Abstract #1537

Prediction of Survival During Immunotherapy of Recurrent High-Grade Glioma Using End-to-End CNN Deep Learning Versus Radiomics Models

Geoffrey S Young^1,2,3, Qi Wan^3,4, Clifford Lindsay⁵, Chenxi Zhang⁶, Jisoo Kim^1,2,3, Xin Chen^7,8, Jing Li⁹, Raymond Huang^1,2,3, David Reardon^10,11, and Lei Qin^2,3

¹Radiology, Brigham and Women's Hospital, Boston, MA, United States, ²Radiology, Harvard Medical School, Boston, MA, United States, ³Department of Imaging, Dana Farber Cancer Institute, Boston, MA, United States, ⁴The First Affiliated Hospital of Guangzhou Medical University, guangzhou, China, ⁵University of Massachusetts Chan Medical School, Worcester, MA, United States, ⁶Fudan University, Shanghai, China, ⁷Guangzhou First People’s Hospital, Guangzhou, China, ⁸School of Medicine of South China University of Technology, Guangzhou, China, ⁹Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, China, ¹⁰Center for Neuro-Oncology, Dana Farber Cancer Institute, Boston, MA, United States, ¹¹Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Diagnosis/Prediction, Radiomics

Motivation: Markers to predict overall survival (OS) in recurrent high-grade glioma (HGG) patients undergoing immunotherapy, are needed to help select patients for trials and personalize treatment.

Goal(s): To evaluate and compare deep learning and radiomic models for OS prediction in recurrent HGG, using manual segmentation, automated segmentation, and an end-to-end deep learning approach.

Approach: We developed a segmentation-free CNN model, and radiomics models using features extracted from manually and CNN-segmented ROIs. We compared accuracy of OS prediction in 154 patients.

Results: Radiomics from manual segmentation was more accurate than automated methods. The end-to-end CNN model achieved similar performance to the robust-feature manual-segmentation-based radiomics model.

Impact: End-to-end CNN models can produce similar accuracy in recurrent HGG patient survival prediction during immunotherapy, compared to robust-feature radiomics from manual segmentation, and may add value in initial patient selection for immunotherapy trials, and personalization of therapy.

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