Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
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| Title: | Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study. |
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| Authors: | Gu J; Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China., Tong T; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China., Xu D; Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China., Cheng F; Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China., Fang C; Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China., He C; Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China., Wang J; Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China., Wang B; Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China., Yang X; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China., Wang K; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China., Tian J; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China., Jiang T; Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.; Zhejiang Provincial Key Laboratory of Pulsed Electric Field Technology Medical Transformation, Hangzhou, China. |
| Source: | Cancer [Cancer] 2023 Feb 01; Vol. 129 (3), pp. 356-366. Date of Electronic Publication: 2022 Nov 19. |
| Publication Type: | Multicenter Study; Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Wiley Country of Publication: United States NLM ID: 0374236 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0142 (Electronic) Linking ISSN: 0008543X NLM ISO Abbreviation: Cancer Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1097-0142 |
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| DOI: | 10.1002/cncr.34540 |
