Prostate cancer (PCa) is a cancer with a broad spectrum of biological behavior and it is a
heterogeneous nature. In order to prevent overdiagnosis and overtreatment, and to detect clinically
significant PCa, standardized scoring and grading systems are used in imaging and pathological
examinations. However, reproducibility and agreement between readers in these diagnostic stages,
which require experience, are low. Promising results have been achieved by integrating artificial
intelligence (AI)-based applications into the diagnosis and management of PCa. In radiological
and pathological imaging, computer-aided diagnostic tools have increased clinical efficiency and
achieved diagnostic accuracy comparable to that of experienced healthcare professionals. This
review provides an overview of AI applications used in radiological imaging, prostate biopsy, and
histopathological examination in the diagnosis of PCa.
Abstract
Prostate cancer (PCa) is a cancer with a broad spectrum of biological behavior and it is a
heterogeneous nature. In order to prevent overdiagnosis and overtreatment, and to detect clinically
significant PCa, standardized scoring and grading systems are used in imaging and pathological
examinations. However, reproducibility and agreement between readers in these diagnostic stages,
which require experience, are low. Promising results have been achieved by integrating artificial
intelligence (AI)-based applications into the diagnosis and management of PCa. In radiological
and pathological imaging, computer-aided diagnostic tools have increased clinical efficiency and
achieved diagnostic accuracy comparable to that of experienced healthcare professionals. This
review provides an overview of AI applications used in radiological imaging, prostate biopsy, and
histopathological examination in the diagnosis of PCa.