ArticleBMC cancer2026
Machine learning-based radiomics analysis of PET imaging for early prediction of concurrent chemoradiotherapy response in stage II-III cervical squamous cell carcinoma.
Article in BMC cancer, 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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Abstract
backgroundCervical squamous cell carcinoma is a major global health burden, with many patients presenting with locally advanced disease requiring concurrent chemoradiotherapy (CCRT). Early assessment of treatment response (TR) is critical for optimizing therapeutic strategies and improving clinical outcomes; however, conventional imaging parameters provide limited predictive value. This study aimed to develop a PET radiomics model using machine learning (ML) to predict early TR after CCRT in patients with stage II-III locally advanced cervical squamous cell carcinoma (LACSC).
methodsThis retrospective study included 184 patients with LACSC who received CCRT (2018-2021) and underwent pre-treatment
resultsEight radiomic features were identified. The random forest model performed best, with AUCs of 0.877 (95% confidence interval [CI]: 0.810-0.943) in the training set and 0.783 (95% CI: 0.648-0.918) in the test set. The sensitivity was 0.833 in both sets, and the specificity was 0.783 (training) and 0.682 (test).
conclusionIn this single-center retrospective cohort study, a pre-treatment
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