Basal cell carcinoma risk prediction in survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Survivors of childhood cancer face excess risk of developing basal cell carcinoma. Age-specific basal cell carcinoma risk prediction models for survivors may support targeted screening recommendations. METHODS: We developed models predicting basal cell carcinoma risk by ages 40 and 50 years featuring detailed cancer treatment predictors, utilizing statistical and machine-learning algorithms and data from 23 166 five-year survivors in the Childhood Cancer Survivor Study, a multi-institutional retrospective cohort study. Selected models were externally validated in 5314 survivors in the St Jude Lifetime Cohort. Model discrimination and precision were evaluated using the area under the receiver operating characteristic curve (AUROC) and area under the 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, basal cell carcinoma cumulative incidence was 5% and 15% in the Childhood Cancer Survivor Study and 7% and 21% in the St Jude Lifetime Cohort, respectively. The XGBoost algorithm-based models with treatment dose-specific predictors performed best, showing good external discrimination (age 40 years: AUROC = 0.75; age 50 years: AUROC = 0.76) and precision (age 40 years: AUPRC = 0.20; age 50 years: AUPRC = 0.52), outperforming COG LTFU Guideline-directed risk stratification (age 40 years: AUROC = 0.65; age 50 years: AUROC = 0.62; age 40 years: AUPRC = 0.09; age 50 years: AUPRC = 0.26; P < .01). These novel models reclassified 37% of survivors with COG-recommended skin cancer screening as low risk by age 40 years and 29% of survivors without COG-recommended screening as moderate or high risk by age 50 years, suggesting these recommendations overestimate risk in younger survivors and miss relevant predictors (eg, attained age, chemotherapy). CONCLUSIONS: In this study, we present validated basal cell carcinoma risk prediction models for childhood cancer survivors that outperform current practice guidelines. The associated online risk calculator can inform risk- and age-based screening recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".