Application of Machine Learning for Prognostic Prediction in early-stage Cervical Cancer, Based on radiomics from ultrasound and MRI

Abstract

Abstract Purpose: We aimed to develop a model for an early-stage cervical cancer for disease free survival (DFS) prediction using machine learning methods based on the combination of clinicopathological and radiomic features which is extracted from magnetic resonance imaging (MRI) and ultrasonography (US). Methods: This retrospectively study included 144 patients who were randomly divided into training and testing cohort at a ratio of 6:4.Radiomic features were extracted from MRI and US images, and in total, 1180 radiomic features and 9 clinicopathological factors were obtained. Six supervised machine learning classifiers were used to assess the prediction performance based on all variables. Next, we established models based on various combinations of clinicopathological characteristic and radiomic features to get the best prediction model using LightGBM. The model’s performance was evaluated by accuracy (ACC) and area under the curve (AUC). Furthermore, unsupervised clustering analysis was performed to identify CC patient subgroups related to DFS prognosis based on the all variables. Results: LightGBM was superior to any other classifiers in CC DFS prediction. The model that combined clinicopathological factors with radiomic features from MRI and US showed the best performance, and the corresponding values were 0.92 of ACC and 0.86 of AUC. Unsupervised clustering analysis identified a strong tendency toward the formation of two distinct groups in DFS rate among CC patients. Conclusion: MRI and US based radiomics has the potential of DFS prediction in early-stage CC with the LightGBM classifier, and the use of predictive algorithms may facilitate the personalized treatment options.