Deep learning in digital pathology for personalized treatment plans of cancer patients
Zhuoyu Wen1, Shidan Wang1, Donghan M Yang1
1Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Abstract:
Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.
Insights
Artificial intelligence (AI) in digital pathology offers objective biomarker quantification from histopathology images. This approach aids oncologists in personalized cancer treatment selection, improving patient outcomes.
Area of Science:
- Oncology
- Digital Pathology
- Artificial Intelligence
Background:
- Numerous novel cancer treatments have emerged, yet patient response varies significantly.
- Selecting the optimal treatment for individual patients is a critical challenge for oncologists.
- Current biomarker assessment methods are often manual, time-consuming, and subjective.
Purpose of the Study:
- To review recent advancements in biomarker quantification using artificial intelligence (AI) in digital pathology.
- To summarize studies predicting treatment response from hematoxylin-eosin (H&E) stained histopathology images.
- To highlight the potential of AI-driven digital pathology for personalized cancer care.
Main Methods:
- Review of recent scientific literature on AI-based biomarker quantification in digital pathology.
- Analysis of studies utilizing H&E stained images for treatment response prediction.
- Synthesis of findings on the efficiency and objectivity of AI approaches.
Main Results:
- AI enables automated and objective quantification of biomarkers from histopathology images.
- Digital pathology approaches show promise in predicting patient response to cancer therapies.
- Studies demonstrate the feasibility and growing importance of AI in this field.
Conclusions:
- AI-powered digital pathology provides an efficient and objective method for biomarker assessment.
- This technology can significantly assist oncologists in personalizing cancer treatment strategies.
- AI in digital pathology is poised to become increasingly integral to cancer care and treatment selection.
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