ArticleNPJ precision oncology2025
A machine learning-based framework for prognostic prediction and tumor microenvironment characterization of locally advanced cervical cancer with concurrent chemoradiotherapy.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.