A cascaded clinical-ultrasound-biochemical model for precise prediction before thyroid nodule fine-needle aspiration biopsy.

Abstract

Objectives: Determining the nature of thyroid nodules through a single fine-needle aspiration (FNA) biopsy is not feasible for approximately one-third of patients. We developed a predictive model to assist FNA decision-making and reduce unnecessary FNAs.

Methods: This retrospective study consecutively included patients who underwent ultrasound-guided FNA between March 2018 and March 2023. Patients were divided into a training dataset (70%) and a validation dataset (30%). Univariate analysis was performed within the training dataset using Kruskal-Wallis test for continuous variables and chi-square test or Fisher's exact test for categorical variables. Variables with significance were entered into multivariate logistic regression. The prediction model (B-Model) was constructed using a cascaded three-stage logistic regression framework: Stage I distinguished benign from non-benign nodules, Stage II differentiated malignant from non-malignant nodules, Stage III separated follicular neoplasm from indeterminate/atypia nodules. Model performance was assessed in the validation dataset using sensitivity (SEN), specificity (SPE), and accuracy (ACC). The reduction in repeat FNA facilitated by the B-Model was calculated.

Results: Training and validation datasets included 1,573 and 672 cases, respectively. The overall SEN, SPE and ACC of the B-Model were 84.7%, 76.7% and 60.1% in the validation dataset. The application of the B-Model reduced the number of patients requiring repeat FNA from 255 to 153, resulting in a 40.0% reduction.

Conclusion: The B-Model demonstrated robust predictive performance, facilitating the optimization of pre-FNA diagnostic workflows, significantly reducing unnecessary repeat FNAs, and advancing precision in thyroid nodule management.