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Sanvito F, Kryukov I, Yao J, Teraishi A, Raymond C, Gao J, Miller C, Nghiemphu PL, Lai A, Liau LM, Patel K, Everson RG, Eldred BSC, Prins RM, Nathanson DA, Salamon N, Cloughesy TF, Ellingson BM. Advanced imaging characterization of post-chemoradiation glioblastoma stratified by diffusion MRI phenotypes known to predict favorable anti-VEGF response. J Neurooncol 2025:10.1007/s11060-025-05019-8. [PMID: 40227555 DOI: 10.1007/s11060-025-05019-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2025] [Accepted: 03/19/2025] [Indexed: 04/15/2025]
Abstract
PURPOSE Recurrent glioblastomas showing a survival benefit from anti-VEGF agents are known to exhibit a distinct diffusion MRI phenotype. We aim to characterize advanced imaging features of this glioblastoma subset. METHODS MRI scans from 87 patients with IDH-wildtype glioblastoma were analyzed. All patients had completed standard chemoradiation and were anti-VEGF-naïve. Contrast-enhancing tumor segmentations were used to extract: the lowest peak of the double gaussian distribution of apparent diffusion coefficient values (ADCL) calculated from diffusion MRI, relative cerebral blood flow (rCBV) values from perfusion MRI, MTRasym @ 3ppm from pH-weighted amine CEST MRI, quantitative T2 and T2* relaxation times (qT2 and qT2*), T1w subtraction map values, and contrast-enhancing tumor volume. Lesions were categorized as high- or low-ADCL using a cutoff of 1240 µm2/s, according to previous studies. RESULTS High-ADCL lesions showed significantly lower rCBV (1.02 vs. 1.28, p = 0.0057), higher MTRasym @ 3ppm (2.36% vs. 2.10%, p = 0.0043), and higher qT2 (114.8 ms vs. 100.9 ms, p = 0.0094), compared to low-ADCL lesions. No group differences were seen in contrast-enhancing tumor volume, T1w subtraction map values, and qT2*, nor in clinical variables such as sex category, MGMT status, and EGFR status. Finally, no clear group-specific preferential locations were seen. CONCLUSION Post-chemoradiation glioblastomas with a diffusion MRI phenotype that is known to predict a favorable response to anti-VEGF (ADCL ≥1240 µm2/s) have distinct biological features, with different perfusion and metabolic characteristics, and T2 relaxation times.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Irina Kryukov
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ashley Teraishi
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - John Gao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Cole Miller
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kunal Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Blaine S C Eldred
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Robert M Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - David A Nathanson
- Department of Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA.
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Foltyn-Dumitru M, Kessler T, Sahm F, Wick W, Heiland S, Bendszus M, Vollmuth P, Schell M. Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities. Neuro Oncol 2024; 26:1099-1108. [PMID: 38153923 PMCID: PMC11145444 DOI: 10.1093/neuonc/noad259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Indexed: 12/30/2023] Open
Abstract
BACKGROUND While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. METHODS A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. RESULTS Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). CONCLUSIONS Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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Ellingson BM, Hagiwara A, Morris CJ, Cho NS, Oshima S, Sanvito F, Oughourlian TC, Telesca D, Raymond C, Abrey LE, Garcia J, Aftab DT, Hessel C, Minei TR, Harats D, Nathanson DA, Wen PY, Cloughesy TF. Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma. Clin Cancer Res 2023; 29:4186-4195. [PMID: 37540556 PMCID: PMC10592195 DOI: 10.1158/1078-0432.ccr-23-1235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/04/2023] [Accepted: 08/01/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE Antiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy. EXPERIMENTAL DESIGN N = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival. RESULTS Optimized thresholds occurred at g = 0.07 months-1 (phase II: HR = 0.2579, P = 5 × 10-20; phase III: HR = 0.2197, P = 5 × 10-5); d = 0.11 months-1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10-17; HR = 0.2061, P = 2 × 10-5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth. CONCLUSIONS Recurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.
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Affiliation(s)
- Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Neuroscience Interdepartmental PhD Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Connor J. Morris
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nicholas S. Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sonoko Oshima
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Talia C. Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Neuroscience Interdepartmental PhD Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Donatello Telesca
- Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | | | | | | | | | | | - David A. Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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Bai JW, Qiu SQ, Zhang GJ. Molecular and functional imaging in cancer-targeted therapy: current applications and future directions. Signal Transduct Target Ther 2023; 8:89. [PMID: 36849435 PMCID: PMC9971190 DOI: 10.1038/s41392-023-01366-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/19/2023] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.
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Affiliation(s)
- Jing-Wen Bai
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Medical Oncology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
| | - Si-Qi Qiu
- Diagnosis and Treatment Center of Breast Diseases, Clinical Research Center, Shantou Central Hospital, 515041, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Shantou University Medical College, 515041, Shantou, China
| | - Guo-Jun Zhang
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
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Kurokawa R, Baba A, Kurokawa M, Capizzano A, Hassan O, Johnson T, Ota Y, Kim J, Hagiwara A, Moritani T, Srinivasan A. Pretreatment ADC Histogram Analysis as a Prognostic Imaging Biomarker for Patients with Recurrent Glioblastoma Treated with Bevacizumab: A Systematic Review and Meta-analysis. AJNR Am J Neuroradiol 2022; 43:202-206. [PMID: 35058300 PMCID: PMC8985678 DOI: 10.3174/ajnr.a7406] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/15/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND The mean ADC value of the lower Gaussian curve (ADCL) derived from the bi-Gaussian curve-fitting histogram analysis has been reported as a predictive/prognostic imaging biomarker in patients with recurrent glioblastoma treated with bevacizumab; however, its systematic summary has been lacking. PURPOSE We applied a systematic review and meta-analysis to investigate the predictive/prognostic performance of ADCL in patients with recurrent glioblastoma treated with bevacizumab. DATA SOURCES We performed a literature search using PubMed, Scopus, and EMBASE. STUDY SELECTION A total of 1344 abstracts were screened, of which 83 articles were considered potentially relevant. Data were finally extracted from 6 studies including 578 patients. DATA ANALYSIS Forest plots were generated to illustrate the hazard ratios of overall survival and progression-free survival. The heterogeneity across the studies was assessed using the Cochrane Q test and I2 values. DATA SYNTHESIS The pooled hazard ratios for overall survival and progression-free survival in patients with an ADCL lower than the cutoff values were 1.89 (95% CI, 1.53-2.31) and 1.98 (95% CI, 1.54-2.55) with low heterogeneity among the studies. Subgroup analysis of the bevacizumab-free cohort showed a pooled hazard ratio for overall survival of 1.20 (95% CI, 1.08-1.34) with low heterogeneity. LIMITATIONS The conclusions are limited by the difference in the definition of recurrence among the included studies. CONCLUSIONS This systematic review with meta-analysis supports the prognostic value of ADCL in patients with recurrent glioblastoma treated with bevacizumab, with a low ADCL demonstrating decreased overall survival and progression-free survival. On the other hand, the predictive role of ADCL for bevacizumab treatment was not confirmed.
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Affiliation(s)
- R. Kurokawa
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Baba
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - M. Kurokawa
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Capizzano
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - O. Hassan
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - T. Johnson
- Department of Biostatistics (T.J.), University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Y. Ota
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - J. Kim
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Hagiwara
- Department of Radiology (A.H.), Juntendo University School of Medicine, Tokyo, Japan
| | - T. Moritani
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A. Srinivasan
- From the Division of Neuroradiology (R.K., A.B., M.K., A.C., O.H., Y.O., J.K., T.M., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
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Seifert R, Kersting D, Rischpler C, Opitz M, Kirchner J, Pabst KM, Mavroeidi IA, Laschinsky C, Grueneisen J, Schaarschmidt B, Catalano OA, Herrmann K, Umutlu L. Clinical Use of PET/MR in Oncology: An Update. Semin Nucl Med 2021; 52:356-364. [PMID: 34980479 DOI: 10.1053/j.semnuclmed.2021.11.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 11/21/2021] [Accepted: 11/23/2021] [Indexed: 12/30/2022]
Abstract
The combination of PET and MRI is one of the recent advances of hybrid imaging. Yet to date, the adoption rate of PET/MRI systems has been rather slow. This seems to be partially caused by the high costs of PET/MRI systems and the need to verify an incremental benefit over PET/CT or sequential PET/CT and MRI. In analogy to PET/CT, the MRI part of PET/MRI was primarily used for anatomical imaging. Though this can be advantageous, for example in diseases where the superior soft tissue contrast of MRI is highly appreciated, the sole use of MRI for anatomical orientation lessens the potential of PET/MRI. Consequently, more recent studies focused on its multiparametric potential and employed diffusion weighted sequences and other functional imaging sequences in PET/MRI. This integration puts the focus on a more wholesome approach to PET/MR imaging, in terms of releasing its full potential for local primary staging based on multiparametric imaging and an included one-stop shop approach for whole-body staging. This approach as well as the implementation of computational analysis, in terms of radiomics analysis, has been shown valuable in several oncological diseases, as will be discussed in this review article.
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Affiliation(s)
- Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; Department of Nuclear Medicine, University Hospital Münster, Münster, Germany; West German Cancer Center, University Hospital Essen, Essen, Germany.; German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany.
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, University Hospital Essen, Essen, Germany.; German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, University Hospital Essen, Essen, Germany.; German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany
| | - Marcel Opitz
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Julian Kirchner
- Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, Dusseldorf, Germany
| | - Kim M Pabst
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, University Hospital Essen, Essen, Germany.; German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany
| | - Ilektra-Antonia Mavroeidi
- West German Cancer Center, University Hospital Essen, Essen, Germany.; Clinic for Internal Medicine (Tumor Research), University Hospital Essen, Essen, Germany
| | - Christina Laschinsky
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, University Hospital Essen, Essen, Germany.; German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany
| | - Johannes Grueneisen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Benedikt Schaarschmidt
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Onofrio Antonio Catalano
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA; Abdominal Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; West German Cancer Center, University Hospital Essen, Essen, Germany.; German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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Abstract
This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.
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Affiliation(s)
- Raymond Y Huang
- Department of Neuroradiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Whitney B Pope
- Radiology, Section of Neuroradiology, Brain Tumor Imaging, UCLA Medical Center, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA
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8
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Ellingson BM, Patel K, Wang C, Raymond C, Brenner A, de Groot JF, Butowski NA, Zach L, Campian JL, Schlossman J, Rizvi S, Cohen YC, Lowenton-Spier N, Minei TR, Shmueli SF, Wen PY, Cloughesy TF. Validation of diffusion MRI as a biomarker for efficacy using randomized phase III trial of bevacizumab with or without VB-111 in recurrent glioblastoma. Neurooncol Adv 2021; 3:vdab082. [PMID: 34377989 PMCID: PMC8350152 DOI: 10.1093/noajnl/vdab082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Evidence from single and multicenter phase II trials have suggested diffusion MRI is a predictive imaging biomarker for survival benefit in recurrent glioblastoma (rGBM) treated with anti-VEGF therapy. The current study confirms these findings in a large, randomized phase III clinical trial. Methods Patients with rGBM were enrolled in a phase III randomized (1:1), controlled trial (NCT02511405) to compare the efficacy and safety of bevacizumab (BV) versus BV in combination with ofranergene obadenovec (BV+VB-111), an anti-cancer viral therapy. In 170 patients with diffusion MRI available, pretreatment enhancing tumor volume and ADC histogram analysis were used to phenotype patients as having high (>1.24 µm2/ms) or low (<1.24 µm2/ms) ADCL, the mean value of the lower peak of the ADC histogram, within the contrast enhancing tumor. Results Baseline tumor volume (P = .3460) and ADCL (P = .2143) did not differ between treatment arms. Univariate analysis showed patients with high ADCL had a significant survival advantage in all patients (P = .0006), as well as BV (P = .0159) and BV+VB-111 individually (P = .0262). Multivariable Cox regression accounting for treatment arm, age, baseline tumor volume, and ADCL identified continuous measures of tumor volume (P < .0001; HR = 1.0212) and ADCL phenotypes (P = .0012; HR = 0.5574) as independent predictors of OS. Conclusion Baseline diffusion MRI and tumor volume are independent imaging biomarkers of OS in rGBM treated with BV or BV+VB-111.
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Affiliation(s)
- Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,UCLA Neuro Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Kunal Patel
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Andrew Brenner
- University of Texas Health San Antonio Cancer Center, San Antonio, Texas, USA
| | - John F de Groot
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nicholas A Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Leor Zach
- Oncology Institute, Chaim Sheba Medical Center, Tel HaShomer, Israel
| | - Jian L Campian
- Division of Medical Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jacob Schlossman
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Shan Rizvi
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | | | | | | | | | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Timothy F Cloughesy
- UCLA Neuro Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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Park YW, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging. Neuroradiology 2021; 63:1811-1822. [PMID: 33755766 DOI: 10.1007/s00234-021-02693-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE We aimed to evaluate the utility of diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast (DSC) imaging for stratifying bevacizumab treatment outcomes in patients with recurrent high-grade glioma. METHODS Fifty-three patients with recurrent high-grade glioma who underwent baseline magnetic resonance imaging including DTI, DCE, and DSC before bevacizumab treatment were included. The mean apparent diffusion coefficient, fractional anisotropy, normalized cerebral blood volume, normalized cerebral blood flow, volume transfer constant, rate transfer coefficient (Kep), extravascular extracellular volume fraction, and plasma volume fraction were assessed. Predictors of response status, progression-free survival (PFS), and overall survival (OS) were determined using logistic regression and Cox proportional hazard modeling. RESULTS Responders (n = 16) showed significantly longer PFS and OS (P < 0.001) compared with nonresponders (n = 37). Multivariable analysis revealed that lower mean Kep (odds ratio = 0.01, P = 0.008) was the only independent predictor of favorable response after adjustment for age, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Multivariable Cox proportional hazard modeling showed that a higher mean Kep was the only variable associated with shorter PFS (hazard ratio [HR] = 7.90, P = 0.006) and OS (HR = 9.71, P = 0.020) after adjustment for age, IDH mutation status, and MGMT promoter methylation status. CONCLUSION Baseline mean Kep may be a useful biomarker for predicting response and stratifying patient outcomes following bevacizumab treatment in patients with recurrent high-grade glioma.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Ju Hyung Moon
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
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