Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload is...

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Published in:	The American journal of surgical pathology Vol. 48; no. 7; pp. 846 - 854
Main Authors:	Retamero, Juan Antonio, Gulturk, Emre, Bozkurt, Alican, Liu, Sandy, Gorgan, Maria, Moral, Luis, Horton, Margaret, Parke, Andrea, Malfroid, Kasper, Sue, Jill, Rothrock, Brandon, Oakley, Gerard, DeMuth, George, Millar, Ewan, Fuchs, Thomas J, Klimstra, David S
Format:	Journal Article
Language:	English
Published:	United States Lippincott Williams & Wilkins 01-07-2024
Subjects:	Algorithms Artificial Intelligence Breast Neoplasms - diagnosis Breast Neoplasms - pathology Female Humans Image Interpretation, Computer-Assisted Lymphatic Metastasis - diagnosis Lymphatic Metastasis - pathology Observer Variation Original Pathologists Predictive Value of Tests Reproducibility of Results Sentinel Lymph Node - pathology Sentinel Lymph Node Biopsy Workflow
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Summary:	The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0147-5185 1532-0979 1532-0979
DOI:	10.1097/PAS.0000000000002248