Cooper Lee A D, Carter Alexis B, Farris Alton B, Wang Fusheng, Kong Jun, Gutman David A, Widener Patrick, Pan Tony C, Cholleti Sharath R, Sharma Ashish, Kurc Tahsin M, Brat Daniel J, Saltz Joel H
Center for Comprehensive Informatics, Emory University, Atlanta, GA 30306 USA.
Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30306 USA.
Proc IEEE Inst Electr Electron Eng. 2012 Apr;100(4):991-1003. doi: 10.1109/JPROC.2011.2182074.
Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.
病理学是一门从事疾病诊断的医学亚专业。对组织进行显微镜检查可揭示相关信息,使病理学家能够做出准确诊断并指导治疗。在过去的一个世纪里,解剖病理学家做出诊断的基本过程相对没有变化,然而信息技术的进步如今在基于图像的诊断和研究应用中提供了重大机遇。病理学在数字技术应用方面落后于其他医疗实践,如数字技术在放射学中已广泛应用。随着生成全切片图像的设备变得更加实用且价格合理,各医疗机构将越来越多地采用这项技术,最终产生的数据量将呈爆炸式增长,迅速超过已海量的放射学影像数据。这些进步伴随着数据管理和存储方面的重大挑战,但它们也带来了新机遇,可通过简化和标准化诊断方法以及揭示疾病机制来改善患者护理。基于计算机的图像分析已在商业诊断系统中可用,但为了充分实现数字病理学在医学发现和患者护理中的益处,图像分析算法仍需进一步发展。在未来几十年里,病理学图像分析将超越简化诊断工作流程和最小化观察者间差异的范畴,开始提供诊断辅助、识别治疗靶点以及预测患者预后和治疗反应。