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.
Department of Radiology, İstanbul University İstanbul School of Medicine, İstanbul, Turkey.
Diagn Interv Radiol. 2019 May;25(3):183-188. doi: 10.5152/dir.2019.19125.
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.
病理分级在前列腺癌风险分层和治疗选择中起着关键作用，传统上通过对整个前列腺进行系统的核心针活检进行评估。多参数磁共振成像（mpMRI）已成为检测和定位前列腺癌的成熟临床工具。然而，病理和影像学评估在读者之间的重复性都较差。人工智能（AI）方法在辅助基于成像的任务检测和评估方面显示出前景，这取决于高质量训练集的编纂。本综述概述了人工智能在前列腺癌 mpMRI 和数字病理学中的最新应用进展，这些进展可通过联合放射学-病理学评估实现对疾病的高级特征描述。
