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

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 and microcalcification were 0.883, 0.873 and 0.841, respectively, which were significantly superior to other RSs and clinical model (all p < 0.05). The hybrid model assisted to significantly improve the sensitivities of junior and senior radiologists by 19.7% and 18.3%, respectively (all p < 0.05).

Conclusions: The intra-peritumoral radiomics model based on tri-modality US imaging holds promise for improving risk stratification and guiding treatment strategies in PTC.

Trial registration: Retrospectively registered.