Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes

TL;DR

This study introduces a rapid deep learning-based screening method for breast cancer metastasis in sentinel lymph nodes using minimal image patches, aiming to enhance digital pathology workflows.

Contribution

It presents a novel CNN approach that detects tumor environment changes with limited image data, reducing analysis time compared to traditional exhaustive methods.

Findings

High accuracy in detecting metastatic changes from small image patches

Proof of concept for integrating rapid screening into pathology workflows

Potential to improve pathologist productivity with automated screening

Abstract

Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for each case. Our deep learning study focuses on breast cancer screening with only a small set of image patches from any sentinel lymph node, positive or negative for metastasis, to detect changes in tumor environment and not in the tumor itself. We design a convolutional neural network in the Python language to build a diagnostic model for this purpose. The excellent results from this preliminary study provided a proof of concept for incorporating automated metastatic screen into the digital pathology workflow to augment the pathologists' productivity. Our approach is unique since it provides a very rapid screen rather than an exhaustive search for tumor in all fields of all sentinel lymph nodes.