Diagnostic Nomogram Model for ACR TI-RADS 4 Nodules Based on Clinical, Biochemical Data and Sonographic Patterns.

Abstract

Objectives: The objective of this study was to develop and validate a nomogram model integrating clinical, biochemical and ultrasound features to predict the malignancy rates of Thyroid Imaging Reporting and Data System 4 (TR4) thyroid nodules.

Methods: A total of 1557 cases with confirmed pathological diagnoses via fine-needle aspiration (FNA) were retrospectively included. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of malignancy. These predictors were incorporated into the nomogram model, and its predictive performance was evaluated using receiver-operating characteristic curve (AUC), calibration plots, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA).

Results: Eight out of 22 variables-age, margin, extrathyroidal extension, halo, calcification, suspicious lymph node metastasis, aspect ratio and thyroid peroxidase antibody-were identified as independent predictors of malignancy. The calibration curve demonstrated excellent performance, and DCA indicated favourable clinical utility. Additionally, our nomogram exhibited superior predictive ability compared to the current American College of Radiology (ACR) score model, as indicated by higher AUC, NRI, IDI, negative likelihood ratio (NLR) and positive likelihood ratio (PLR) values.

Conclusions: The developed nomogram model effectively predicts the malignancy rate of TR4 thyroid nodules, demonstrating promising clinical applicability.