A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

SIMPLE SUMMARY: Accurate prediction of the risk of endometrial cancer (EC) recurrence is crucial to identify the best treatment and achieve the most favorable outcome. Currently, no model is available to predict this recurrence risk using pre-surgical computed tomography (CT) scans. This pilot study was carried out to investigate the potential of radiomic features extracted from CT scans to accurately predict the risk recurrence in such patients. The results showed that a machine learning-based model trained on CT radiomic features was able to predict EC recurrence risk with high accuracy. These results suggest that radiomics analysis using pre-surgical CT scans may provide a valuable tool for predicting recurrences in patients with EC. Further independent studies are required to strengthen these findings. ABSTRACT: Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. Methods: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). Results: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. Conclusions: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.