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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.

Mary-Kate Hayward1, Valerie M Weaver2

  • 1Center for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, California, USA.

Biochimica Et Biophysica Acta. Reviews on Cancer
|May 2, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can analyze breast ductal carcinoma in situ (DCIS) images to predict which lesions may progress to invasive cancer. This approach promises improved diagnosis and treatment strategies for DCIS patients, potentially reducing overtreatment.

Keywords:
Breast cancerDCISImage analysisPathology

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Area of Science:

  • Oncology
  • Pathology
  • Artificial Intelligence

Background:

  • Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion preceding invasive breast cancer.
  • Current histopathology cannot reliably predict which DCIS cases will progress.
  • This limitation leads to potential overtreatment of many DCIS patients.

Purpose of the Study:

  • To review the role of AI in histopathological analysis of DCIS.
  • To explore AI's potential in identifying novel features for predicting DCIS progression.
  • To discuss the clinical utility of AI in therapeutic stratification of DCIS patients.

Main Methods:

  • Review of artificial intelligence (AI) image-based analysis methods in histopathology.
  • Focus on AI's ability to identify and analyze novel features in DCIS.
  • Discussion of AI's application in diagnosis and clinical decision-making for DCIS.

Main Results:

  • AI methods show promise in accurately identifying DCIS lesions.
  • AI can potentially identify novel features for predicting disease outcome.
  • AI demonstrates potential clinical utility in therapeutic stratification of DCIS patients.

Conclusions:

  • AI techniques in histopathology offer a promising approach for DCIS diagnosis.
  • AI can aid in predicting which DCIS cases require intervention, reducing overtreatment.
  • Integration of AI into clinical practice could significantly improve DCIS patient management.