The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists
Open accessAbstract
Context.—: Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci. Objective.—: To evaluate the application and clinical implementation of a state-of-the-art deep learning–based artificial intelligence algorithm (LYmph Node Assistant or LYNA) for detection of metastatic breast cancer in sentinel lymph node biopsies. Design.—: Whole slide images were obtained from hematoxylin-eosin–stained lymph nodes from 399 patients (publicly available Camelyon16 challenge dataset). LYNA was developed by using 270 slides and evaluated on the remaining 129 slides. We compared the findings to those obtained from an independent laboratory (108 slides from 20 patients/86 blocks) using a different scanner to measure reproducibility. Results.—: LYNA achieved a slide-level area under the receiver operating characteristic (AUC) of 99% and a tumor-level sensitivity of 91% at 1 false positive per patient on the Camelyon16 evaluation dataset. We also identified 2 “normal” slides that contained micrometastases. When applied to our second dataset, LYNA achieved an AUC of 99.6%. LYNA was not affected by common histology artifacts such as overfixation, poor staining, and air bubbles. Conclusions.—: Artificial intelligence algorithms can exhaustively evaluate every tissue patch on a slide, achieving higher tumor-level sensitivity than, and comparable slide-level performance to, pathologists. These techniques may improve the pathologist's productivity and reduce the number of false negatives associated with morphologic detection of tumor cells. We provide a framework to aid practicing pathologists in assessing such algorithms for adoption into their workflow (akin to how a pathologist assesses immunohistochemistry results).
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“pathologists without time constraint, and significantly more accurate than pathologists in a simulated (1 minute per slide) environment.(14) Computer algorithms may significantly improve a pathologist’s workflow.”
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Cited by
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
Key challenges for delivering clinical impact with artificial intelligence
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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
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Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040
Retracted
Metadata
- Authors
- Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N., Peng, L. H., Hipp, J. D., & Stumpe, M. C.
- Venue
- Archives of Pathology & Laboratory Medicine
- Published
- 2018-10-08
- Volume
- 143
- Issue
- 7
- Pages
- 859-868
- ISSN
- 0003-9985 (ISSN-L)
- 1543-2165
- OpenAlex
- W2894917609
- Cited by
- 394
- References
- 27
- FWCI
- 23.76
- Type
- journal article
- Language
- en
- Open access
- diamond
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- The paper
- Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N., Peng, L. H., Hipp, J. D., & Stumpe, M. C. (2019). Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. Archives of Pathology & Laboratory Medicine. https://doi.org/10.5858/arpa.2018-0147-oa
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