人工智能在尿路上皮癌中的现有趋势与应用：一项范围综述

Existing trends and applications of artificial intelligence in urothelial cancer A scoping review.

作者信息

Malik Shamir, Wu Jeremy, Bodnariuc Nicole, Narayana Krishnateja, Gupta Naveen, Malik Mikail, Kwong Jethro C C, Khondker Adree, Johnson Alistair E W, Kulkarni Girish S

机构信息

Temerty Faculty of Medicine, University of Toronto, Toronto, ON , Canada.

Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON , Canada.

出版信息

Can Urol Assoc J. 2023 Nov;17(11):E395-E401. doi: 10.5489/cuaj.8322.

DOI:10.5489/cuaj.8322

PMID:37549345

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10657228/

Abstract

INTRODUCTION

The use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC).

METHODS

Comprehensive searches based on the concepts of "AI" and "urothelial cancer" were conducted in MEDLINE , EMBASE , Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework.

RESULTS

From a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen's kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76.

CONCLUSIONS

The use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

摘要

引言

人工智能（AI）在泌尿外科的应用正越来越受到关注。虽然此前已有关于AI在泌尿外科应用的综述，但很少有人尝试综合现有的关于尿路上皮癌（UC）的文献。

方法

在MEDLINE、EMBASE、科学网和Scopus数据库中，基于“AI”和“尿路上皮癌”的概念进行全面检索。由两名独立的评审员进行研究筛选和数据提取。两名独立的评估者使用预测模型偏倚风险评估工具（PROBAST）和泌尿外科机器学习应用标准化报告（STREAM-URO）框架，对25项研究的随机样本进行研究质量评估。

结果

在对4581项研究的数据库检索中，纳入了227项研究。按研究领域划分，33%聚焦于图像分析，26%聚焦于基因组学，16%聚焦于放射组学，15%聚焦于临床病理学。主题内容分析确定了所采用的AI模型和特征提取变量的定性趋势。只有19%的研究将AI模型的性能与非AI方法进行了比较。所有入选研究在分析方面均显示出高偏倚风险，Cohen's kappa（k）=0.68表示总体存在问题。入选研究符合STREAM-URO项目的66%，k=0.76。