Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.
Authors
Affiliations (11)
Affiliations (11)
- Department of Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, 212002, China.
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China.
- Department of Medical Laboratory Technology, University of Cape Coast, Cape Coast, Ghana.
- College of Engineering, Birmingham City University, Birmingham, B4 7XG, UK.
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China. [email protected].
- Northern Jiangsu People's Hospital, Yangzhou, Jiangsu Province, China. [email protected].
- The Yangzhou Clinical Medical College of Xuzhou Medical University, Yangzhou, Jiangsu Province, China. [email protected].
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China. [email protected].
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China. [email protected].
- Northern Jiangsu People's Hospital, Yangzhou, Jiangsu Province, China. [email protected].
- The Yangzhou Clinical Medical College of Xuzhou Medical University, Yangzhou, Jiangsu Province, China. [email protected].
Abstract
The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients with breast cancer who underwent preoperative breast ultrasonography (US) between April 2016 and June 2022 were retrospectively enrolled. The training cohort consisted of 621 patients, whereas validation cohort 1 included 112 patients, and validation cohort 2 included 87 patients. A US-based GCN model was built using US deep learning features. In validation cohort 1, the US-based GCN model performed satisfactorily, with an AUC of 0.88 and an accuracy of 0.76. In validation cohort 2, the US-based GCN model performed satisfactorily, with an AUC of 0.84 and an accuracy of 0.75. This approach has the potential to help guide optimal ALNM management in breast cancer patients, particularly by preventing overtreatment. In conclusion, we developed a US-based GCN model to assess the ALN status of breast cancer patients prior to surgery. The US-based GCN model can provide a possible noninvasive method for detecting ALNM and aid in clinical decision-making. High-level evidence for clinical use in later studies is anticipated to be obtained through prospective studies.