Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor on Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans of patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRIs in 1297 patients with glioblastoma, including an internal cohort of 243 MRIs (January 2010-June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists based on imaging, clinical history, and pathology. Multimodal MRI with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in detecting recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± [SD]13; 121 male, 57 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610 and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR:0.53-0.89) and the AUC for detecting residual/recurrent tumor was 0.84 (95% CI:0.79- 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04/mL, P < .001) and PFS (HR = 1.04/mL, P < .001) when adjusting for age, sex and gross total resection status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01/mL, P < .001) when adjusting for age, sex and gross total resection status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, classify recurrent/residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. ©RSNA, 2024.