Nomogram to differentiate benign and malignant thyroid nodules in the American College of Radiology Thyroid Imaging Reporting and Data System level 5.

OBJECTIVES: To develop and validate a nomogram for differentiating benign and malignant thyroid nodules of American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) level 5 (TR5) and improving the performance of the guideline. METHODS: From May 2018 to December 2019, 640 patients with TR5 nodules were retrospectively included in the primary cohort. Univariate and multivariable analyses were performed to determine the risk factors for thyroid cancer. A nomogram was established on the basis of multivariable analyses; the performance of the nomogram was evaluated with respect to discrimination, calibration, and clinical usefulness. The nomogram model was also compared to the ACR score model. External validation was performed and the independent validation cohort contained 201 patients from April 2021 to January 2022. RESULTS: Multivariable analyses showed that age, tumour location, multifocality, concomitant Hashimoto's disease, neck lymph node status reported by ultrasound (US) and ACR score were the independent risk factors for thyroid cancer (all p < .05). The nomogram showed good discrimination, with an area under the curve (AUC) of 0.786 (95% confidence interval [CI]: 0.742-0.830) and 0.712 (95% CI: 0.615-0.809) in the primary cohort and external validation cohort, respectively. Decision curve analysis demonstrated the clinical usefulness of the model. Compared to the ACR score model, the nomogram showed higher AUC (0.786 vs. 0.626, p < .001) and specificity (0.783 vs. 0.391). CONCLUSIONS: The presented nomogram model, based on age, tumour features and ACR score, can differentiate benign and malignant thyroid nodules in TR5 and had a high specificity.

Preoperative Evaluation of Central Lymph Nodes in Papillary Thyroid Carcinoma Using High-Resolution Ultrasound and Shear-Wave Elastography.

ABSTRACT: The aim of this study was to discuss the diagnostic value of high-resolution ultrasound and virtual touch tissue imaging quantification (VTIQ) for distinguishing metastatic and benign central lymph nodes (CLNs) in patients with papillary thyroid carcinoma. This retrospective study involved 86 pathologically proven benign lymph nodes (LNs) and 118 metastatic LNs in patients with papillary thyroid carcinoma. We analyzed the sonographic features of CLNs (size, shape, distribution, hilum, echogenicity, cystic change, calcification, vascularity, shear-wave velocity [SWV]). The prevalence of sonographic features and the SWV was compared between metastatic and benign CLNs. The size, shape, margin, distribution, presence of hilum, echogenicity, calcification, and vascularity were significantly different between benign and metastatic CLNs (P < 0.05 for all). The mean maximum SWV for malignant CLNs was 3.139 ± 0.408 m/s, whereas that of benign CLNs was 2.418 ± 0.369 m/s (P < 0.05). The cutoff point of the SWV for differentiating benign and malignant LNs was 2.675 m/s. Logistic regression analysis showed that round or irregular shape, aggregation or fusion, calcification, and VTIQ value greater than 2.675 m/s of CLNs were independent risk factors for malignancy, with an odds ratio of 5.77, 3.05, 3.23, and 62.85, respectively. High-resolution ultrasound and VTIQ can provide valuable information for distinguishing metastatic from benign CLNs.

A case-oriented web-based training system for breast cancer diagnosis.

BACKGROUND AND OBJECTIVE: Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. METHODS: We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. RESULTS: This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05). CONCLUSIONS: The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner.