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Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques.

S Niyas1, Ramya Bygari1, Rachita Naik1

  • 1Department of Computer Science and EngineeringNational Institute of Technology Karnataka Surathkal 575025 India.

IEEE Journal of Translational Engineering in Health and Medicine
|February 23, 2023
PubMed
Summary

This study introduces a novel deep learning framework for automated breast cancer molecular subtyping using immunohistochemistry (IHC) images. The AI accurately predicts biomarkers, improving efficiency and aiding targeted therapy.

Keywords:
Molecular subtypingbreast cancerdeep learningimage segmentation

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Biomarker analysis

Background:

  • Breast carcinoma molecular subtyping is crucial for prognosis and targeted therapy.
  • Immunohistochemistry (IHC) analysis of ER, PR, HER2, and Ki67 is the standard method.
  • Manual assessment is time-consuming and lacks automation.

Purpose of the Study:

  • To develop a novel deep learning framework for automated molecular subtyping of breast cancer from IHC images.
  • To enable accurate and efficient prediction of four key molecular biomarkers.

Main Methods:

  • A modified LadderNet architecture was employed for segmenting immunopositive elements in IHC slides.
  • The framework utilizes fully convolutional neural networks with adaptive learning pathways.
  • Post-processing quantifies immunopositive elements for biomarker status prediction.

Main Results:

  • The deep learning framework achieved high concordance with manual pathologist assessments for IHC biomarker evaluation.
  • Segmentation models demonstrated robust performance for all four biomarkers (ER, PR, HER2, Ki67).
  • Automated prediction of biomarker status was successfully validated.

Conclusions:

  • The proposed automated method significantly speeds up breast cancer molecular subtyping.
  • It reduces pathologist workload and associated costs.
  • Facilitates timely targeted treatment for improved patient outcomes.