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Cited by 384AI in cancer detectionRadiomics and Machine Learning in Medical ImagingBreast Lesions and Carcinomas

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Open access
Zhou, Li-Qiang, Wu, Xing-Long, Huang, Shu-Yan, Wu, Ge-Ge, Ye, Hua-Rong, Wei, Qi, Bao, Ling-Yun, Deng, You-Bin +3 more·2020·Radiology
DOI 10.1148/radiol.2019190372
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Abstract

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models’ performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

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Authors
Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., & Dietrich, C. F.
Affiliations
  • Wuhan Textile University (CN)
  • China Resources (China) (CN)
ORCID
Venue
Radiology
Publisher
Radiological Society of North America (RSNA)
Published
2020-01
Volume
294
Issue
1
Pages
19-28
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
DOI
10.1148/radiol.2019190372
OpenAlex
W2989693433
Cited by
384
References
24
Citations by year
2026: 36 · 2025: 61 · 2024: 66 · 2023: 88 · 2022: 69 · 2021: 41 · 2020: 20 · 2019: 2
FWCI
15.06
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Radiology (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
Keywords
breast cancer, axillary lymph node metastasis, ultrasonography, convolutional neural networks, InceptionV3, lymph node metastasis prediction, radiologist comparison, breast cancer early detection, lymph node echogenicity
MeSH terms
Deep Learning, Adult, Aged, Aged, 80 and over, Algorithms, Breast Neoplasms, Feasibility Studies, Female, Humans, Image Interpretation, Computer-Assisted, Lymph Nodes, Lymphatic Metastasis, Middle Aged, Predictive Value of Tests, Retrospective Studies, Sensitivity and Specificity, Cohort Studies, Ultrasonography, Mammary, Neural Networks, Computer, Young Adult

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