Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images.

Autor:	Tang X; Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Zhang H; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China., Mao R; Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Zhang Y; Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Jiang X; Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Lin M; Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Xiong L; Department of Medical Imaging, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China., Chen H; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Li L; Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China., Wang K; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China., Zhou J; Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China. Electronic address: zhoujh@sysucc.org.cn.
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Aug 05. Date of Electronic Publication: 2024 Aug 05.
DOI:	10.1016/j.acra.2024.07.029
Abstrakt:	Rationale and Objectives: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer. Materials and Methods: A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DL US and DL MRI , respectively), a multimodal deep learning (DL MRI+US +Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DL US , DL MRI , combined bimodal (DL MRI+US ), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves. Results: A total of 588 patients with breast cancer participated in this study. The DL MRI+US +Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness. Conclusion: The DL MRI+US ＋Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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