The Journal of clinical endocrinology and metabolism · 2026
Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Redlich Antje, Pfaehler Elisabeth, Kunstreich Marina, Schmutz Maximilian, Lapa Constantin, Kuhlen Michaela
Аннотация
CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However, recurrence or failure to achieve remission remains relatively frequent, underscoring the need for improved early risk stratification.
OBJECTIVE: To develop and evaluate an interpretable machine learning model for predicting recurrence or nonremission in pediatric DTC using routine clinical and biochemical variables.
DESIGN AND SETTING: Retrospective analysis of 250 pediatric patients (aged <18 years) enrolled in the German Pediatric Oncology Hematology-Malignant Endocrine Tumors Registry (1997-2023). Inclusion required known age at diagnosis and ≥24 months of follow-up. The composite study endpoint was structural recurrence or failure to achieve remission within 24 months of initial therapy.
METHODS: An extreme gradient boosting classifier was trained on 80% of the data, with the remaining 20% used as an independent test set. Model generalizability was assessed via 50 randomized stratified train-validation splits of the training dataset. SHapley Additive exPlanations (SHAP) were used to interpret feature contributions.
RESULTS: The final model achieved an area under the receiver operating characteristic curve (AUROC) of 0.86 on the independent test set. Across 50 validation splits, the mean AUROC was 0.82 (SD ± 0.05), sensitivity 0.81 (SD ± 0.09), and specificity 0.64 (SD ± 0.06). SHAP analysis identified younger age at diagnosis (<10 years), elevated postoperative thyroglobulin levels, and distant metastases as the most influential predictors.
CONCLUSION: This interpretable machine learning model reliably predicts early recurrence or nonremission in pediatric DTC and may complement current risk stratification systems to support personalized, risk-adapted treatment decisions.
OBJECTIVE: To develop and evaluate an interpretable machine learning model for predicting recurrence or nonremission in pediatric DTC using routine clinical and biochemical variables.
DESIGN AND SETTING: Retrospective analysis of 250 pediatric patients (aged <18 years) enrolled in the German Pediatric Oncology Hematology-Malignant Endocrine Tumors Registry (1997-2023). Inclusion required known age at diagnosis and ≥24 months of follow-up. The composite study endpoint was structural recurrence or failure to achieve remission within 24 months of initial therapy.
METHODS: An extreme gradient boosting classifier was trained on 80% of the data, with the remaining 20% used as an independent test set. Model generalizability was assessed via 50 randomized stratified train-validation splits of the training dataset. SHapley Additive exPlanations (SHAP) were used to interpret feature contributions.
RESULTS: The final model achieved an area under the receiver operating characteristic curve (AUROC) of 0.86 on the independent test set. Across 50 validation splits, the mean AUROC was 0.82 (SD ± 0.05), sensitivity 0.81 (SD ± 0.09), and specificity 0.64 (SD ± 0.06). SHAP analysis identified younger age at diagnosis (<10 years), elevated postoperative thyroglobulin levels, and distant metastases as the most influential predictors.
CONCLUSION: This interpretable machine learning model reliably predicts early recurrence or nonremission in pediatric DTC and may complement current risk stratification systems to support personalized, risk-adapted treatment decisions.