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Isolation and Functional Assessment of Human Breast Cancer Stem Cells from Cell and Tissue Samples
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Automatic cellularity assessment from post-treated breast surgical specimens.

Mohammad Peikari1, Sherine Salama2, Sharon Nofech-Mozes2

  • 1Medical Biophysics, University of Toronto, Canada.

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|October 5, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to assess breast cancer cellularity after neoadjuvant treatment (NAT). The new pipeline accurately estimates residual tumor cellularity, improving upon manual methods prone to variability.

Keywords:
breast cancermachine learningneoadjuvant therapypathology image analysis

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Oncology

Background:

  • Neoadjuvant treatment (NAT) for breast cancer (BCa) aims to improve prognosis and surgical outcomes.
  • Assessing residual tumor cellularity post-NAT is crucial for patient management but relies on manual, time-consuming, and variable pathological review.
  • Inter-observer variability in manual cellularity assessment can impact prognostic power in clinical trials.

Purpose of the Study:

  • To develop and validate an automated method for assessing residual cancer cellularity in breast cancer tissue slides.
  • To compare the performance of the automated method against expert pathologists in estimating tumor cellularity.
  • To enable objective and efficient tumor burden assessment for post-NAT response evaluation.

Main Methods:

  • Development of an automated computational pipeline to segment nuclei and estimate cellularity from breast cancer patches and whole slide images (WSIs).
  • Comparison of the automated pipeline's cellularity estimates with those provided by two expert pathologists.
  • Calculation of intra-class correlation coefficients (ICC) to quantify agreement between pathologists and between pathologists and the automated method.

Main Results:

  • The automated pipeline demonstrated strong agreement with expert pathologists, achieving ICCs of 0.74 and 0.75.
  • The intra-pathologist agreement was high at 0.89, establishing a benchmark for comparison.
  • The method successfully identified areas of high residual cancer concentration in whole slide images.

Conclusions:

  • An automated computational pipeline can accurately assess residual cancer cellularity from H&E stained breast cancer slides.
  • This automated approach offers a reliable and efficient alternative to manual assessment, reducing inter-observer variability.
  • The developed technique represents a significant step towards automated pathological assessment of tumor response to neoadjuvant therapy.