| Abstract: |
Background: Cancer treatment can cause gonadal impairment. Acute ovarian failure is defined as the permanent loss of ovarian function within 5 years of cancer diagnosis. We aimed to develop and validate risk prediction tools to provide accurate clinical guidance for paediatric patients with cancer. Methods: In this cohort study, prediction models of acute ovarian failure risk were developed using eligible female US and Canadian participants in the Childhood Cancer Survivor Study (CCSS) cohort and validated in the St Jude Lifetime Cohort (SJLIFE) Study. 5-year survivors from the CCSS cohort were included if they were at least 18 years old at their most recent follow-up and had complete treatment exposure and adequate menstrual history (including age at menarche, current menstrual status, age at last menstruation, and menopausal aetiology) information available. Participants in the SJLIFE cohort were at least 10-year survivors. Participants were excluded from the prediction analysis if they had an ovarian hormone deficiency, had missing exposure information, or had indeterminate ovarian status. The outcome of acute ovarian failure was defined as permanent loss of ovarian function within 5 years of cancer diagnosis or no menarche after cancer treatment by the age of 18 years. Logistic regression, random forest, and support vector machines were used as candidate methods to develop the risk prediction models in the CCSS cohort. Prediction performance was evaluated internally (in the CCSS cohort) and externally (in the SJLIFE cohort) using the areas under the receiver operating characteristic curve (AUC) and the precision-recall curve (average precision [AP; average positive predictive value]). Findings: Data from the CCSS cohort were collected for participants followed up between Nov 3, 1992, and Nov 25, 2016, and from the SJLIFE cohort for participants followed up between Oct 17, 2007, and April 16, 2012. Of 11 336 female CCSS participants, 5886 (51·9%) met all inclusion criteria for analysis. 1644 participants were identified from the SJLIFE cohort, of whom 875 (53·2%) were eligible for analysis. 353 (6·0%) of analysed CCSS participants and 50 (5·7%) of analysed SJLIFE participants had acute ovarian failure. The overall median follow-up for the CCSS cohort was 23·9 years (IQR 20·4–27·9), and for SJLIFE it was 23·9 years (19·0–30·0). The three candidate methods (logistic regression, random forest, and support vector machines) yielded similar results, and a prescribed dose model with abdominal and pelvic radiation doses and an ovarian dose model with ovarian radiation dosimetry using logistic regression were selected. Common predictors in both models were history of haematopoietic stem-cell transplantation, cumulative alkylating drug dose, and an interaction between age at cancer diagnosis and haematopoietic stem-cell transplant. External validation of the model in the SJLIFE cohort produced an estimated AUC of 0·94 (95% CI 0·90–0·98) and AP of 0·68 (95% CI 0·53–0·81) for the ovarian dose model, and AUC of 0·96 (0·94–0·97) and AP of 0·46 (0·34–0·61) for the prescribed dose model. Based on these models, an online risk calculator has been developed for clinical use. Interpretation: Both acute ovarian failure risk prediction models performed well. The ovarian dose model is preferred if ovarian radiation dosimetry is available. The models, along with the online risk calculator, could help clinical discussions regarding the need for fertility preservation interventions in girls and young women newly diagnosed with cancer. Funding: Canadian Institutes of Health Research, Women and Children's Health Research Institute, National Cancer Institute, and American Lebanese Syrian Associated Charities. © 2020 Elsevier Ltd |
