Deep learning based on ultrasound to differentiate pathologically proven atypical and typical medullary thyroid carcinoma from follicular thyroid adenoma

Objectives: To investigate the feasibility and value of deep learning based on grayscale ultrasonography in the differentiation of pathologically proven atypical and typical medullary thyroid carcinoma (MTC) from follicular thyroid adenoma (FTA). Methods: The preoperative 770 ultrasound images consisted of 354 MTCs (66% were typical MTCs with a high suspicion sonographic pattern, 34% were atypical MTCs with a suspicion pattern of intermediate or less) and 416 FTAs. All images were delineated manually by a senior sonographer to achieve the regions of interest. Two deep neural networks of ResNet-34 and ResNet-18 were performed on the training set (n = 690). The test data set (n = 80) was subsequently evaluated by the two models and two sonographers, their diagnostic performances and misdiagnosis lesions were compared and analyzed. Results: The ResNet-34 model shows higher diagnostic ability than the junior sonographer with an area under the receiver operating curve of 0.992 (95% CI: 0.840–0.970)versus 0.754 (95% CI:0.645–0.843). Moreover, 12 of 16 atypical MTCs were successfully identified by the ResNet-34, which is significantly better than the senior and junior sonographer, suggesting that these patients could benefit from timely serological examination and surgical strategy at an earlier stage. Conclusion: Deep learning to differentiate MTC from FTA on grayscale ultrasound may be a useful diagnostic support tool, especially in atypical MTC and FTA. Moreover, the computing time of deep learning is short, which will help to incorporate it into real-time ultrasound diagnosis.