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Deep computational pathology in breast cancer.

Andrea Duggento¹, Allegra Conti¹, Alessandro Mauriello²

¹Department of Biomedicine and prevention, University of Rome Tor Vergata, Rome, Italy.

Seminars in Cancer Biology

|August 21, 2020

Summary

Deep learning (DL) algorithms enhance medical imaging analysis, particularly in histopathology for breast cancer detection and staging. These advanced computational pathology tools promise increased precision and reduced workload for human experts.

Keywords:

Breast cancer Deep Learning Deep Neural Networks Deep histology Digital pathology

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Area of Science:

Computational pathology
Medical image analysis
Artificial intelligence in oncology

Background:

Deep learning (DL) algorithms excel in image processing and interpretation, showing significant potential in medical imaging.
Histopathology, using whole slide imaging (WSI), is increasingly integrating DL for interpretation, aiding in cancer staging and reducing human workload.
DL applications in pathology aim to improve precision and reproducibility across various settings and techniques.

Purpose of the Study:

To review common DL architectures for image analysis, focusing on histopathological and breast histology applications.
To compare DL performance with human experts on critical tasks like mitotic count and nuclear pleomorphism analysis.
To discuss challenges and opportunities of DL in breast cancer detection, diagnosis, staging, and prognosis.

Main Methods:

Review of common DL architectures used in image analysis, with emphasis on histopathology.
Analysis of DL performance benchmarks against human experts in specific pathological tasks.
Examination of publicly available, large-scale, multicentric pathology image databases and challenges in training DL models.
Review of repositories for labeled breast cancer pathology images and methods for interpreting immunohistochemical analyses.

Main Results:

DL architectures demonstrate potential to outperform human experts in specific histopathological tasks.
Worldwide challenges and large datasets have accelerated the development of DL algorithms for pathology.
Semi-supervised learning methods are gaining traction for increased flexibility and applicability in pathology image analysis.

Conclusions:

DL is poised to significantly impact breast cancer detection, diagnosis, staging, and prognosis.
Adoption of DL paradigms presents both challenges and opportunities for the future of computational pathology.
This review serves as a foundational resource for transdisciplinary understanding of modern computational pathology.