JNCI cancer spectrum · 2026
Artificial intelligence-based multimodal multitask analysis of thyroid ultrasound image features predicts thyroid cancer: a multicenter study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Gui Yu, Zhang Xuerui, He Yun, Li Tingting, Hu Lan, Liu Longle, Peng Dan, Yuan Juan, Xiong Xiaoyu, Li Wenhan, Wu Xin, Li Shichao, Fan Haiyan, Peng Tijie, Yang Xi, Cui Xiang, Yang Ying, Zeng Lingjuan, Song Dajiang, Liu Feng, Li Jianhui, Wang Peng, Huang Zhongquan, Chen Li
Аннотация
BACKGROUND: Thyroid nodule ultrasound (US) images and their features are of great importance in thyroid nodule diagnosis and can be helpful for radiologists' clinical decision making. The aim was to evaluate whether an artificial intelligence (AI)-assisted system can accurately characterize thyroid nodule US features and assist radiologists in diagnosing thyroid cancer.
METHODS: The AI-assisted system (MDT-TC) was trained and internally validated on B-mode US images from 7204 lesions in 6884 patients in Southwest Hospital. The model performance was validated using 3 independent external validation cohorts.
RESULTS: Echogenicity (ECH) and shape (SHA) are features of high importance for model recognition, and these features lead to excellent model performance. The model achieved up to 87.56% accuracy in determining ECH attributes and 69.21% in identifying shape categories. The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and 3 independent external validation cohorts for MDT-TC were 0.951, 0.837, 0.816, and 0.871, respectively. The sensitivity values were 98.7%, 91.2%, 90.3%, and 85.6%, respectively. The AUC for the accurate diagnosis of radiologists with MDT-TC assistance was significantly higher than that of radiologists without MDT-TC assistance (P < .001). In addition, the AUC for the accurate diagnosis of junior doctors with MDT-TC assistance was significantly higher than that for those without (P < .01).
CONCLUSION: MDT-TC incorporates radiomic features extracted from thyroid lesion US images and can significantly improve the diagnostic performance of radiologists. This result was particularly strong for junior doctors. Therefore, our data support the idea that MDT-TC can help to identify patients with thyroid cancer and could greatly benefit clinical practice.
METHODS: The AI-assisted system (MDT-TC) was trained and internally validated on B-mode US images from 7204 lesions in 6884 patients in Southwest Hospital. The model performance was validated using 3 independent external validation cohorts.
RESULTS: Echogenicity (ECH) and shape (SHA) are features of high importance for model recognition, and these features lead to excellent model performance. The model achieved up to 87.56% accuracy in determining ECH attributes and 69.21% in identifying shape categories. The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and 3 independent external validation cohorts for MDT-TC were 0.951, 0.837, 0.816, and 0.871, respectively. The sensitivity values were 98.7%, 91.2%, 90.3%, and 85.6%, respectively. The AUC for the accurate diagnosis of radiologists with MDT-TC assistance was significantly higher than that of radiologists without MDT-TC assistance (P < .001). In addition, the AUC for the accurate diagnosis of junior doctors with MDT-TC assistance was significantly higher than that for those without (P < .01).
CONCLUSION: MDT-TC incorporates radiomic features extracted from thyroid lesion US images and can significantly improve the diagnostic performance of radiologists. This result was particularly strong for junior doctors. Therefore, our data support the idea that MDT-TC can help to identify patients with thyroid cancer and could greatly benefit clinical practice.