Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

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 slidelevel 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).

Data-driven advice for applying machine learning to bioinformatics problems

“…1 For example, ML algorithms have been applied to great success in GWAS, and have proven effective at detecting patterns of epistasis within the human genome. 2 Recently, deep learning algorithms were used to detect cancer metastases on high-resolution pathology images 3 at levels comparable to human pathologists. These results, among others, indicate heavy interest in ML development and analysis for bioinformatics applications.…”

Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

“…OOF image artifacts can have even more severe consequences in automated image analysis by directly impacting detection and classification. Some studies found that systematic errors can be attributed to suboptimal focus quality, such as OOF germinal centers being mistaken for tumor metastases by an algorithm [5] .…”

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer

“…Previous work has shown that it is beneficial to have more negative samples than positive samples in a training dataset for image classification in digital pathology 29,[46][47][48] . A good ratio of negative to positive samples (i.e., patches in our case) will increase the generalization of a CNN model and decrease false positive rate.…”