Deep learning in digital pathology for personalized treatment plans of cancer patients.
Zhuoyu Wen, Shidan Wang, Donghan M Yang, Yang Xie, Mingyi Chen, Justin Bishop, Guanghua Xiao
Author Information
Zhuoyu Wen: Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Shidan Wang: Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Donghan M Yang: Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Yang Xie: Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Mingyi Chen: Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
Justin Bishop: Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
Guanghua Xiao: Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address: guanghua.xiao@utsouthwestern.edu.
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.
