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Arch Pathol Lab Med. 2018 Oct 8. doi: 10.5858/arpa.2018-0147-OA. [Epub ahead of print]

Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection.

Author information

1
From Google AI Healthcare, Google Research, Mountain View, California (Drs Liu, Kohlberger, Norouzi, Dahl, Peng, Hipp, and Stumpe); and Laboratory Department, Naval Medical Center, San Diego, California (Drs Smith, Mohtashamian, and Olson).

Abstract

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).

PMID:
30295070
DOI:
10.5858/arpa.2018-0147-OA

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