Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach - Xie, Lin; Chu, Ran; Wang, Kai; Zhang, Xi; Li, Jie; Zhao, Zhe; Yao, Shu; Wang, Zhiwen; Dong, Taotao; Yang, Xingsheng; Su, Xuantao; Qiao, Xu; et al. (Frontiers in Oncology, 2020)
Introduction: The International Federation of Gynecology and Obstetrics (FIGO) staging system is considered the most powerful prognostic factor in patients with cervical cancer. In addition, other surgical-pathological risk factors have been demonstrated to have significance in predicting the prognosis of patients. Therefore, the purpose of this study was to investigate the effects of the FIGO staging system and surgical-pathological risk factors on the prognosis of cervical cancer patients. Methods: A retrospective study was performed on patients diagnosed with cervical cancer at FIGO stage IB1–IIA2. Kaplan-Meier, Cox proportional hazards regression analysis and the support vector machine (SVM) algorithm were used to assess and validate the high-risk factors related to recurrence and death. Results: A total of 647 patients were included. Kaplan-Meier analysis showed that five high-risk factors, including FIGO stage, status of pelvic lymph node, parametrial involvement, tumour size, and depth of cervical cancer, had a significant effect on the prognosis of patients. In multivariate analysis, pelvic lymph node metastasis (hazard ratio [HR] 2.415, 95% confidence interval [CI] 1.471-3.965), parametrial involvement (HR 2.740, 95%CI 1.092-6.872) and >2/3 depth of cervical invasion (HR 2.263, 95%CI 1.045-4.902) were three independent risk factors of disease-free survival. Pelvic lymph node metastasis (HR 3.855, 95%CI 2.125-6.991) and parametrial involvement (HR 3.871, 95%CI 1.375-10.900) were two independent risk factors for overall survival. When all five high-risk factors were assembled and used for classification prediction through SVM, it achieved the highest prediction accuracy of recurrence (accuracy = 69.1%). The highest prediction accuracy for survival was 94.3% when only using the two independent predictors (the pathological status of lymph nodes and parametrium involvement) by SVM classifiers. Among the 13 groups of intermediate-risk factor, the combination of tumour size, histology and grade of differentiation was more accurate in predicting prognosis than the intermediate-risk factors in the Sedlis criteria (recurrence: 86.8% vs. 60.0%; death: 92.0% vs. 71.6%). Conclusions: The combination of FIGO stage and surgical-pathological risk factors can further enhance the prediction accuracy of the prognosis in patients with early-stage cervical cancer. Histology and grade of differentiation can further improve the prediction accuracy of intermediate-risk factors in the Sedlis criteria.