Predicting lymph node metastasis in papillary thyroid carcinoma: radiomics using two types of ultrasound elastography

作者：Xian‐Ya Zhang, Di Zhang, Wang Zhou, Zhiyuan Wang, Chaoxue Zhang, Jin Li, Liang Wang, Xin‐Wu Cui · 发表于：Cancer Imaging · 年份：2025 · DOI：10.1186/s40644-025-00832-w · 被引用次数：15 · 研究领域：Thyroid Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Thyroid and Parathyroid Surgery

Abstract Background To develop a model based on intra- and peritumoral radiomics features derived from B-mode ultrasound (BMUS), strain elastography (SE), and shear wave elastography (SWE) for cervical lymph node metastasis (LNM) prediction in papillary thyroid cancer (PTC) and to determine the optimal peritumoral size. Methods PTC Patients were enrolled from two medical centers. Radiomics features were extracted from intratumoral and four peritumoral regions with widths of 0.5–2.0 mm on tri-modality ultrasound (US) images. Boruta algorithm and XGBoost classifier were used for features selection and radiomics signature (RS) construction, respectively. A hybrid model combining the optimal RS with the highest AUC and clinical characteristics as well as a clinical model were built via multivariate logistic regression analysis. The performance of the established models was evaluated by discrimination, calibration, and clinical utility. DeLong’s test was used for performance comparison. The diagnostic augmentation of two radiologists with hybrid model’s assistance was also evaluated. Results A total of 660 patients (mean age, 41 years ± 12 [SD]; 506 women) were divided into training, internal test and external test cohorts. The multi-modality RS 1.0 mm yielded the optimal AUCs of 0.862, 0.798 and 0.789 across the three cohorts, outperforming other single-modality RSs and intratumoral RS. The AUCs of the hybrid model integrating multi-modality RS 1.0 mm , age, gender, tumor size an...