Abstract #1389

Enhancing Diagnostic Accuracy of American College of Radiology TI-RADS 4 Nodules: nomogram models based on MRI Morphological Features

Bin Song^1,2, Qiaohui Chen², Hao Wang², Lang Tang³, Xiaoli Xie⁴, Qingyin Fu³, Pu-Yeh Wu⁵, Anwei Mao⁶, and Mengsu Zeng¹

¹Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Medical Imaging Institute, Shanghai, China, ²Department of Radiology, Minhang Hospital, Fudan University, Shanghai, China, ³Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China, ⁴Department of Pathology, Minhang Hospital, Fudan University, Shanghai, China, ⁵GE Healthcare, Beijing, China, ⁶Department of General Surgery, Minhang Hospital, Fudan University, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Diagnosis/Prediction

Motivation: Thyroid nodules classified as ACR-TR4 present a diagnostic challenge due to their uncertain nature.

Goal(s): This study aimed to develop and validate nomogram models using MRI morphological features to enhance diagnostic accuracy of ACR-TR4 thyroid nodules, thereby reducing unnecessary FNA.

Approach: We analyzed 229 thyroid ACR-TR4 nodules. MRI morphological features were recorded, and nomogram and improved models were developed. The performance of models was assessed and compared with that of the ACR-TR4.

Results: The nomogram showed robust discrimination performance, with an AUC 0.904 in validation cohort. Improved models indicated that unnecessary FNA and missed cancer rates were lower than those of ACR-TR4 system.

Impact: MRI-based models demonstrated outstanding diagnostic performance for distinguishing benign from malignant ACR-TR4 thyroid nodules. Combined model, which utilizing restricted diffusion and reversed halo sign, holds promise for reducing the need for unnecessary FNA, while simultaneously minimizing risk of missed cancers.

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