Non-contrast computed tomography radiomics model to predict benign and malignant thyroid nodules with lobe segmentation: A dual-center study

Abstract

Background: Accurate preoperative differentiation of benign and malignant thyroid nodules is critical for optimal patient management. However, conventional imaging modalities present inherent diagnostic limitations.

Aim: To develop a non-contrast computed tomography-based machine learning model integrating radiomics and clinical features for preoperative thyroid nodule classification.

Methods: This multicenter retrospective study enrolled 272 patients with thyroid nodules (376 thyroid lobes) from center A (May 2021-April 2024), using histopathological findings as the reference standard. The dataset was stratified into a training cohort (264 lobes) and an internal validation cohort (112 lobes). Additional prospective temporal (97 lobes, May-August 2024, center A) and external multicenter (81 lobes, center B) test cohorts were incorporated to enhance generalizability. Thyroid lobes were segmented along the isthmus midline, with segmentation reliability confirmed by an intraclass correlation coefficient (≥ 0.80). Radiomics feature extraction was performed using Pearson correlation analysis followed by least absolute shrinkage and selection operator regression with 10-fold cross-validation. Seven machine learning algorithms were systematically evaluated, with model performance quantified through the area under the receiver operating characteristic curve (AUC), Brier score, decision curve analysis, and DeLong test for comparison with radiologists interpretations. Model interpretability was elucidated using SHapley Additive exPlanations (SHAP).

Results: The extreme gradient boosting model demonstrated robust diagnostic performance across all datasets, achieving AUCs of 0.899 [95% confidence interval (CI): 0.845-0.932] in the training cohort, 0.803 (95%CI: 0.715-0.890) in internal validation, 0.855 (95%CI: 0.775-0.935) in temporal testing, and 0.802 (95%CI: 0.664-0.939) in external testing. These results were significantly superior to radiologists assessments (AUCs: 0.596, 0.529, 0.558, and 0.538, respectively; P < 0.001 by DeLong test). SHAP analysis identified radiomic score, age, tumor size stratification, calcification status, and cystic components as key predictive features. The model exhibited excellent calibration (Brier scores: 0.125-0.144) and provided significant clinical net benefit at decision thresholds exceeding 20%, as evidenced by decision curve analysis.

Conclusion: The non-contrast computed tomography-based radiomics-clinical fusion model enables robust preoperative thyroid nodule classification, with SHAP-driven interpretability enhancing its clinical applicability for personalized decision-making.

Keywords: Machine learning; Non-contrast computed tomography; Papillary thyroid carcinoma; Radiomics; Thyroid nodules.

Figure 1

Study inclusion flowchart. CT: Computed tomography; PTC: Papillary thyroid carcinoma; TA: Thyroid adenoma; NG: Nodular goiter.

Figure 2

Workflow of model development. CT: Computed tomography; PTC: Papillary thyroid carcinoma; TA: Thyroid adenoma; NG: Nodular goiter.

Figure 3

Least absolute shrinkage and selection operator feature selection and comparison. A: Least absolute shrinkage and selection operator feature selection plot, where the horizontal axis represents the logarithm of the regularization parameter (λ), and the vertical axis denotes the corresponding feature weights. As λ increases, feature weights progressively decrease until exclusion at zero; B: 10-fold cross-validation curve, where the vertical axis represents the cross-validated mean squared error. The λ-value at the lowest point (λ min) is identified as the optimal regularization parameter, balancing model complexity and prediction error; C: 27 radiomic features and their corresponding weight coefficients retained after least absolute shrinkage and selection operator regression; D: Correlation heatmap of the 27 selected radiomic features, with darker colors indicating stronger correlations.

Figure 4

Radiomic score distribution across cohorts. A: Comparison of radiomic score (Radscore) between benign and malignant lesions in the training; B: Comparison of Radscore between benign and malignant lesions in the validation; C: Comparison of Radscore between benign and malignant lesions in the temporal test; D: Comparison of Radscore between benign and malignant lesions in the external test cohorts. ^aP < 0.05; ^bP < 0.001.

Figure 5

Performance evaluation of seven machine learning models. A: Receiver operating characteristic curves of different models across the training, validation, temporal test, and external test cohorts (from left to right). The area under the receiver operating characteristic curve quantifies classification performance, with higher area under the receiver operating characteristic curve values indicating superior discrimination; B: Calibration curves for different models in the training, validation, temporal test, and external test cohorts (from left to right). These curves assess the agreement between predicted probabilities and actual outcomes, with lower Brier scores indicating better calibration, as reflected by proximity to the 45° diagonal line; C: Decision curve analysis in the training, validation, temporal test, and external test cohorts (from left to right), evaluating the clinical net benefit of different models across probability thresholds. Overall, the extreme gradient boosting model exhibits consistent and superior performance across multiple cohorts. DT: Decision trees; KNM: K-nearest neighbors; LR: Logistic regression; RF: Random forests; LGBM: Light gradient boosting machines; SVM: Support vector machines; XGB: Extreme gradient boosting; AUC: Area under the receiver operating characteristic curve.

Figure 6

SHapley Additive exPlanations analysis and clinical application. A: Honeycomb and bar plot ranking SHapley Additive exPlanations values by feature importance; B: Case illustration of a 67-year-old female with an 8 mm right thyroid lobe nodule, pathologically confirmed as nodular goiter; C: Case illustration of a 43-year-old female with a 4 mm right thyroid lobe nodule, pathologically confirmed as papillary thyroid carcinoma. SHAP: SHapley Additive exPlanations.