Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer

• Published in: European radiology 2024-12, Vol.34 (12), p.7567-7579
• Main Authors: Li, Sheng, Wei, Xiaoting, Wang, Li, Zhang, Guizhi, Jiang, Linling, Zhou, Xuhui, Huang, Qinghua
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Aged; Biomarkers; Cancer; Computed tomography; Deep Learning; Diagnostic Radiology; Diagnostic systems; Energy; Female; Field of view; Humans; Imaging; Internal Medicine; Interventional Radiology; Lymph nodes; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic Metastasis - diagnostic imaging; Lymphatic system; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Oncology; Parameter sensitivity; Patients; Prospective Studies; Radiography, Dual-Energy Scanned Projection - methods; Radiology; Regression analysis; Regression models; Sensitivity and Specificity; Spectral sensitivity; Thyroid; Thyroid cancer; Thyroid diseases; Thyroid Neoplasms - diagnostic imaging; Thyroid Neoplasms - pathology; Tomography; Tomography, X-Ray Computed - methods; Ultrasound; Visual discrimination
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-024-10854-w

Objectives This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients. Methods In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports. Results Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively ( p