A machine learning-based framework for prognostic prediction and tumor microenvironment characterization of locally advanced cervical cancer with concurrent chemoradiotherapy.

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.