{ "abstract": "Background: Accurate prognostic stratification following concurrent chemoradiotherapy (CCRT) for cervical cancer remains challenging. Existing approaches often rely on contrast-enhanced magnetic resonance imaging (MRI), which limits applicability in patients with renal dysfunction and in resource-limited settings. We aimed to develop and validate a non-contrast MRI-based prognosis prediction model combined with clinical predictors to estimate recurrence risk and progression-free survival (PFS).\n\nMaterials and Methods: A total of 145 cervical cancer patients from two centers were retrospectively analyzed. The model-building cohort (n=114) was used to extract radiomics features from pre-treatment MRI, and an extreme gradient boosting classifier was developed. Clinical predictors were identified through univariate Cox and multivariate logistic regression analyses. A nomogram integrating radiomics and clinical variables was then constructed. External validation was performed in an independent cohort (n=31). Model performance was assessed by discrimination, calibration, and its ability to stratify patients into low- and high-risk groups for predicting 5-year PFS.\n\nResults: The proposed method categorized patients into well-defined risk groups. Survival results varied greatly among groups. Kaplan–Meier analysis indicated that patients with high risk had a worse 5-year PFS compared to those with low risk. This result was observed in both the construction cohort (P < 0.001) and the external validation cohort (P = 0.012). The prognostic performance was comparable to that of contrast-enhanced MRI–based models. A significant point to note is that no gadolinium administration was necessary.\n\nDiscussion: This study develops a radiomics nomogram that relies on non-contrast MRI. The model offers strong and tailored prognostic information. Imaging features and clinical predictors combined, which improved predictive accuracy. The approach increases accessibility, particularly for patients contraindicated for contrast or in low-resource settings.\n\nConclusion: We developed and validated a non-contrast MRI radiomics nomogram that accurately predicts recurrence and 5-year PFS following CCRT in cervical cancer. The model offers a clinically applicable, cost-effective tool for personalized risk stratification.", "abstract_shown": true, "affiliations": [ "Department of Gynecologic Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing 100142, China", "Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing 100142, China", "Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, United States", "Department of Psychology, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, 100875, Beijing, China", "School of Artificial Intelligence, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, 100875, Beijing, China", "Department of Radiotherapy Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Hai Dian District, Beijing 100142, China" ], "article_type": "research article", "arxiv": null, "author_total": 11, "authors": [ { "affs": [ 0 ], "name": "Wei Wang", "orcid": "0000-0001-5896-8821" }, { "affs": [ 1 ], "name": "Xiaoting Li", "orcid": "0000-0003-2758-3843" }, { "affs": [ 2 ], "name": "Yueqi Jiang", "orcid": "0009-0004-9247-7136" }, { "affs": [ 3 ], "name": "Yiheng Lu", "orcid": "0009-0002-1323-5725" }, { "affs": [ 0 ], "name": "Min Gao", "orcid": "0000-0002-2225-8243" }, { "affs": [ 0 ], "name": "Yunong Gao", "orcid": "0000-0001-9926-0360" }, { "affs": [ 4 ], "name": "Yun Tian", "orcid": "0000-0001-5574-2325" }, { "affs": [ 5 ], "name": "Jing You", "orcid": "0000-0002-7167-6492" }, { "affs": [ 5 ], "name": "Xiaofan Li", "orcid": "0000-0001-9202-5499" }, { "affs": [ 1 ], "name": "Kun Cao", "orcid": "0000-0002-0685-300X" }, { "affs": [ 0 ], "name": "Hong Zheng", "orcid": "0000-0002-4729-4232" } ], "bibtex": "https://research.draconitic.com/paper/emsk28rngj8b9.bib", "cited_by": 0, "doi": "10.2174/0115734056449836260717192214", "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728984/", "id": "emsk28rngj8b9", "issns": [ "1573-4056" ], "landing_url": "https://doi.org/10.2174/0115734056449836260717192214", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Medicine, Imaging, Radiology, Nuclear Medicine" ], [ "openalex", "2741|Radiomics and Machine Learning in Medical Imaging" ] ], "openalex": "W7170716806", "pdf_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13728984/pdf/CMIM-22-E15734056449836.pdf", "pmcid": "PMC13728984", "pmid": "42515922", "published": "2026-07-23", "publisher": null, "ris": "https://research.draconitic.com/paper/emsk28rngj8b9.ris", "sources": [ { "first_seen": "2026-11-07T12:21:49Z", "native_id": "PMC13728984", "source": "pmc", "url": "https://doi.org/10.2174/0115734056449836260717192214" } ], "subjects": [ { "field": "medicine", "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging", "subfield": "imaging" } ], "title": "Non-contrast MRI Radiomics Nomogram for Predicting Recurrence and Progression-free Survival in Cervical Cancer following Concurrent Chemoradiotherapy", "title_text": "Non-contrast MRI Radiomics Nomogram for Predicting Recurrence and Progression-free Survival in Cervical Cancer following Concurrent Chemoradiotherapy", "type": "peer-reviewed", "updated_at": "2026-11-07T12:21:49Z", "url": "https://research.draconitic.com/paper/emsk28rngj8b9", "venue": "Current Medical Imaging", "versions": [] }