Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS

• Published in: Cancers Vol. 14; no. 19; p. 4826
• Main Authors: Lu, Wenwu, Zhang, Di, Zhang, Yuzhi, Qian, Xiaoqin, Qian, Cheng, Wei, Yan, Xia, Zicong, Ding, Wenbo, Ni, Xuejun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2022; MDPI
• Subjects: Accuracy; Benign; Correlation analysis; Diagnosis; Diagnostic imaging; Hospitals; Logistics; Lymphatic system; Metastasis; Nodules; nomogram; Nomography (Mathematics); Open source software; Patients; Physicians; Public domain; Radiomics; Regression analysis; Reproducibility; Risk factors; Technology application; Thyroid; Thyroid cancer; thyroid imaging reporting and data system (TI-RADS); thyroid nodules; Ultrasonic imaging; Ultrasound; Wavelet transforms; China; radiomics; nomogram; thyroid imaging reporting and data system (TI-RADS); thyroid nodules; ultrasound
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14194826

The aim of the present study was to develop a radiomics nomogram to assess whether thyroid nodules (TNs) < 1 cm are benign or malignant. From March 2021 to March 2022, 156 patients were admitted to the Affiliated Hospital of Nantong University, and from September 2017 to March 2022, 116 patients were retrospectively collected from the Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine. These patients were divided into a training group and an external test group. A radiomics nomogram was established using multivariate logistics regression analysis using the radiomics score and clinical data, including the ultrasound feature scoring terms from the thyroid imaging reporting and data system (TI-RADS). The radiomics nomogram incorporated the correlated predictors, and compared with the clinical model (training set AUC: 0.795; test set AUC: 0.783) and radiomics model (training set AUC: 0.774; test set AUC: 0.740), had better discrimination performance and correction effects in both the training set (AUC: 0.866) and the test set (AUC: 0.866). Both the decision curve analysis and clinical impact curve showed that the nomogram had a high clinical application value. The nomogram constructed based on TI-RADS and radiomics features had good results in predicting and distinguishing benign and malignant TNs < 1 cm.