Work

Interpretable LightGBM model with SHAP analysis predicts non‐excellent response to initial radioiodine therapy in differentiated thyroid carcinoma

Year: 2026

Type: article

Abstract: PURPOSE: I) therapy in differentiated thyroid carcinoma (DTC) and establish an interpretable predictive framework for clinical decision-making. MATERIALS AND METHODS: I therapy was randomly allocated into training (n = 664) and testing (n = 286) cohorts. Multivariable logistic regression (LR) analysis identified response-associated variables, followed by the development of seven machine learning (ML) architectures including decision trees (DT), LR, random forests (RF), support vector machines (SVM), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Model performance was systematically evaluated through ROC curves (AUC), calibration plots, and decision curve analysis (DCA), complemented by SHapley Additive exPlanationgs (SHAP) interpretability framework for optimal model explanation. RESULTS: I therapeutic interval (TI), tumor multiplicity, maximum tumor diameter (MTD), lymph node metastasis (LNM) topography, stimulated thyroglobulin (sTg), thyroglobulin antibody (TgAb) levels, administered activity (AA), and post-therapy whole-body scan (Rx-WBS) findings as independent predictors of non-excellent response (N-ER).The LightGBM architecture achieved superior predictive accuracy (AUC = 0.896) in the testing cohort, outperforming conventional LR (AUC = 0.842).SHAP interpretation identified sTg (mean absolute SHAP value = 1.285), TgAb (mean absolute SHAP value = 0.642), and LNM topography (mean absolute SHAP value = 0.308) as principal predictive determinants. DCA confirmed that the model had a net benefit higher than the "full treatment" and "no treatment" strategies at multiple threshold probabilities, indicating its certain application value in clinical decision-making. CONCLUSION: I therapeutic efficacy in DTC patients, with SHAP-driven elucidation of clinical risk factor contributions enabling personalized therapeutic paradigms.Future integration of multicenter prospective data and molecular biomarkers is required to enhance model generalizability.

Source: Journal of Applied Clinical Medical Physics

DOI: 10.1002/acm2.70555

Authors: Zhaoxia Luo, Yangyang Lei, Qing Zhang

Institutions: Nanchang University, First Affiliated Hospital of Jiangxi Medical College

Language: English

Topic: Thyroid Cancer Diagnosis and Treatment

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $900