An Explainable Deep Learning Framework for Predicting Postoperative Radiotherapy-Induced Vaginal Stenosis in Surgically Treated Cervical Cancer Patients

Taking the surgical context into account, our SE-Inception framework demonstrated enhanced accuracy and interpretability in identifying patients at risk for postoperative radiotherapy-induced VS. Through alignment with expert clinical assessments and enabling early, personalized intervention strategies, this approach has the potential to improve outcomes and long-term quality of life in cervical cancer survivors, supporting more proactive, surgery-informed treatment planning.