Bhargava Rohit, Madabhushi Anant
Departments of Bioengineering, Chemical and Biomolecular Engineering, Electrical and Computer Engineering, Mechanical Science and Engineering, and Chemistry, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801; email:
Center for Computational Imaging and Personalized Diagnostics; Departments of Biomedical Engineering, Urology, Pathology, Radiology, Radiation Oncology, General Medical Sciences, Electrical Engineering, and Computer Science; and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106; email:
Annu Rev Biomed Eng. 2016 Jul 11;18:387-412. doi: 10.1146/annurev-bioeng-112415-114722.
Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.
病理学对于疾病与发育研究以及临床决策至关重要。一百多年来，病理学实践一直涉及由经过培训的人员使用光学显微镜分析染色薄组织切片的图像。技术进步正推动这一模式朝着数字病理学（DP）发生重大变革。病理学的数字化转型不仅限于图像的记录、存档和检索，还提供了新的计算工具，以促进精准医学做出更优决策。首先，我们讨论数字病理学中计算图像分析和成像仪器方面的一些新兴创新。其次，我们讨论病理学中的分子对比。传统上，分子数字病理学一直是使用分子特异性染料的病理学扩展。无标记光谱图像正迅速成为另一个重要的信息来源，我们将描述这一发展的益处和潜力。第三，我们描述多模态数字病理学，它由计算算法实现，结合了结构病理学和分子病理学的最佳特性。最后，我们提供远程病理学、教育和精准医学应用领域的示例。我们通过讨论该领域的挑战和新出现的机遇来结束本文。
