He Chunlei, Xu Hui, Yuan Enyu, Ye Lei, Chen Yuntian, Yao Jin, Song Bin
Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China.
Insights Imaging. 2024 Aug 1;15(1):185. doi: 10.1186/s13244-024-01780-y.
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).
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.
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.
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.
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.
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.
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）、影像组学质量评分（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。
我们检索了截至2023年10月30日的Medline、Embase、Web of Science和Cochrane图书馆数据库。使用CLAIM、RQS和PROBAST对符合条件的研究进行评估。还计算了这些模型对MIBC的合并敏感性、特异性和诊断性能。
纳入了21项包含4256例患者的研究，其中17项研究用于定量统计分析。CLAIM研究的依从率在52.5%至75%之间，中位数为64.1%。每项研究的RQS分数在2.78%至50%之间，中位数为30.56%。所有模型的总体偏倚风险均被评为高。计算机断层扫描的合并曲线下面积为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），影像组学为0.89（95%CI 0.86 - 0.92），深度学习为0.91（95%CI 0.88 - 0.93）。
尽管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显示出有前景的性能，但报告质量和方法学质量普遍较低，存在较高的偏倚风险。
人工智能可能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有助于识别肌层浸润性膀胱癌。当前研究的报告质量低、方法学质量低且偏倚风险高。未来的研究可以集中在更大的样本量以及更透明地报告病理评估、模型解释以及失败和敏感性分析。
