Novel Artificial Intelligence Machine Learning Approaches to Precisely Predict Survival and Site-Specific Recurrence in Cervical Cancer: A Multi-Institutional Study

Background: Machine learning (ML) has been gradually integrated into oncologic research, but seldom applied in prediction of cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.Methods: We retrospectively collected data on CC patients with FIGO (2009) stage IA1 (lymphovascular invasion) to IIB2 who underwent radical hysterectomy from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables and recurrence patterns were analyzed using multivariate Cox, principal component and K-means clustering analyses. The predictive performance of eight ML models was compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.Findings: This study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. For the prediction of individual recurrence and death risk, the best results were obtained by support vector machine. For predicting the specific time of recurrence free survival and overall survival, all ML models outperformed the Cox model. (3) The ML models were superior to traditional models when dealing with multiple variables. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.Interpretation: ML models might be a better analytic approach in CC prognostic prediction than traditional models since they can predict survival and site-specific recurrence simultaneously, especially when dealing with multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.Trial Registration: This study was registered in the Chinese Clinical Trial Registry (ChiCTR1900028702).Funding Statement: The National Natural Science Foundation of China (No. 81971361), the Natural Science Foundation of Shanghai Science and Technology (No. 19ZR1406900), the Research and Innovation Project of Shanghai Municipal Education Commission (No. 2019-01-07-00-07-E00050), the artificial Intelligence Innovation Project of Shanghai Municipal Commission of Economy and Informatization (No. 2018-RGZN-02041), the clinical research project of Shanghai Municipal Health Commission (No.202040498) and shanghai “Rising Stars of Medical Talent” Youth Development Program (No. AB83030002019004).Declaration of Interests: The authors report no conflicts of interest.Ethics Approval Statement: This retrospective multicenter cohort study was approved by the Institutional Ethics Committee of Fudan University Obstetrics and Gynecology Hospital (2019-87).