iScience (Oct 2023)
Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images
- Jiefeng Gan,
- Hanchen Wang,
- Hui Yu,
- Zitong He,
- Wenjuan Zhang,
- Ke Ma,
- Lianghui Zhu,
- Yutong Bai,
- Zongwei Zhou,
- Alan Yullie,
- Xiang Bai,
- Mingwei Wang,
- Dehua Yang,
- Yanyan Chen,
- Guoan Chen,
- Joan Lasenby,
- Chao Cheng,
- Jia Wu,
- Jianjun Zhang,
- Xinggang Wang,
- Yaobing Chen,
- Guoping Wang,
- Tian Xia
Affiliations
- Jiefeng Gan
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
- Hanchen Wang
- Department of Engineering, University of Cambridge, Fitzwilliam House 32 Trumpington Street, Cambridge CB2 1QY, UK; Computing + Mathematical Sciences Department, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA
- Hui Yu
- Wuhan Children’s Hospital, Tongji Medical College, Wuhan, Hubei 430000, China
- Zitong He
- Department of Computer Science, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218, USA
- Wenjuan Zhang
- Department of Pathology, Maternal and Child Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 43000, China
- Ke Ma
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Lianghui Zhu
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
- Yutong Bai
- Department of Computer Science, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218, USA
- Zongwei Zhou
- Department of Computer Science, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218, USA
- Alan Yullie
- Department of Computer Science, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218, USA
- Xiang Bai
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 43000, China
- Mingwei Wang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Dehua Yang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Yanyan Chen
- Department of Information Management, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China
- Guoan Chen
- Wuhan Blood Center, Wuhan, Hubei 43000, China
- Joan Lasenby
- Department of Engineering, University of Cambridge, Fitzwilliam House 32 Trumpington Street, Cambridge CB2 1QY, UK
- Chao Cheng
- Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Dr, Palo Alto, CA 94304, USA
- Jianjun Zhang
- Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Xinggang Wang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei 430000, China; Corresponding author
- Yaobing Chen
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Corresponding author
- Guoping Wang
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Corresponding author
- Tian Xia
- Institute of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China; Corresponding author
- Journal volume & issue
-
Vol. 26,
no. 10
p. 107243
Abstract
Summary: Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.
