Enhancing ACR TI-RADS Predictive Value: A Logistic Regression Approach With Size and Age to Personalized Fine Needle Aspiration Decision-Making.

Authors

Mehdia Amini, Ekta Shrestha, Allina Ghimire, Jennifer Newman, Alan B Silverberg, Stewart G Albert

Published in

AACE endocrinology and diabetes. Volume 13. Issue 5. Pages 699-705. Epub May 14, 2026.

Abstract

The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) stratifies thyroid nodules by malignancy risk and aims to reduce unnecessary fine-needle aspiration (FNA). We evaluated whether incorporating patient age and nodule size improves the positive predictive value for clinically relevant thyroid neoplasia.
We retrospectively reviewed thyroid ultrasound examinations of patients age 18-75, first applying standard ACR TI-RADS criteria and then fitting logistic regression models using a composite predictor defined as TI-RADS category multiplied by nodule size in millimeters (TI-RADS × mm).
Of 446 eligible patients, 171 (38%) met standard ACR criteria for FNA. Fourteen (3.0%) had clinically relevant thyroid lesions (carcinoma or neoplasm) on surgical pathology. Standard ACR criteria for FNA define TI-RADS categories versus abrupt size thresholds. In this evaluation, logistic regression using TI-RADS × mm supported a continuous risk gradient. Age was not independently associated with malignancy risk but was associated with a higher frequency of benign nodules meeting biopsy criteria, yielding an inverse age-risk relationship among nodules meeting threshold criteria. We propose an exploratory model to consider a continuum of nodule size and age of the patient risk model could influence and individualized approach for FNA.
TI-RADS × mm scores provide a continuous estimate of malignancy risk. While TI-RADS ≤2 is reassuring and TI-RADS 5 is strongly suspicious, the lower risk category of TI-RADS 3 may benefit from individualized, shared decision-making that considers patient age and nodule size, particularly in borderline cases.

PMID:
42978764
Bibliographic data and abstract were imported from PubMed on 04 Nov 2026.

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