The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction.

IF 4.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Insights into Imaging Pub Date : 2024-08-01 DOI:10.1186/s13244-024-01780-y

Chunlei He, Hui Xu, Enyu Yuan, Lei Ye, Yuntian Chen, Jin Yao, Bin Song

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引用次数: 0

Abstract

Purpose: To evaluate the diagnostic performance of image-based artificial intelligence (AI) studies in predicting muscle-invasive bladder cancer (MIBC). (2) To assess the reporting quality and methodological quality of these studies by Checklist for Artificial Intelligence in Medical Imaging (CLAIM), Radiomics Quality Score (RQS), and Prediction model Risk of Bias Assessment Tool (PROBAST).

Materials and methods: We searched Medline, Embase, Web of Science, and The Cochrane Library databases up to October 30, 2023. The eligible studies were evaluated using CLAIM, RQS, and PROBAST. Pooled sensitivity, specificity, and the diagnostic performances of these models for MIBC were also calculated.

Results: Twenty-one studies containing 4256 patients were included, of which 17 studies were employed for the quantitative statistical analysis. The CLAIM study adherence rate ranged from 52.5% to 75%, with a median of 64.1%. The RQS points of each study ranged from 2.78% to 50% points, with a median of 30.56% points. All models were rated as high overall ROB. The pooled area under the curve was 0.85 (95% confidence interval (CI) 0.81-0.88) for computed tomography, 0.92 (95% CI 0.89-0.94) for MRI, 0.89 (95% CI 0.86-0.92) for radiomics and 0.91 (95% CI 0.88-0.93) for deep learning, respectively.

Conclusion: Although AI-powered muscle-invasive bladder cancer-predictive models showed promising performance in the meta-analysis, the reporting quality and the methodological quality were generally low, with a high risk of bias.

Critical relevance statement: Artificial intelligence might improve the management of patients with bladder cancer. Multiple models for muscle-invasive bladder cancer prediction were developed. Quality assessment is needed to promote clinical application.

Key points: Image-based artificial intelligence models could aid in the identification of muscle-invasive bladder cancer. Current studies had low reporting quality, low methodological quality, and a high risk of bias. Future studies could focus on larger sample sizes and more transparent reporting of pathological evaluation, model explanation, and failure and sensitivity analyses.

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基于图像的人工智能预测肌肉浸润性膀胱癌的准确性和质量。

目的：评估基于图像的人工智能（AI）研究在预测肌层浸润性膀胱癌（MIBC）方面的诊断性能；（2）通过医学影像人工智能检查表（CLAIM）、放射组学质量评分（RQS）和预测模型偏倚风险评估工具（PROBAST）评估这些研究的报告质量和方法学质量。材料与方法：我们检索了 Medline、Embase、Web of Science 和 Cochrane Library 数据库，检索时间截至 2023 年 10 月 30 日。使用 CLAIM、RQS 和 PROBAST 对符合条件的研究进行了评估。还计算了这些模型对MIBC的汇总灵敏度、特异性和诊断性能：结果：共纳入 21 项研究，包含 4256 名患者，其中 17 项研究用于定量统计分析。CLAIM研究的依从率从52.5%到75%不等，中位数为64.1%。每项研究的 RQS 分数从 2.78% 到 50% 不等，中位数为 30.56%。所有模型的总体 ROB 都很高。计算机断层扫描的集合曲线下面积为0.85（95%置信区间（CI）0.81-0.88），磁共振成像为0.92（95% CI 0.89-0.94），放射组学为0.89（95% CI 0.86-0.92），深度学习为0.91（95% CI 0.88-0.93）：虽然人工智能驱动的肌层浸润性膀胱癌预测模型在荟萃分析中表现出良好的性能，但报告质量和方法学质量普遍较低，存在较高的偏倚风险：人工智能可改善膀胱癌患者的管理。已开发出多种肌层浸润性膀胱癌预测模型。需要进行质量评估以促进临床应用：基于图像的人工智能模型可帮助识别肌层浸润性膀胱癌。目前的研究报告质量低、方法学质量低、偏倚风险高。未来的研究可侧重于更大的样本量以及更透明的病理评估报告、模型解释、失败分析和敏感性分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

7.30

自引率

4.30%

发文量

182

审稿时长

13 weeks

期刊介绍： Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.