The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.

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Abstract

This study aimed to develop and validate diagnostic models for distinguishing benign and malignant thyroid nodules. Between January 2020 and June 2024, 735 patients were retrospectively selected for surgery or needle biopsy at our hospital. This cohort was divided into a training cohort (n=514) and a validation cohort (n=221) in a ratio of 7:3. All models were validated 10 times, and a receiver operating characteristic curve (ROC curve) was generated. Furthermore, a nomogram prediction model was constructed using the independent risk factors from Model 3, and its validity was verified through ROC curves, calibration curves, and clinical decision curves. The AUC of Model 1 in the training cohort was 0.889, 0.913, 0.914, 0.914, 0.928, and 0.910, respectively. The AUC of Model 2 in the training cohort was 0.950, 0.957, 0.936, 0.946, 0.963, and 0.921, respectively. The AUC of Model 3 in the training cohort was 0.985, 0.991, 0.969, 0.949, 0.866, and 0.986, respectively. The Model 3, which was constructed based on the optimal classifier LDA, had the highest predictive value. Moreover, the clinical decision curve also indicated that it offers the greatest net benefit to patients. The integration of clinical features with multimodal ultrasound imageomics significantly improved diagnostic accuracy. The combined model showed enhanced sensitivity and specificity, potentially reducing unnecessary invasive procedures. This study developed a non-invasive preoperative diagnostic method for thyroid nodules using multimodal ultrasound histology and clinical models, and its nomogram was also convenient for clinical application.

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