A vision-language foundation model improves preoperative diagnosis of follicular thyroid neoplasms using ultrasound images.

Preoperative discrimination between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challenging, as imaging and cytological approaches often show limited efficacy. Even fine-needle aspiration (FNA) biopsy and intraoperative frozen sections frequently fail to provide conclusive results. Thus, follicular thyroid neoplasms (FNs) typically necessitate complete surgical excision for definitive diagnosis, leading to unnecessary thyroidectomies for benign conditions or delayed treatment for malignancies. To address this gap, we developed FTC-Net, a vision-language foundation model, to preoperatively classify FNs using ultrasound images. In a multicenter retrospective study of 2421 patients (6477 images) from 14 institutions, FTC-Net was trained on 1462 patients and validated in two independent cohorts (n = 578 and n = 381). FTC-Net achieved AUCs of 0.836 and 0.841 in external validation, outperforming benchmark deep learning models and established TI-RADS systems. It also substantially reduced both total FNA rates and unnecessary FNA rates compared to ACR TI-RADS and C-TI-RADS. FTC-Net has the potential to serve as a non-invasive and advanced tool for the preoperative diagnosis of FNs, thereby improving clinical decision-making and reducing unnecessary procedures.