Pejman Jabehdar Maralani1, Sten Myrehaug2, Hatef Mehrabian2, Aimee K M Chan3, Max Wintermark4, Chris Heyn3, John Conklin5, Benjamin M Ellingson6, Saba Rahimi7, Angus Z Lau8, Chia-Lin Tseng2, Hany Soliman2, Jay Detsky2, Shadi Daghighi3, Julia Keith9, David G Munoz9, Sunit Das10, Eshetu G Atenafu11, Nir Lipsman10, James Perry12, Greg Stanisz8, Arjun Sahgal2. 1. Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada. Electronic address: Pejman.Maralani@sunnybrook.ca. 2. Department of Radiation Oncology, Sunnybrook Health Sciences Center, University of Toronto, Canada. 3. Department of Medical Imaging, Sunnybrook Health Sciences Center, University of Toronto, Canada. 4. Department of Radiology, Stanford University, United States. 5. Department of Radiology, Massachusetts General Hospital, United States. 6. Department of Radiological Sciences and Psychiatry, University of California Los Angeles, United States. 7. Department of Biomedical Engineering, University of Toronto, Canada. 8. Department of Medical Biophysics, Sunnybrook Research Institute, University of Toronto, Canada. 9. Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada. 10. Department of Surgery, Division of Neurosurgery, University of Toronto, Canada. 11. Department of Biostatistics, University Health Network, Canada. Electronic address: e.atenafu@utoronto.ca. 12. Department of Medicine, Division of Neurology, University of Toronto, Canada.
Abstract
BACKGROUND: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. METHODS: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. RESULTS: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01-1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04-4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02-1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56-0.95, p = 0.018) was associated with longer PFS. CONCLUSION: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.
BACKGROUND: Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome. METHODS: 38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated. RESULTS: Higher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01-1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04-4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02-1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56-0.95, p = 0.018) was associated with longer PFS. CONCLUSION: ADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.
Authors: Otto M Henriksen; María Del Mar Álvarez-Torres; Patricia Figueiredo; Gilbert Hangel; Vera C Keil; Ruben E Nechifor; Frank Riemer; Kathleen M Schmainda; Esther A H Warnert; Evita C Wiegers; Thomas C Booth Journal: Front Oncol Date: 2022-03-03 Impact factor: 5.738
Authors: Vittorio Stumpo; Lelio Guida; Jacopo Bellomo; Christiaan Hendrik Bas Van Niftrik; Martina Sebök; Moncef Berhouma; Andrea Bink; Michael Weller; Zsolt Kulcsar; Luca Regli; Jorn Fierstra Journal: Cancers (Basel) Date: 2022-03-05 Impact factor: 6.639
Authors: Michael H Wang; Anthony Kim; Mark Ruschin; Hendrick Tan; Hany Soliman; Sten Myrehaug; Jay Detsky; Zain Husain; Eshetu G Atenafu; Brian Keller; Arjun Sahgal; Chia-Lin Tseng Journal: Technol Cancer Res Treat Date: 2022 Jan-Dec