Development of a fertility risk calculator to predict individualized chance of ovarian failure after chemotherapy

PURPOSE: To develop an innovative machine learning (ML) model that predicts personalized risk of primary ovarian insufficiency (POI) after chemotherapy for reproductive-aged women. Currently, individualized prediction of a patient’s risk of POI is challenging. METHODS: Authors of published studies examining POI after gonadotoxic therapy were contacted, and six authors shared their de-identified data (N = 435). A composite outcome for POI was determined for each patient and validated by 3 authors. The primary dataset was partitioned into training and test sets; random forest binary classifiers were trained, and mean prediction scores were computed. Institutional data collected from a cross-sectional survey of cancer survivors (N = 117) was used as another independent validation set. RESULTS: Our model predicted individualized risk of POI with an accuracy of 88% (area under the ROC 0.87, 95% CI: 0.77–0.96; p