Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.
- pathology department
- diagnostic screening programs
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Handling editor Runjan Chetty.
Contributors All authors contributed to the design and implementation of the review and to the writing of the manuscript.
Funding Funding is acknowledged from the Irish Cancer Society Collaborative Cancer Research Centre BREASTPREDICT [grant number CCRC13GAL], as well as Science Foundation Ireland (SFI) under the Investigator Programme OPTi-PREDICT [grant number 15/IA/3104] and the Strategic Research Programme Precision Oncology Ireland [grant number 18/SPP/3522]. Funding is also acknowledged from Enterprise Ireland (EI) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Slodowska-Curie grant agreement number 713654. Funding is acknowledged from Enterprise Ireland’s Disruptive Technologies Innovation Fund 2021.
Competing interests None declared.
Provenance and peer review Commissioned; internally peer reviewed.
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