| Sumario: | Simple Summary: Medullary thyroid carcinoma (MTC) is a rare but aggressive type of thyroid cancer. Accurate preoperative diagnosis using standard ultrasound is highly challenging and relies heavily on the doctor's experience, often leading to misdiagnosis or inadequate surgery. To address this, we developed a non-invasive artificial intelligence (AI) method. Our results demonstrated that this radiomics model showed good discriminative ability in the diagnosis and stratification of MTC. By overcoming the limitations of subjective evaluation and equipment differences, this AI tool shows potential as a non-invasive adjunctive tool. Background: Preoperative diagnosis of medullary thyroid carcinoma (MTC) is clinically challenging due to sonographic overlap with other thyroid tumors. To address this, we aimed to develop a multi-vendor, multimodal radiomic framework for accurate MTC identification, comparing its diagnostic performance with that of experienced radiologists. Methods: This retrospective study included 467 pathologically confirmed thyroid nodules (94 MTCs, 373 non-MTCs) acquired across multiple ultrasound platforms. The dataset was randomly partitioned into training (80%) and internal testing (20%) sets. In total, 2250 radiomic features were extracted from grayscale and color Doppler images, followed by Z-score normalization to mitigate batch effects. A robust feature selection strategy (LASSO and recursive feature elimination) identified optimal signatures for developing machine learning classifiers (SVM, LR, RF). The optimal model was further validated on an independent, balanced cohort (n = 60; comprising 12 cases each of MTC, papillary carcinoma, follicular carcinoma, follicular adenoma, and nodular goiter) and compared with experienced radiologists across seven classification tasks. Results: The RF model achieved an AUC of 0.993 in distinguishing MTC from papillary carcinoma. The LR model showed an AUC of 0.991 for identifying MTC from all other nodules. In the independent validation cohort, the models maintained superior discriminatory ability, showing better diagnostic performance compared to the image interpretation by radiologists (AUC 0.993 vs. 0.488, p < 0.001). Conclusions: The proposed multi-vendor, multimodal radiomic system demonstrated good discriminative ability in the diagnosis and stratification of MTC. By integrating grayscale and Doppler ultrasound features while overcoming scanner variability, this model shows potential as a non-invasive adjunctive tool.
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