Basal cell carcinoma risk prediction in survivors of childhood cancer

Abstract Background Survivors of childhood cancer face excess risk of developing basal cell carcinoma (BCC). Age-specific BCC risk prediction models for survivors may support targeted screening recommendations. Methods We developed models predicting BCC risk by ages 40 and 50 years featuring detailed cancer treatment predictors, utilizing statistical/machine learning algorithms and data from 23,166 five-year survivors in the Childhood Cancer Survivor Study (CCSS), a multi-institutional retrospective cohort study. Selected models were externally validated in 5,314 survivors in the St Jude Lifetime Cohort (SJLIFE). Model discrimination and precision were evaluated using the areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC), and benchmarked against the current Children’s Oncology Group Long-Term Follow-Up Guidelines (COG LTFU, v6.0) for skin cancer screening. Results By ages 40 and 50 years, BCC cumulative incidence was 5% and 15% in CCSS and 7% and 21% in SJLIFE. The XGBoost algorithm-based models with treatment dose-specific predictors performed best, showing good external discrimination (AUROC40y=0.75; AUROC50y=0.76) and precision (AUPRC40y=0.20; AUPRC50y=0.52), outperforming COG LTFU Guideline-directed risk stratification (AUROC40y=0.65, AUROC50y=0.62; AUPRC40y=0.09, AUPRC50y=0.26; P < .01). These novel models reclassified 37% of survivors with COG-recommended skin cancer screening as low risk by age 40 and 29% of survivors without COG-recommended screening as moderate/high risk by age 50, suggesting these recommendations overestimate risk in younger survivors and miss relevant predictors (eg, attained age, chemotherapy). Conclusions In this study, we present validated BCC risk prediction models for childhood cancer survivors that outperform current practice guidelines. The associated online risk calculator can inform risk-/age-based screening recommendations.