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.

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Publication Details

Journal
npj Precision Oncology
Published
2025-12-12
DOI
https://doi.org/10.1038/s41698-025-01234-8
Primary Topic
Endometrial and Cervical Cancer Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Jingkui Tian, Zijian Sun, Xiaoyong Zhang, Hanmei Lou et al.
npj Precision Oncology
Endometrial and Cervical Cancer Treatments
article

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

Jingkui Tian, Zijian Sun, Xiaoyong Zhang, Hanmei Lou, Haowen Li, Jiahui Ma, Liting Shi, Yue Feng, Jiayu Song, Qiyang Li, Zhen Zhang, Yihan Wang, Wei Zhu, Yuqiang Li, Yunyan Zhang, Fang Wang, Zhao Wang, Hui Ye, Lei Shi, Tao Feng, Xiaojuan Lv, C. Xu
article en

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.

npj Precision OncologyVol. 10(1)
Zhejiang Chinese Medical University (CN), Harbin Medical University (CN), Zhejiang University of Science and Technology (CN), Chinese Academy of Sciences (CN), University of Macau (MO), Zhejiang Cancer Hospital (CN), Third Affiliated Hospital of Harbin Medical University (CN), Zhejiang University of Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Japan Society for the Promotion of Science, Natural Science Foundation of Zhejiang Province
Openalex Percentile: Top 49%
Endometrial and Cervical Cancer Treatments
DOIOpen Access PDFOpenAlex
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