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Despite advances in treatment modalities, radiotherapy remains a cornerstone in managing cervical cancer. However, radiotherapy-induced complications, such as radiation proctitis, present substantial diagnostic and prognostic challenges. Accurate diagnosis are crucial for optimizing treatment strategies and improving patient outcomes. Deep learning has shown remarkable success in medical image segmentation, aiding clinicians in assessing patient conditions. In the other hand, radiomics excels in extracting diagnostically valuable features from medical images but requires extensive manual annotation and often lacks generalizability. Therefore, combining the strengths of deep learning and radiomics is pivotal in addressing these challenges. In this study, we propose a novel paradigm that leverages deep learning models for initial segmentation, followed by detailed radiomics analysis. Specifically, we utilize the Transformer-based SAM-Med2D model to extract visual features from CT images of cervical cancer patients. We apply T-tests and Lasso regression to identify features most correlated with radiation proctitis and build predictive models using logistic regression, random forest, and naive Gaussian Bayesian algorithms. Experimental results demonstrate that our method effectively extracts CT imaging features and exhibits excellent performance in diagnosis radiation proctitis. This approach not only enhances predictive accuracy but also provides a valuable tool for personalizing treatment plans and improving patient outcomes in cervical cancer radiotherapy.<\/jats:p>","DOI":"10.1038\/s41598-025-87409-6","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T10:04:09Z","timestamp":1744970649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A prediction method for radiation proctitis based on SAM-Med2D model"],"prefix":"10.1038","volume":"15","author":[{"given":"Ning","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"87409_CR1","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1016\/j.ccell.2021.04.002","volume":"39","author":"BH Kann","year":"2021","unstructured":"Kann, B. 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