Automated Prognostic Assessment of Endometrial Hyperplasia for Progression Risk Evaluation Using Artificial Intelligence

Endometrial hyperplasia is a precursor to endometrial cancer, characterized by excessive proliferation of glands that is distinguishable from normal endometrium. Current classifications define 2 types of EH, each with a different risk of progression to endometrial cancer. However, these schemes are based on visual assessments and, therefore, subjective, possibly leading to overtreatment or undertreatment. In this study, we developed an automated artificial intelligence tool (ENDOAPP) for the measurement of morphologic and cytologic features of endometrial tissue using the software Visiopharm. The ENDOAPP was used to extract features from whole-slide images of PAN-CK+-stained formalin-fixed paraffin-embedded tissue sections from 388 patients diagnosed with endometrial hyperplasia between 1980 and 2007. Follow-up data were available for all patients (mean = 140 months). The most prognostic features were identified by a logistic regression model and used to assign a low-risk or high-risk progression score. Performance of the ENDOAPP was assessed for the following variables: images from 2 different scanners (Hamamatsu XR and S60) and automated placement of a region of interest versus manual placement by an operator. Then, the performance of the application was compared with that of current classification schemes: WHO94, WHO20, and EIN, and the computerized-morphometric risk classification method: D-score. The most significant prognosticators were percentage stroma and the standard deviation of the lesser diameter of epithelial nuclei. The ENDOAPP had an acceptable discriminative power with an area under the curve of 0.765. Furthermore, strong to moderate agreement was observed between manual operators (intraclass correlation coefficient: 0.828) and scanners (intraclass correlation coefficient: 0.791). Comparison of the prognostic capability of each classification scheme revealed that the ENDOAPP had the highest accuracy of 88%-91% alongside the D-score method (91%). The other classification schemes had an accuracy between 83% and 87%. This study demonstrated the use of computer-aided prognosis to classify progression risk in EH for improved patient treatment.