Objective Despite advances in prevention, cervical cancer remains a serious global health issue. Concurrent chemoradiation is the standard treatment for locally advanced squamous cell carcinoma, yet 20–30% of patients develop persistent cervical cancer due to incomplete response, resulting in poor outcomes. This study aims to develop a predictive model for persistent cervical cancer in patients with locally advanced cervical squamous cell carcinoma following concurrent chemoradiation therapy, leveraging pretreatment multisequence magnetic resonance imaging data and advanced deep learning techniques. Methods This retrospective study included 259 patients with locally advanced cervical squamous cell carcinoma who underwent concurrent chemoradiation therapy at two centres. Four magnetic resonance imaging sequences were used to generate 2.5D data. A deep learning model incorporating Crossformer was developed and compared with radiomics and clinical models. Model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. Results CrossFormer model outperformed the traditional convolutional neural network models in slice-level analysis across all cohorts, achieving an area under the curve of 0.775 in the test cohorts. The deep learning model achieved high predictive accuracy, with area under the curves of 0.884, 0.833, and 0.814 in the training, validation, and test cohorts, respectively, outperforming both the clinical and radiomics models. Combining clinical features with the deep learning model further improved performance, yielding area under the curves of 0.914, 0.868, and 0.839 in the respective cohorts. Conclusion The developed model, utilizing 2.5D multi-sequence magnetic resonance imaging data and the deep learning technology that incorporated Crossformer, demonstrated strong predictive performance for persistent cervical cancer in patients with locally advanced cervical squamous cell carcinoma following concurrent chemoradiation therapy. This approach offers a promising and clinically applicable tool for treatment decision-making.
Chen et al. (Thu,) studied this question.