Development and validation of age-specific risk prediction models for primary ovarian insufficiency in long-term survivors of childhood cancer: a report from the Childhood Cancer Survivor Study and St Jude Lifetime Cohort

Background: Female survivors of childhood cancer are at risk for primary ovarian insufficiency (POI), defined as the cessation of gonadal function before the age of 40 years. We aimed to develop and validate models to predict age-specific POI risk among long-term survivors of childhood cancer. Methods: To develop models to predict age-specific POI risk for the ages of 21–40 years, we used data from the Childhood Cancer Survivor Study (CCSS). Female survivors aged 18 years or older at their latest follow-up, with self-reported menstrual history information and free of subsequent malignant neoplasms within 5 years of diagnosis, were included. We evaluated models that used algorithms based on statistical or machine learning to consider all predictors, including cancer treatments. Cross-validated prediction performance metrics (eg, area under the receiver operating characteristic curve [AUROC]) were compared to select the best-performing models. For external validation of the models, we used data from 5-year survivors in the St Jude Lifetime Cohort (SJLIFE) with ovarian status clinically ascertained using hormone measurements (menopause defined by follicle stimulating hormone >30 mIU/mL and oestradiol <17 pg/mL) and medical chart or questionnaire review. We also evaluated an SJLIFE-based polygenic risk score for POI among 1985 CCSS survivors with genotype data available. Findings: 7891 female CCSS survivors (922 with POI) were included in the development of the POI risk prediction model, and 1349 female SJLIFE survivors (101 with POI) were included in the validation study. Median follow-up from cancer diagnosis was 23·7 years (IQR 18·3–30·0) in CCSS and 15·1 years (10·4–22·9) in SJLIFE. Between the ages of 21 and 40 years, POI prevalence increased from 7·9% (95% CI 7·3–8·5) to 18·6% (17·3–20·0) in CCSS and 7·3% (5·8–8·9) to 14·9% (11·6–19·1) in SJLIFE. Age-specific logistic regression models considering ovarian radiation dosimetry or prescribed pelvic and abdominal radiation dose, along with individual chemotherapy predictors, performed well in CCSS. In the SJLIFE validation, the prescribed radiation dose model performed well (AUROC 0·88–0·95), as did a simpler model that considered any exposures to pelvic or abdominal radiotherapy or alkylators (0·82–0·90). Addition of the polygenic risk predictor significantly improved the average positive predictive value (from 0·76 [95% CI 0·63–0·89] to 0·87 [0·80–0·94]; p=0·029) among CCSS survivors treated with ovarian radiation and chemotherapy. Interpretation: POI risk prediction models using treatment information showed robust prediction performance in adult survivors of childhood cancer. Funding: Canadian Institutes of Health Research, US National Cancer Institute.