Hatazawa J, Fujimoto Y, Yoshimine T. Comparison of diffusion tensor imaging and 11C-methionine positron emission tomography for reliable prediction of tumor cell density in gliomas. J Neurosurg 2016; 125:1136-1142. [PMID: 26918477 DOI: 10.3171/2015.11.jns151848] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Diffusion MRI is attracting increasing interest for tissue characterization of gliomas, especially after the introduction of antiangiogenic therapy to treat malignant gliomas. The goal of the current study is to elucidate the actual magnitude of the correlation between diffusion MRI and cell density within the tissue. The obtained results were further extended and compared with metabolic imaging with 11C-methionine (MET) PET. METHODS Ninety-eight tissue samples from 37 patients were stereotactically obtained via an intraoperative neuronavigation system. Diffusion tensor imaging (DTI) and MET PET were performed as routine presurgical imaging studies for these patients. DTI was converted into fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps, and MET PET images were registered to Gd-administered T1-weighted images that were used for navigation. Metrics of FA, ADC, and tumor-to-normal tissue ratio of MET PET along with relative values of FA (rFA) and ADC (rADC) compared with normal-appearing white matter were correlated with cell density of the stereotactically obtained tissues. RESULTS rADC was significantly lower in lesions obtained from Gd-enhancing lesions than from nonenhancing lesions. Although rADC showed a moderate but statistically significant negative correlation with cell density (p = 0.010), MET PET showed a superb positive correlation with cell density (p < 0.0001). On the other hand, rFA showed little correlation with cell density. CONCLUSIONS The presented data validated the use of rADC for estimating the treatment response of gliomas but also caution against overestimating its limited accuracy compared with MET PET.
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Affiliation(s)
- Manabu Kinoshita
- Department of Neurosurgery, Osaka Medical Center for Cancer and Cardiovascular Diseases;,Departments of 2 Neurosurgery
| | | | - Yoshiko Okita
- Department of Neurosurgery, Osaka National Hospital, National Hospital Organization, Osaka, Japan
| | | | | | | | | | | | - Eku Shimosegawa
- Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine; and
| | - Jun Hatazawa
- Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine; and
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49
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Wen Q, Jalilian L, Lupo JM, Li Y, Roy R, Molinaro AM, Chang SM, Prados M, Butowski N, Clarke J, Nelson SJ. Association of Diffusion and Anatomic Imaging Parameters with Survival for Patients with Newly Diagnosed Glioblastoma Participating in Two Different Clinical Trials. Transl Oncol 2015; 8:446-55. [PMID: 26692525 PMCID: PMC4700297 DOI: 10.1016/j.tranon.2015.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 09/14/2015] [Accepted: 10/02/2015] [Indexed: 01/20/2023] Open
Abstract
PURPOSE: To evaluate the time course and association with survival of anatomic lesion volumes and diffusion imaging parameters for patients with newly diagnosed glioblastoma who were treated with radiation and concurrently with either temozolomide and enzastaurin (TMZ+enza cohort) or temozolomide, erlotonib, and bevaciumab (TMZ+erl+bev cohort). MATERIALS AND METHODS: Regions of interest corresponding to the contrast-enhancing and hyperintense lesions on T2-weighted images were generated. Diffusion-weighted images were processed to provide maps of apparent diffusion coefficient, fractional anisotropy, and longitudinal and radial eigenvalues. Histograms of diffusion values were generated and summary statistics calculated. Cox proportional hazards models were employed to assess the association of representative imaging parameters with survival with adjustments for age, Karnofsky performance status, and extent of resection. RESULTS: Although progression-free survival was significantly longer for the TMZ+erl+bev cohort (12.8 vs 7.3 months), there was no significant difference in overall survival between the two populations (17.0 vs 17.8 months). The median contrast-enhancing lesion volumes decreased from 6.3 to 1.9 cm3 from baseline to the postradiotherapy scan for patients in the TMZ+enza cohort and from 2.8 to 0.9cm3 for the TMZ+erl+bev cohort. Changes in the T2 lesion volumes were only significant for the latter cohort (26.5 to 11.9 cm3). The median apparent diffusion coefficient and related diffusion parameters were significantly increased for the TMZ+enza cohort (1054 to 1225 μm2/s). More of the anatomic parameters were associated with survival for the TMZ+enza cohort, whereas more diffusion parameters were associated with survival for the TMZ+erl+bev cohort. CONCLUSION: The early changes in anatomic and diffusion imaging parameters and their association with survival reflected differences in the mechanisms of action of the treatments that were being given. This suggests that integrating diffusion metrics and anatomic lesion volumes into the Response Assessment in Neuro-Oncology criteria would assist in interpreting treatment-induced changes and predicting outcome in patients with newly diagnosed glioblastoma who are receiving such combination treatments.
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Affiliation(s)
- Qiuting Wen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States; UCSF/UCB Joint Graduate Group in Bioengineering, University of California, San Francisco, San Francisco, CA United States
| | - Laleh Jalilian
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States
| | - Yan Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States
| | - Ritu Roy
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA United States
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA United States
| | - Susan M Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Michael Prados
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Nicholas Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Jennifer Clarke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA United States
| | - Sarah J Nelson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA United States; UCSF/UCB Joint Graduate Group in Bioengineering, University of California, San Francisco, San Francisco, CA United States; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA United States.
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50
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Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, Nakaji P, Plasencia J, Ranjbar S, Price SJ, Tran N, Loftus J, Jenkins R, O’Neill BP, Elmquist W, Baxter LC, Gao F, Frakes D, Karis JP, Zwart C, Swanson KR, Sarkaria J, Wu T, Mitchell JR, Li J. Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma. PLoS One 2015; 10:e0141506. [PMID: 26599106 PMCID: PMC4658019 DOI: 10.1371/journal.pone.0141506] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 10/08/2015] [Indexed: 01/14/2023] Open
Abstract
Background Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. Methods We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. Results We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Conclusion Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.
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Affiliation(s)
- Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
- * E-mail:
| | - Shuluo Ning
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Jennifer M. Eschbacher
- Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Nathan Gaw
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Amylou C. Dueck
- Department of Biostatistics, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kris A. Smith
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Peter Nakaji
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Jonathan Plasencia
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Stephen J. Price
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Nhan Tran
- Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States of America
| | - Joseph Loftus
- Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, AZ, United States of America
| | - Robert Jenkins
- Department of Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Brian P. O’Neill
- Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - William Elmquist
- Department of Pharmacology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Leslie C. Baxter
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Fei Gao
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - David Frakes
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - John P. Karis
- Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Christine Zwart
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jann Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - J. Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
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