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

Kareem Allam1, Xiaohong Iris Wang1, Songlin Zhang2

  • 1Department of Pathology and Laboratory Medicine, University of Texas Health Science Center-Houston, Medical School, Houston, TX, USA.

Annals of Clinical and Laboratory Science
|January 5, 2024
PubMed
Summary

This study developed a rapid deep learning method for breast cancer metastasis detection in lymph nodes. Analyzing image patches offers a faster alternative to examining whole slide images, achieving high accuracy.

Keywords:
Breast CancerDeep LearningMetastasisRapid ScreenSentinel Lymph NodesWhole Slide Imaging

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Area of Science:

  • Computational pathology
  • Oncology
  • Digital pathology

Background:

  • Deep learning (DL) aids breast cancer metastasis detection in whole slide images (WSI) of sentinel lymph nodes.
  • Current DL methods require extensive analysis of all lymph node slides, which is time-consuming.

Purpose of the Study:

  • To develop a rapid screening method for breast cancer metastasis.
  • To analyze a small subset of image patches for detecting tumor environment changes, bypassing exhaustive WSI analysis.

Main Methods:

  • A convolutional neural network (CNN) was designed for metastasis detection.
  • WSIs from 34 cases were used, with 40 image patches extracted per WSI.
  • A dataset of 2720 image patches was created for training, validation, and testing.

Main Results:

  • The diagnostic model achieved high accuracy (91.15%), sensitivity (77.92%), and specificity (92.09%).
  • Interobserver variation among 3 users was minimal, indicating reliable results.

Conclusions:

  • This preliminary study demonstrates a proof of concept for rapid metastasis screening.
  • The approach offers a faster alternative to exhaustive tumor searching in sentinel lymph nodes.