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Diagn Interv Radiol. 2019 May;25(3):183-188. doi: 10.5152/dir.2019.19125.

Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Author information

1
Clinical Research Directorate Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program National Cancer Institute, NIH, Bethesda, MD, USA.
2
Department of Radiology, İstanbul University İstanbul School of Medicine, İstanbul, Turkey.
3
Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA.
4
Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

Abstract

Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.

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