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Cited by 607Radiomics and Machine Learning in Medical ImagingUltrasound Imaging and ElastographyAI in cancer detection

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

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Zheng, Xueyi, Yao, Zhao, Huang, Yini, Yu, Yanyan, Wang, Yun, Liu, Yubo, Mao, Rushuang, Li, Fei +5 more·2020·Nature Communications
DOI 10.1038/s41467-020-15027-zPMC PMC7060275
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Abstract

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

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  • Deep learning can extract deep features that adapt to tasks, thereby increasing model accuracy.

    “Deep learning can extract the deep features that adapt to tasks, thereby increasing model accuracy (11).”

    From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [11]

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  1. [45]He, K. M., Zhang, X. Y., Ren, S. Q. & Sun, J. in IEEE Conf. Comput. Vis. Pattern Recognit. 770–778 (IEEE, 2016).
  2. [46]Deng, J. et al. in IEEE Conf. Comput. Vis. Pattern Recognit. 248–255 (2009).

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Authors
Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., Hu, Y., Yu, J., & Zhou, J.
Affiliations
  • Sun Yat-sen University Cancer Center (CN)
  • Fudan University (CN)
  • Chinese Academy of Sciences (CN)
Corresponding author
  • Yu, Jinhua
  • Zhou, Jianhua
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-03-06
Volume
11
Issue
1
Pages
1236-1236
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-15027-z
PMC
PMC7060275
OpenAlex
W3010519118
Cited by
607
References
59
Citations by year
2026: 62 · 2025: 97 · 2024: 142 · 2023: 118 · 2022: 102 · 2021: 68 · 2020: 17
FWCI
37.38
Type
journal article
Language
en
Update notices
correction (2021-07-12)
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (91959127, 81971631, 2018SHZDZX01)
  • Major Research Plan
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
deep learning radiomics, axillary lymph node status, early breast cancer, shear wave elastography, breast ultrasound, axillary lymph node metastasis, metastatic burden, preoperative prediction, imaging biomarkers
MeSH terms
Deep Learning, Adult, Aged, Aged, 80 and over, Axilla, Breast, Breast Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Lymph Node Excision, Lymph Nodes, Lymphatic Metastasis, Mastectomy, Middle Aged, Neoplasm Staging, Prognosis, Prospective Studies, Reference Standards, ROC Curve, Ultrasonography, Elasticity Imaging Techniques, Preoperative Period

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