The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. (November 2018) - Pergialiotis, V.; Pouliakis, A.; Parthenis, C.; Damaskou, V.; Chrelias, C.; Papantoniou, N.; Panayiotides, I.
Abstract: Objective: Artificial neural networks (ANNs) and classification and regression trees (CARTs) have been previously used for the prediction of cancer in several fields. In our study, we aim to investigate the diagnostic accuracy of three different methodologies (i.e. logistic regression, ANNs and CARTs) for the prediction of endometrial cancer in postmenopausal women with vaginal bleeding or endometrial thickness ≥5 mm, as determined by ultrasound examination. Study design: We conducted a retrospective case-control study based on data from analysis of pathology reports of curettage specimens in postmenopausal women. Methods: Classical regression analysis was performed in addition to ANN and CART analysis using the IBM SPSS and Matlab statistical packages. Results: Overall, 178 women were enrolled. Among them, 106 women were diagnosed with carcinoma, whereas the remaining 72 women had normal histology in the final specimen. ANN analysis seems to perform better with a sensitivity of 86.8%, specificity of 83.3%, and overall accuracy (OA) of 85.4%. CART analysis did not perform well with a sensitivity of 78.3%, specificity of 76.4%, and OA of 77.5%. Regression analysis had a poorer predictive accuracy with a sensitivity of 76.4%, a specificity of 66.7%, and an OA of 72.5%. Conclusion: Artificial intelligence is a powerful mathematical tool that may significantly promote public health. It may be used as a non-invasive screening tool to guide clinicians involved in primary care decision making when endometrial pathology is suspected. Highlights: Endometrial cancer is the most prevalent malignancy of the reproductive tract in women. Postmenopausal vaginal bleeding (a sign that alerts for the possibility of cancer) occurs in approximately 10% of women. Current predictive models lack the adequate specificity and cannot be used as screening tools in clinical practice. Artificial intelligence may fill this gap by increasing the diagnostic accuracy of predictive algorithms.