An online nomogram based on bimodal ultrasound images for preoperative diagnosis of cytologically indeterminate...

Accurately distinguishing benign from malignant indeterminate thyroid nodules is essential to avoid unnecessary diagnostic surgeries. This study aims to develop an online nomogram model that enables precise preoperative assessment of malignancy risk in indeterminate thyroid nodules (ITNs) following fine-needle aspiration, thereby helping to reduce unnecessary thyroidectomies.

Patients with thyroid nodules were recruited from five centers and divided into retrospective training, independent testing, and prospective validation cohorts. Radiomics features and deep learning features were extracted from both B-mode ultrasound (BMUS) and strain elastography ultrasound (SEUS) images for each patient. Malignancy-associated features were selected to construct BMUS and SEUS signature scores, respectively. Multivariate regression analysis was performed on all variables to develop and visualize a comprehensive model for diagnosing benign and malignant ITNs. The model was further evaluated for discrimination, calibration, and clinical usefulness.

Multimodal imaging features, genetic testing, and elastography levels were identified as key biological markers for diagnosing ITNs. The nomogram model built with these variables demonstrated strong performance. The area under the receiver operating characteristic curve was 0.907 (95% CI: 0.877-0.931) in the training set, 0.885 (95% CI: 0.821-0.932) in the external test set, and 0.860 (95% CI: 0.762-0.929) in the prospective validation set. Compared to clinical and individual scoring models, the nomogram demonstrated superior performance and calibration.

The proposed nomogram accurately diagnoses ITNs and holds promise for reducing unnecessary diagnostic thyroidectomies in clinical practice.