A machine learning-based framework for prognostic prediction and tumor microenvironment characterization of locally advanced cervical cancer with concurrent chemoradiotherapy.
Authors
Affiliations (10)
Affiliations (10)
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau SAR, China.
- The College of Computer Science and Technology at Zhejiang University of Technology, No. 288, Liuhe Road, Xihu District, Hangzhou, Zhejiang, China.
- Department of Gynecological Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, China.
- Department of Gynecological Radiotherapy, Harbin Medical University Cancer Hospital, Harbin, China. [email protected].
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
- Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
Abstract
Accurate prognosis prediction for locally advanced cervical cancer (LACC) after concurrent chemoradiotherapy (CCRT) is essential for individualized treatment decision making. We aimed to develop a multitask prognostic model and reveal radiomic-phenotypic associations for LACC patients after CCRT. The framework consists of (1) A deep learning-based fully automated model (DeepMR-LACC) which used T2-weighted magnetic resonance images obtained before CCRT to predict patient outcomes; (2) Proteomics profiling from paired cervical biopsy samples for tumor microenvironment characterization and radioproteomics-based risk stratification. The DeepMR-LACC predicted progression-free survival (PFS) and overall survival (OS) in training [C-indices, 0.80 (95% confidence interval, 0.75-0.84) and 0.83 (0.80-0.87)], internal test [0.67 (0.59-0.75) and 0.70 (0.61-0.78)], external test [0.69 (0.59-0.78) and 0.65 (0.55-0.76)] cohorts. The DeepMR-LACC effectively stratified patients into high- or low-risk groups, outperforming current clinical risk factors. Furthermore, proteomic profiling revealed an immunosuppressive microenvironment in the high-risk group. Finally, radioproteomics-based risk stratification showed superior prognostic performance compared to the DeepMR-LACC for PFS and OS in the radioproteomics cohort [C-indices 0.85 (0.74-0.96) and 0.85 (0.73-0.96)]. The DeepMR-LACC enabled accurate prognostic prediction and in-depth tumor microenvironment characterization for LACC, aiding personalized long-term management.