Added Prognostic Value of 3D Deep Learning-Derived Features from Preoperative MRI for Adult-type Diffuse Gliomas.

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Abstract

To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network (CNN) for adult-type diffuse gliomas.In a retrospective, multicenter study, 1,925 diffuse glioma patients were enrolled from five datasets: SNUH (n=708), UPenn (n=425), UCSF (n=500), TCGA (n=160), and Severance (n=132). The SNUH and Severance datasets served as external test sets. Pre- and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers was evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score (BS).The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS=0.142 and 0.215) and survival differences (p < 0.001 and p = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio=0.032 and 0.036 (p < 0.001 and p=0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, p = 0.001, SNUH; 0.766 vs. 0.748, p = 0.023, Severance.The global morphologic feature derived from 3D-CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: [email protected].