Ultrasound Radiomics for Preoperative Prediction of Cervical Lymph Node Metastasis in Medullary Thyroid Carcinoma.

Abstract

Aims/Background Medullary thyroid carcinoma (MTC) is a rare thyroid malignancy with a high mortality rate. Early detection of cervical lymph node metastasis (LNM) is critical for improving prognosis for patients with MTC. This study aimed to investigate the predictive utility of ultrasound-based radiomics for preoperative prediction of cervical LNM in MTC patients. Methods The clinical, ultrasound, and pathological information of 193 patients with MTC were retrospectively examined. Radiomics features were obtained from the ultrasound images using PyRadiomics. The selected patients were randomly divided into training (n = 135) and validation (n = 58) cohorts. In the training dataset, radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) regression, and the univariate and multivariate logistic regression tests were employed to identify the clinical independent predictors of cervical LNM. Three models were created: radiomics, clinical, and combined models, with the latter presented as a nomogram. The area under the curve (AUC) was calculated to evaluate the models' predictive performance. The differences in AUCs between the combined and approach-specific models were compared using the DeLong test. The clinical usefulness of the models was evaluated using decision curve analysis (DCA). Results Nineteen radiomics features were chosen, and the AUCs of the developed radiomics model in the training and validation datasets were 0.881 and 0.859, respectively. Tumour diameter, calcitonin (Ctn) level, tumour margin, and sonographers' suspicion of cervical LNM based on ultrasound findings were clinical independent predictors for cervical LNM. The AUCs of the clinical model built using these predictors were 0.800 and 0.805 in the training and validation datasets, whereas the combined model had much-improved AUCs, measuring 0.925 for the training dataset and 0.918 for the validation test. The DeLong test indicated a significant AUC difference between the combined and clinical models (training dataset p < 0.001, validation dataset p = 0.027), but the difference between the combined and radiomics models was significant only in the training dataset (training dataset p = 0.021, validation dataset p = 0.066). Furthermore, based on the DCA results, the combined model features the largest clinical net benefit. Conclusion The nomogram, the combined model merging the ultrasound-based radiomics with clinical independent predictors, effectively predicts preoperative cervical LNM in MTC patients, outperforming the radiomics and clinical models.