Affiliation:
1. Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine University of Science and Technology of China Hefei China
2. School of Management Hefei University of Technology Hefei China
3. Key Laboratory of Process Optimization and Intelligent Decision‐Making Ministry of Education Hefei China
4. Ministry of Education Engineering Research Center for Intelligent Decision‐Making & Information System Technologies Hefei China
5. Reliable Intelligence and Medical Innovation Laboratory (RIMI Lab), Department of Biostatistics & Data Science University of Kansas Medical Center Kansas City Kansas USA
Abstract
ObjectivesOur study aims to investigate the impact of B‐mode ultrasound (B‐US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions.MethodsTotally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet‐18 network was employed to predict four molecular subtypes from B‐US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1‐scores, and confusion matrix.ResultsBased on B‐US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1‐scores is 0.73. By combining B‐US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B‐US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40 years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40 years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively.ConclusionMultimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B‐US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40 years old compared with those who are 40 years old and above.