A vision-language foundation model improves preoperative diagnosis of follicular thyroid neoplasms using ultrasound images.
Authors
Affiliations (19)
Affiliations (19)
- Department of Ultrasound, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
- Department of Medicine Ultrasonics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
- Department of Ultrasound, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Clinical Research Academy of Chinese Medicine, Guangzhou, Guangdong, China.
- Department of Pediatric/Aesthetic medicine Surgery, The First Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
- Department of Radiology, The First People's Hospital of Foshan, Foshan, Guangdong, China.
- Department of Ultrasound, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
- Department of Radiology, The People's Hospital of Wenshan Prefecture, Wenshan, Yunnan, China.
- Department of Radiology, Zhuhai Clinical Medical College of Jinan University (Zhuhai People's Hospital), Zhuhai, Guangdong, China.
- Department of Ultrasound, The Second People's Hospital of Foshan, Foshan, Guangdong, China.
- Department of Ultrasound, Shunde Hospital of Southern Medical University, Foshan, Guangdong, China.
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, China.
- Department of Anesthesiology, The First People's Hospital of Yunnan Province, Kunming, Yunnan, China.
- Department of Ultrasound, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China. [email protected].
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China. [email protected].
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China. [email protected].
Abstract
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.