Prediction model for lateral lymph node metastasis of papillary thyroid carcinoma in children and adolescents based on ultrasound imaging and clinical features: a retrospective study.

BACKGROUND: The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC. METHODS: We developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis. RESULTS: Among the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%. CONCLUSIONS: The independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.

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.