한빛사논문
- 조회501
서울대학교병원
Jung Oh Lee 1,2, Sung Soo Ahn 3, Kyu Sung Choi 1,2, Junhyeok Lee 4, Joon Jang 5, Jung Hyun Park 6, Inpyeong Hwang 1,2, Chul-Kee Park 7, Sung Hye Park 8, Jin Wook Chung 1,2,9, Seung Hong Choi 1,2,10
1Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
2Artificial Intelligence Collaborative Network, Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
3Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
4Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea.
5Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea.
6Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, South Korea.
7Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea.
8Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea.
9Institute of Innovate Biomedical Technology, Seoul National University Hospital, Seoul, Republic of Korea.
10Center for Nanoparticle Research, Institute for Basic Science, Seoul, Republic of Korea.
Correspondence: Kyu Sung Choi, MD, PhD
Abstract
Background: 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.
Methods: 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).
Results: 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.
Conclusions: 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.
논문정보
- Research article
- 2023년 10월 (BRIC 등록일 2023-10-20)
- 국내 연구진
- 의약학 > 종양의학
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