Preoperative assessment of axillary lymph node tumor burden in cT1-2N0 breast cancer patients with a modality-adaptive network based on sentinel lymph node ultrasound images.
Authors
Affiliations (9)
Affiliations (9)
- Department of Ultrasound, Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China.
- Department of Computer Science, Rutgers, The State University of New Jersey, New Brunswick, USA.
- Ultrasound Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
- Department of Electronic Engineering, The Chinese University of Hong Kong, Room 428, Ho Sin Hang Engineering Building (SHB), The Chinese University of Hong Kong, NT, HK, Hong Kong SAR, China.
- Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Breast Surgery, Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China.
- Department of Electronic Engineering, The Chinese University of Hong Kong, Room 428, Ho Sin Hang Engineering Building (SHB), The Chinese University of Hong Kong, NT, HK, Hong Kong SAR, China. [email protected].
- Ultrasound Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China. [email protected].
- Department of Ultrasound, Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China. [email protected].
Abstract
To determine a direct method for diagnosing axillary lymph node (ALN) tumor burden preoperatively in cT1-2N0 breast cancer patients, we developed and validated a deep learning (DL) model based on ultrasound (US) images of sentinel lymph nodes (SLNs) detected by contrast-enhanced lymphatic ultrasound (CEUS). Women with cT1-T2N0 breast cancer who received CEUS were enrolled prospectively from Peking Union Medical College Hospital between April 2020 and July 2021 and from Sichuan Cancer Hospital between April 2022 and July 2022. Heavy ALN tumor burden was defined as > 2 metastatic lymph nodes according to the Z0011 criteria. We developed a DL model, the modality-adaptive network with clinicopathological information (MAN + C), using grayscale or color Doppler US images and radioclinicopathological information to predict heavy tumor burden. A total of 595 SLNs from 374 patients met the inclusion criteria. The areas under the receiver operating characteristic curve (AUCs) were calculated to evaluate the predictive performance of the model, yielding values of 0.91[95%CI(confidence interval):0.899-0.943], 0.98[95%CI:0.950-1], 0.89[95%CI:0.850-0.935], and 0.84[95%CI:0.811-0.869] in the training, validation, independent and external test datasets, respectively. Compared to current artificial intelligence (AI) models, this model extends usability by 30%, encompassing patients with multifocal lesions or those who have undergone primary breast cancer lesion therapy. Using this model, 88.9% of patients could safely decide whether they could waive unnecessary SLN biopsy. MAN + C provided a direct and efficient method for accurate preoperative assessment of ALN tumor burden in cT1-2N0 breast cancer patients.