Artificial intelligence applications in prostate cancer

被引：28

作者：

Baydoun, Atallah ^{[1
]}

Jia, Angela Y. Y. ^{[1
]}

Zaorsky, Nicholas G. G. ^{[1
]}

Kashani, Rojano ^{[1
]}

Rao, Santosh ^{[2
]}

Shoag, Jonathan E. E. ^{[3
]}

Vince, Randy A. A. ^{[3
]}

Bittencourt, Leonardo Kayat ^{[4
]}

Zuhour, Raed ^{[1
]}

Price, Alex T. T. ^{[1
]}

Arsenault, Theodore H. H. ^{[1
]}

Spratt, Daniel E. E. ^{[1
]}

机构：

[1] Case Western Reserve Univ, Univ Hosp Seidman Canc Ctr, Dept Radiat Oncol, Cleveland, OH 44106 USA

[2] Case Western Reserve Univ, Univ Hosp Seidman Canc Ctr, Dept Med, Cleveland, OH 44106 USA

[3] Case Western Reserve Univ, Univ Hosp Seidman Canc Ctr, Dept Urol, Cleveland, OH 44106 USA

[4] Case Western Reserve Univ, Univ Hosp Cleveland Med Ctr Ctr, Dept Radiol, Cleveland, OH 44106 USA

来源：

PROSTATE CANCER AND PROSTATIC DISEASES | 2024年 / 27卷 / 01期

基金：

美国国家卫生研究院;

关键词：

MACHINE LEARNING APPLICATIONS; SYNTHETIC CT; RADIATION; BIOPSIES; DIAGNOSIS; SYSTEM; CLASSIFICATION; RADIOTHERAPY; VALIDATION; BIOMARKERS;

D O I：

10.1038/s41391-023-00684-0

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

引用

页码：37 / 45

页数：9

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