A new era: artificial intelligence and machine learning in prostate cancer
Nature Reviews Urology 15 May 2019
Nature Reviews Urology 15 May 2019
- Machine learning methods are used to identify genes or groups of genes for which expression specificity to predict outcomes of prostate cancer is high and could be used for screening, developing diagnostic tools, determining optimal individualized treatment and producing targeted drug regimens.
Key points
Applications of machine learning (ML) to prostate cancer care are rapidly growing owing to the many technological platforms involved in its diagnosis, prognosis and treatment.
In diagnostic imaging, ML is applied to perform low-level image analysis tasks such as prostate segmentation and fusion of different modalities (for example MRI, CT and ultrasonography) and high-level inference and prediction tasks such as prostate cancer detection and characterization.
ML algorithms are able to enhance prostate cancer treatment by augmenting the surgeon’s display with information such as cancer localization during robotic procedures and other image-guided interventions and could be used towards autonomous manipulation of tools for assistance in the operating room.
Computer-assisted diagnosis of prostate cancer in histopathological slides could be achieved by ML in order to optimize accuracy, reproducibility and throughput and to further enhance health-care delivery by enabling the use of customized precision-care pathways.
ML methods are used to identify genes or groups of genes for which expression specificity to predict outcomes of prostate cancer is high and could be used for screening, developing diagnostic tools, determining optimal individualized treatment and producing targeted drug regimens.
Collaboration between urologists, data scientists, computer researchers and engineers is required to ensure that artificial intelligence (AI)-based decision-support applications are properly trained, operated and regulated.
