Performance of Automatic Machine Learning versus Radiologists in the Evaluation of Endometrium on Computed Tomography

Objectives: In this study, we developed radiomic models that utilize a combination of imaging features and clinical variables to distinguish endometrial cancer (EC) from non-EC diagnoses on computed tomography (CT). Methods: A total of 926 patients consisting of 416 EC and 510 non-EC diagnoses were included. Uterus and the endometrium were manually segmented on CT. Fourteen feature selection and ten classification methods were manually examined to select the most optimized machine learning pipeline. Automatic machine learning using Tree-Based Pipeline Optimization Tool (TPOT) was performed. 847 patients were portioned into training, validation, testing sets, and another 79 patients were as our external testing set. The performance of the machine learning pipelines on the testing sets was compared to radiologists. Results: There was significant difference in age between the EC and non-EC groups (64.0 vs. 53.7, p