An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.
Authors
Affiliations (19)
Affiliations (19)
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [email protected].
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [email protected].
- Department of Radiology, The First People's Hospital of Kashi Prefecture, Kashi, China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- CellsVision Medical Technology Services Co., Ltd., Guangzhou, China.
- Department of Radiology, Sun Yat-Sen University Cancer Center, Sun Yat-Sen University, Guangzhou, China.
- Department of Radiology, Shantou Central Hospital, Shantou, China.
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
- Department of Radiology, The First People's Hospital of Qinzhou, Qinzhou, China.
- Department of Breast Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- Department of Radiology, Shenshan Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, China.
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [email protected].
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [email protected].
- Department of Medical Imaging, First Affiliated Hospital of Gannan Medical University, Ganzhou, China. [email protected].
- Department of Radiology, Yantai Yuhuangding Hospital, The Affiliated Hospital of Qingdao University, Yantai, China. [email protected].
- Department of Radiology, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, China. [email protected].
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [email protected].
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. [email protected].
Abstract
An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) metastasis in breast cancer remains to be developed to guide axillary surgery de-escalation. Here we develop a magnetic resonance imaging-based hierarchical multitask deep learning model, breast cancer axillary lymph node network (BCALN-Net) to predict SLN metastasis, SLN metastatic burden, and NSLN metastasis in 6,271 breast cancer patients. BCALN-Net achieves high performance in predicting SLN metastasis, SLN metastatic burden, and NSLN metastasis and exhibits robust performance across molecular subtypes, clinical tumor stages, clinical node stages, estrogen receptor statuses, human epidermal growth factor receptor 2 statuses, menopausal statuses, and SLN metastatic burdens. In pooled analysis of 4,081 patients, BCALN-Net also shows superior performance in predicting the omission of axillary invasive procedures and added value over clinical criteria. BCALN-Net holds the potential to provide an integrated diagnostic strategy of ALN status to help axillary surgery de-escalation in breast cancer patients.