ArticleNPJ precision oncology2026
Dynamic multimodal radiomic model for survival prediction in cervical cancer: a multi-cohort study.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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14 authors.
Funding
Abstract
Accurate survival prediction is critical for guiding individualized radiotherapy in locally advanced cervical cancer (LACC), yet most existing models rely solely on pre-treatment imaging and overlook tumor changes during therapy. In this study, we developed and validated a multivariable Cox model to predict disease-free survival (DFS) in 971 LACC patients undergoing concurrent chemoradiotherapy. The model incorporated clinical variables, radiomics features from pre- and post-radiotherapy CT scans, and their changes during treatment, thereby quantifying treatment-associated imaging changes. It achieved C-indices of 0.713 and 0.693 in internal and external validation cohorts, with AUCs up to 0.859, showing improved overall performance compared with single-timepoint imaging models in most evaluation settings. By integrating dynamic imaging and clinical data, the model improved prognostic accuracy, enhanced risk stratification, and showed strong potential for supporting personalized radiotherapy planning.
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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 Socratic graph.