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Li Z, Gao J, Zhou H, Li X, Zheng T, Lin F, Wang X, Chu T, Wang Q, Wang S, Cao K, Liang Y, Zhao F, Xie H, Xu C, Zhang H, Niu Q, Ma H, Mao N. Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study. EBioMedicine 2024; 107:105311. [PMID: 39191174 DOI: 10.1016/j.ebiom.2024.105311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 08/11/2024] [Accepted: 08/12/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer. METHODS This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL. FINDINGS 1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P < 0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways. INTERPRETATION FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice. FUNDING This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).
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Affiliation(s)
- Ziyin Li
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong, 264003, China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Heng Zhou
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, 264005, China
| | - Xianglin Li
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong, 264003, China
| | - Tiantian Zheng
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong, 264003, China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Xiaodong Wang
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong, 264003, China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Tongpeng Chu
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Qi Wang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Simin Wang
- Department of Radiology, Fudan University Cancer Center, Shanghai, 200433, China
| | - Kun Cao
- Department of Radiology, Beijing Cancer Hospital, Beijing, 100142, China
| | - Yun Liang
- Department of Radiology, Guilin Municipal Hospital of Traditional Chinese Medicine, Guilin, Yunnan, 541002, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, 264005, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Cong Xu
- Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China
| | - Qingliang Niu
- Weifang NO.2 People's Hospital, Weifang, Shandong, 261041, China.
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China.
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, China.
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