Introduction: Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.
Areas covered: This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.
Expert opinion: Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.
Keywords: Tissue-based imaging; artificial intelligence; digital pathology and image analysis; multiplexed immunohistochemistry; regulatory body.