An explainable and leakage-aware machine learning model for early risk stratification of radioiodine-refractory differentiated thyroid cancer: development and single-center geographic external validation
7 affiliations
- 1Department of Geriatrics, Affiliated People’s Hospital of Jiangsu University , , ,
- 2Department of Nuclear Medicine, Shuyang Hospital of Chinese Traditional Medicine , , ,
- 3Department of Laboratory, Affiliated Fourth People’s Hospital of Jiangsu University , , ,
- 4Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University , , ,
- 5Nanjing D.A. Medical Laboratory , , ,
- 6Department of Oncology, Affiliated People’s Hospital of Jiangsu University , , ,
- 7Nanjing Tianyinshan Hospital, The Affiliated Hospital of China Pharmaceutical University , , ,
Abstract
Background: Radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) poses a major therapeutic challenge, with loss of radioiodine avidity, limited treatment options, and worse outcomes. Current risk stratification systems were not designed to predict RAIR transformation before clinical refractoriness.
Methods: This retrospective two-center study included a development cohort of 302 consecutive patients and an external validation cohort of 122. Candidate predictors were restricted to baseline demographic, clinicopathological, molecular, and biochemical variables available at or before first postoperative RAI administration; all post-treatment, response-related, and time-dependent variables were excluded a priori . Six machine-learning algorithms were developed with repeated cross-validation and compared by discrimination, probability accuracy, and clinical utility. Model interpretation used SHapley Additive exPlanations (SHAP). External transportability was assessed by discrimination, calibration, decision-curve analysis, and validation of prespecified risk groups.
Results: Forty-six patients (15.2%) developed RAIR-DTC in the development cohort and 18 (14.8%) in the external cohort. Random forest was selected for the most favorable balance of cross-validated discrimination and probability accuracy, despite comparable external discrimination from XGBoost and the stacked ensemble. It achieved an area under the receiver operating characteristic curve (AUC) of 0.896 and Brier score of 0.078 in the internal test cohort, with robust external discrimination (AUC, 0.892; PR-AUC, 0.597; sensitivity, 0.778; specificity, 0.798). Raw external calibration was poor (calibration slope 0.304; observed-to-expected ratio 0.613), indicating that ranking was preserved whereas absolute risk estimates required local recalibration. SHAP analysis identified age ≥55 years, TERT promoter (TERTp) mutation, vascular invasion, initial stimulated thyroglobulin (sTg), and extrathyroidal extension (ETE) as the dominant contributors. Training-derived quantile cut points generated three-tier risk groups, identifying a low-risk subgroup with zero events in the external cohort and a low event rate (3.3%) in the internal test set, while enriching the high-risk subgroup to event rates of 75.0% and 37.2%, respectively, with 95% confidence intervals of 47.6%-92.7% and 23.0%-53.3%, respectively.
Conclusions: An explainable, leakage-aware random forest based exclusively on routinely available baseline variables provided robust external discrimination and clinically meaningful early risk stratification for RAIR-DTC. Preserved discrimination did not guarantee transportable probability estimates, underscoring that external calibration and site-specific recalibration are essential prerequisites for responsible clinical deployment.
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BibTeX
@article{tan2026explainable,
title = {{An explainable and leakage-aware machine learning model for early risk stratification of radioiodine-refractory differentiated thyroid cancer: development and single-center geographic external validation}},
author = {Gongxun Tan and Yue Zhang and Zhenwei Mao and Chuan Zhang and Jingjing Fu and Feng Wang and Canhong Zhu and Jin Zhou and Bingquan Jin and Ying Xu and Rong Qin and Xun Shao and Lele Zhang and Shaohua Li and Qian Gu},
journal = {Frontiers in endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.1952443},
url = {https://europepmc.org/article/PMC/PMC13672023},
note = {CC BY},
}
RIS
TY - JOUR
TI - An explainable and leakage-aware machine learning model for early risk stratification of radioiodine-refractory differentiated thyroid cancer: development and single-center geographic external validation
AU - Gongxun Tan
AU - Yue Zhang
AU - Zhenwei Mao
AU - Chuan Zhang
AU - Jingjing Fu
AU - Feng Wang
AU - Canhong Zhu
AU - Jin Zhou
AU - Bingquan Jin
AU - Ying Xu
AU - Rong Qin
AU - Xun Shao
AU - Lele Zhang
AU - Shaohua Li
AU - Qian Gu
PY - 2026
DA - 2026/10/02
JO - Frontiers in endocrinology
DO - 10.3389/fendo.2026.1952443
UR - https://europepmc.org/article/PMC/PMC13672023
AB - Background: Radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) poses a major therapeutic challenge, with loss of radioiodine avidity, limited treatment options, and worse outcomes. Current risk stratification systems were not designed to predict RAIR transformation before clinical refractoriness. Methods: This retrospective two-center study included a development cohort of 302 consecutive patients and an external validation cohort of 122. Candidate predictors were restricted to baseline demographic, clinicopathological, molecular, and biochemical variables available at or before first postoperative RAI administration; all post-treatment, response-related, and time-dependent variables were excluded a priori . Six machine-learning algorithms were developed with repeated cross-validation and compared by discrimination, probability accuracy, and clinical utility. Model interpretation used SHapley Additive exPlanations (SHAP). External transportability was assessed by discrimination, calibration, decision-curve analysis, and validation of prespecified risk groups. Results: Forty-six patients (15.2%) developed RAIR-DTC in the development cohort and 18 (14.8%) in the external cohort. Random forest was selected for the most favorable balance of cross-validated discrimination and probability accuracy, despite comparable external discrimination from XGBoost and the stacked ensemble. It achieved an area under the receiver operating characteristic curve (AUC) of 0.896 and Brier score of 0.078 in the internal test cohort, with robust external discrimination (AUC, 0.892; PR-AUC, 0.597; sensitivity, 0.778; specificity, 0.798). Raw external calibration was poor (calibration slope 0.304; observed-to-expected ratio 0.613), indicating that ranking was preserved whereas absolute risk estimates required local recalibration. SHAP analysis identified age ≥55 years, TERT promoter (TERTp) mutation, vascular invasion, initial stimulated thyroglobulin (sTg), and extrathyroidal extension (ETE) as the dominant contributors. Training-derived quantile cut points generated three-tier risk groups, identifying a low-risk subgroup with zero events in the external cohort and a low event rate (3.3%) in the internal test set, while enriching the high-risk subgroup to event rates of 75.0% and 37.2%, respectively, with 95% confidence intervals of 47.6%-92.7% and 23.0%-53.3%, respectively. Conclusions: An explainable, leakage-aware random forest based exclusively on routinely available baseline variables provided robust external discrimination and clinically meaningful early risk stratification for RAIR-DTC. Preserved discrimination did not guarantee transportable probability estimates, underscoring that external calibration and site-specific recalibration are essential prerequisites for responsible clinical deployment.
SN - 1664-2392
ER -