Deep learning MRI signature to predict survival and treatment benefit from temozolomide in IDH-wildtype glioblastoma |
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| Affiliation: | 1. School of Medical Technology, Guangdong Medical University, Dongguan, China;2. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;3. Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China;4. Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;5. School of Biomedical Engineering, Guangdong Medical University, Dongguan, China;6. Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen 518045, China |
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| Abstract: | BackgroundO6-methylguanine-DNA-methyltransferase (MGMT) methylation status does not correlate with temozolomide (TMZ) sensitivity in all IDH-wildtype glioblastoma (GBM) patients. New predictors of TMZ benefit are still in demand.MethodsBased on MR images, a deep learning image signature was constructed to predict the survival and benefit of temozolomide in patients with IDH wild-type glioblastoma.ResultsDiS signature was associated with OS as an independent prognostic factor in patients with IDH-wildtype glioblastoma. For high-risk group patients, TMZ was associated with improved OS for patients in MGMT-methylated subgroup (HR: 2.051, 95 % CI: 0.939–4.482, log-rank P = 0.034), but had not effect on MGMT-unmethylated patients. However, patients in the low- risk group did not benefit from TMZ.ConclusionDiS could offer complementary value beyond MGMT methylation status in predicting survival benefit from TMZ chemotherapy in patients with IDH-wildtype glioblastoma. |
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| Keywords: | Deep learning MRI Glioblastoma Temozolomide |
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