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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital

Kaustav Bera1,2, Ian Katz3, Anant Madabhushi1,4

  • 1Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH.

JCO Clinical Cancer Informatics
|November 9, 2020
PubMed
Summary

Artificial intelligence (AI) in digital pathology offers new ways to predict cancer prognosis and treatment response. These AI methods analyze pathology images to provide insights beyond traditional tumor staging and grading.

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Area of Science:

  • Oncology
  • Digital Pathology
  • Artificial Intelligence

Background:

  • Tumor stage and grade are crucial for cancer prognosis but are subjective and vary between pathologists.
  • The TNM (tumor-node-metastasis) staging system is the standard but has limitations.
  • Artificial intelligence (AI) is emerging as a tool to analyze pathology images for cancer prediction.

Purpose of the Study:

  • To discuss AI approaches in digital pathology for cancer prognosis, genomic/molecular alteration prediction, and treatment response.
  • To highlight AI's potential to overcome limitations of conventional staging and grading.
  • To explore challenges and future opportunities for AI in oncology.

Main Methods:

  • AI approaches, including handcrafted and deep learning (DL)-based methods, are used to extract patterns from pathology images.
  • DL methods require minimal domain knowledge, relying on annotated examples for training.
  • Analysis involves comparing extracted image patterns against defined disease signatures.

Main Results:

  • AI can provide prognostic predictions independent of tumor stage and grade.
  • AI aids in predicting genomic and molecular alterations within tumors.
  • AI assists in predicting patient response to cancer treatments.

Conclusions:

  • AI in digital pathology shows promise for improving cancer diagnosis and treatment prediction.
  • Widespread clinical deployment requires addressing challenges in validation, interpretability, and reimbursement.
  • Future opportunities exist for advancing AI applications in oncology.