An AI model of sonographer’s evaluation+ S-Detect + elastography + clinical information improves the preoperative identification of benign and malignant breast masses - Sun, Pengfei; Feng, Ying; Chen, Chen; Dekker, Andre; Qian, Linxue; Wang, Zhixiang; Guo, Jun (Frontiers in Oncology, 2022)
Purpose：To build an AI model with selected preoperative clinical features to further improve the accuracy of the assessment of benign and malignant breast nodules. Methods：Patients who underwent ultrasound, strain elastography and S-Detect before ultrasound-guided biopsy or surgical excision were enrolled. The diagnosis model was built using a logistic regression model. The diagnostic performances of different models were evaluated and compared. Results：179 lesions (101 benign/78 malignant) were included. The whole dataset consisted of training set (145 patients) and independent test set (34 patients). The AI models constructed based on clinical features, ultrasound features and strain elastography to predict and classify benign and malignant breast nodules had ROC AUCs of 0.87, 0.81, and 0.79 in the test set. The AUCs of the sonographer and S-Detect were 0.75 and 0.82,respectively,in the test set. The AUC of the combined AI model has the best performance was 0.89 in the test set. The combined AI model showed better specificity of 0.92 than other models. The sonographer’s assessment showed better sensitivity 0.97 in the test set. Conclusion：The combined AI model could improve preoperative identification of benign and malignant breast masses and may reduce unnecessary breast biopsy.