To validate the 2018 revised FIGO cervical cancer staging system for stage III patients with a cohort from China. Patients and Methods: Patients with stage III cervical cancer (FIGO 2018) treated with definitive radiotherapy at our institute were reviewed. Each patient was evaluated with both the 2014 and 2018 staging systems. Disease-free survival (DFS) was calculated with the Kaplan-Meier method. Receiver operative characteristic (ROC) curves for the predictive accuracy of DFS in patients with cervical cancer according to different FIGO staging systems were created. Results: Between January 2008 and December 2014, a total of 586 patients with FIGO stage IIIC cervical cancer (2018) were treated with definitive radiotherapy at our institute. The 3-year DFS for patients according to FIGO stage (2014) were as follows: IB2 73.2%, IIA 63.7%, IIB 66.7%, IIIA 64.7%, and IIIB 59.6% (P=0.580). The 3-year DFS according to FIGO stage (2018) were IIIA 79.9%, IIIB 70.4%, IIIC1 66.3% and IIIC2 29.8% (P<0.001). The AUC values for DFS were 0.552 (95% CI: 0.503-0.600, P=0.037) and 0.623 (95% CI: 0.575-0.671, P<0.001) for the 2014 and 2018 FIGO staging systems, respectively. Conclusion: The 2018 FIGO staging system of cervical cancer showed more distinction within stages and better predictive accuracy for DFS than the preceding staging system in patients with stage III disease from China.

Objective: We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity-modulated radiation therapy and brachytherapy.Design: A retrospective cohort study.

Purpose We analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment strategies. Methods The research retrospectively collected 238 non-surgical cervical cancer patients treated with radiochemotherapy from 2014 to 2017. These patients were randomly divided into the training set (n = 165) and test set (n = 73). Then, we extract deep features after segmenting the HE-stained image into patches of size 224 × 224. A Lasso–Cox model was constructed with clinical data to predict 5-year OS. C-index evaluated this model performance with 95% CI, calibration curve, and ROC. Results Based on multivariate analysis, 2 of 11 clinical characteristics (C-index 0.68) and 2 of 2048 pathomic features (C-index 0.74) and clinical–pathomic model (C-index 0.83) of nomograms predict 5-year survival in the training set, respectively. In test set, compared with the pathomic and clinical characteristics used alone, the clinical–pathomic model had an AUC of 0.750 (95% CI 0.540–0.959), the clinical predictor model had an AUC of 0.729 (95% CI 0.551–0.909), and the pathomic model AUC was 0.703 (95% CI 0.487–0.919). Based on appropriate nomogram scores, we divided patients into high-risk and low-risk groups, and Kaplan–Meier survival probability curves for both groups showed statistical differences. Conclusion We built a clinical–pathomic model to predict 5-year OS in non-surgical cervical cancer patients, which may be a promising method to improve the precision of personalized therapy.