Frontiers in immunology · 2026
Machine learning diagnostic model integrating ultrasound features and immune-inflammatory biomarkers for identifying papillary thyroid carcinoma in patients with Hashimoto's thyroiditis.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Hu Lulu, Li Lu, Zhou Fengsheng, Chen Yunfeng
Аннотация
BACKGROUND: Differentiating papillary thyroid carcinoma (PTC) from benign nodules in patients with Hashimoto's thyroiditis (HT) remains a significant clinical challenge due to the complex sonographic background and overlapping inflammatory features. This study aimed to develop and validate an interpretable machine learning (ML) model integrating ultrasound features and immune-inflammatory biomarkers for PTC identification in HT patients.
METHODS: A multicenter retrospective study was conducted, enrolling 1, 200 HT patients with thyroid nodules from Wuxi People's Hospital (January 2020 to December 2025), randomly divided into a training cohort (n=840) and a testing cohort (n=360). An independent external validation cohort (n=550) was recruited from Nanjing Gaochun People's Hospital during the same period. A total of 38 candidate variables, including clinical characteristics, ultrasound features, and immune-inflammatory indices, were evaluated. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression were utilized for feature selection. Seven ML algorithms were compared to construct the optimal predictive model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability.
RESULTS: Six variables were selected as the most informative features for predicting the pathological outcome of PTC: Systemic Immune-Inflammation Index (SII), Thyroid Peroxidase Antibody (TPOAb), pseudonodule formation, shear wave elastography maximum elasticity (SWE-Emax), vascularity grade, and TI-RADS category. The Random Forest (RF) model demonstrated superior performance, achieving an AUC of 0.907 in the training cohort, 0.872 in the testing cohort, and 0.823 in the external validation cohort. Calibration curves and DCA confirmed the model's high clinical net benefit. SHAP analysis revealed that SII, TPOAb, and pseudonodule formation were the top three contributing features. A web-based risk calculator was subsequently deployed for clinical application.
CONCLUSION: The proposed interpretable RF model effectively integrates ultrasound features and immune-inflammatory biomarkers to accurately identify PTC in HT patients. The developed web-based calculator provides a practical, non-invasive tool to assist clinicians in personalized decision-making.
METHODS: A multicenter retrospective study was conducted, enrolling 1, 200 HT patients with thyroid nodules from Wuxi People's Hospital (January 2020 to December 2025), randomly divided into a training cohort (n=840) and a testing cohort (n=360). An independent external validation cohort (n=550) was recruited from Nanjing Gaochun People's Hospital during the same period. A total of 38 candidate variables, including clinical characteristics, ultrasound features, and immune-inflammatory indices, were evaluated. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression were utilized for feature selection. Seven ML algorithms were compared to construct the optimal predictive model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability.
RESULTS: Six variables were selected as the most informative features for predicting the pathological outcome of PTC: Systemic Immune-Inflammation Index (SII), Thyroid Peroxidase Antibody (TPOAb), pseudonodule formation, shear wave elastography maximum elasticity (SWE-Emax), vascularity grade, and TI-RADS category. The Random Forest (RF) model demonstrated superior performance, achieving an AUC of 0.907 in the training cohort, 0.872 in the testing cohort, and 0.823 in the external validation cohort. Calibration curves and DCA confirmed the model's high clinical net benefit. SHAP analysis revealed that SII, TPOAb, and pseudonodule formation were the top three contributing features. A web-based risk calculator was subsequently deployed for clinical application.
CONCLUSION: The proposed interpretable RF model effectively integrates ultrasound features and immune-inflammatory biomarkers to accurately identify PTC in HT patients. The developed web-based calculator provides a practical, non-invasive tool to assist clinicians in personalized decision-making.