A review of artificial. intelligence in prostate cancer detection on imaging

被引：38

作者：

Bhattacharya, Indrani ^{[1
,2
]}

Khandwala, Yash S. ^{[2
]}

Vesal, Sulaiman ^{[2
]}

Shao, Wei ^{[1
]}

Yang, Qianye ^{[3
,4
]}

Soerensen, Simon J. C. ^{[2
,5
]}

Fan, Richard E. ^{[2
]}

Ghanouni, Pejman ^{[1
,2
]}

Kunder, Christian A. ^{[6
]}

Brooks, James D. ^{[2
]}

Hu, Yipeng ^{[3
,4
]}

Rusu, Mirabela ^{[1
]}

Sonn, Geoffrey A. ^{[1
,2
]}

机构：

[1] Stanford Univ, Dept Radiol, Sch Med, 1201 Welch Rd, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Urol, Sch Med, Stanford, CA 94305 USA

[3] UCL, Ctr Med Image Comp, London, England

[4] UCL, Wellcome EPSRC Ctr Intervent & Surg Sci, London, England

[5] Stanford Univ, Dept Epidemiol & Populat Hlth, Sch Med, Stanford, CA USA

[6] Stanford Univ, Dept Pathol, Sch Med, Stanford, CA USA

来源：

THERAPEUTIC ADVANCES IN UROLOGY | 2022年 / 14卷

基金：

美国国家卫生研究院;

关键词：

artificial intelligence; histopathology images; magnetic resonance imaging; prostate cancer diagnosis; registration; ultrasound images; SHEAR-WAVE ELASTOGRAPHY; WHOLE-MOUNT HISTOLOGY; MULTI-PARAMETRIC MRI; PI-RADS V2; MAGNETIC-RESONANCE; COMPUTED-TOMOGRAPHY; ULTRASOUND FUSION; MICRO-ULTRASOUND; BIPARAMETRIC MRI; SEGMENTATION;

D O I：

10.1177/17562872221128791

中图分类号：

R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];

学科分类号：

1002 ; 100201 ;

摘要：

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

引用

页数：31

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