Benjamin J Geraghty1,2,3, Archya Dasgupta1,2,3, Arjun Sahgal1,2,3, Gregory J Czarnota4,5,6,7, Michael Sandhu1,2,3, Nauman Malik1,2, Pejman Jabehdar Maralani8,9, Jay Detsky1,2,3, Chia-Lin Tseng1,2,3, Hany Soliman1,2,3, Sten Myrehaug1,2,3, Zain Husain1,2,3, James Perry10,11, Angus Lau3,12. 1. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. 2. Department of Radiation Oncology, University of Toronto, Toronto, Canada. 3. Physical Sciences, Sunnybrook Research Institute, Toronto, Canada. 4. Department of Radiation Oncology, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. gregoryczarnota.submissions@gmail.com. 5. Department of Radiation Oncology, University of Toronto, Toronto, Canada. gregoryczarnota.submissions@gmail.com. 6. Physical Sciences, Sunnybrook Research Institute, Toronto, Canada. gregoryczarnota.submissions@gmail.com. 7. Department of Medical Biophysics, University of Toronto, Toronto, Canada. gregoryczarnota.submissions@gmail.com. 8. Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada. 9. Department of Medical Imaging, University of Toronto, Toronto, Canada. 10. Department of Neurology, Sunnybrook Health Sciences Centre, Toronto, Canada. 11. Department of Medicine, University of Toronto, Toronto, Canada. 12. Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Abstract
BACKGROUND: Quantitative image analysis using pre-operative magnetic resonance imaging (MRI) has been able to predict survival in patients with glioblastoma (GBM). The study explored the role of postoperative radiation (RT) planning MRI-based radiomics to predict the outcomes, with features extracted from the gross tumor volume (GTV) and clinical target volume (CTV). METHODS: Patients with IDH-wildtype GBM treated with adjuvant RT having MRI as a part of RT planning process were included in the study. 546 features were extracted from each GTV and CTV. A LASSO Cox model was applied, and internal validation was performed using leave-one-out cross-validation with overall survival as endpoint. Cross-validated time-dependent area under curve (AUC) was constructed to test the efficacy of the radiomics model, and clinical features were used to generate a combined model. Analysis was done for the entire group and in individual surgical groups-gross total excision (GTR), subtotal resection (STR), and biopsy. RESULTS: 235 patients were included in the study with 57, 118, and 60 in the GTR, STR, and biopsy subgroup, respectively. Using the radiomics model, binary risk groups were feasible in the entire cohort (p < 0.01) and biopsy group (p = 0.04), but not in the other two surgical groups individually. The integrated AUC (iAUC) was 0.613 for radiomics-based classification in the biopsy subgroup, which improved to 0.632 with the inclusion of clinical features. CONCLUSION: Imaging features extracted from the GTV and CTV regions can lead to risk-stratification of GBM undergoing biopsy, while the utility in other individual subgroups needs to be further explored.
BACKGROUND: Quantitative image analysis using pre-operative magnetic resonance imaging (MRI) has been able to predict survival in patients with glioblastoma (GBM). The study explored the role of postoperative radiation (RT) planning MRI-based radiomics to predict the outcomes, with features extracted from the gross tumor volume (GTV) and clinical target volume (CTV). METHODS: Patients with IDH-wildtype GBM treated with adjuvant RT having MRI as a part of RT planning process were included in the study. 546 features were extracted from each GTV and CTV. A LASSO Cox model was applied, and internal validation was performed using leave-one-out cross-validation with overall survival as endpoint. Cross-validated time-dependent area under curve (AUC) was constructed to test the efficacy of the radiomics model, and clinical features were used to generate a combined model. Analysis was done for the entire group and in individual surgical groups-gross total excision (GTR), subtotal resection (STR), and biopsy. RESULTS: 235 patients were included in the study with 57, 118, and 60 in the GTR, STR, and biopsy subgroup, respectively. Using the radiomics model, binary risk groups were feasible in the entire cohort (p < 0.01) and biopsy group (p = 0.04), but not in the other two surgical groups individually. The integrated AUC (iAUC) was 0.613 for radiomics-based classification in the biopsy subgroup, which improved to 0.632 with the inclusion of clinical features. CONCLUSION: Imaging features extracted from the GTV and CTV regions can lead to risk-stratification of GBM undergoing biopsy, while the utility in other individual subgroups needs to be further explored.
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Authors: Roger Stupp; Warren P Mason; Martin J van den Bent; Michael Weller; Barbara Fisher; Martin J B Taphoorn; Karl Belanger; Alba A Brandes; Christine Marosi; Ulrich Bogdahn; Jürgen Curschmann; Robert C Janzer; Samuel K Ludwin; Thierry Gorlia; Anouk Allgeier; Denis Lacombe; J Gregory Cairncross; Elizabeth Eisenhauer; René O Mirimanoff Journal: N Engl J Med Date: 2005-03-10 Impact factor: 91.245
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