This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging–Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.
本综述聚焦于多参数磁共振成像（mpMRI）在膀胱成像中的原理、应用及性能表现。源自mpMRI的定量成像生物标志物（QIBs）在肿瘤学应用中日渐广泛，涵盖肿瘤分期、预后评估及治疗反应监测等领域。为规范mpMRI的采集与解读，专家小组制定了膀胱影像报告与数据系统（VI-RADS）。多项研究证实该系统在鉴别膀胱癌肌层浸润性方面具有标准化优势及较高的观察者间一致性，支持其在常规临床实践中的应用。VI-RADS评分标准序列包括解剖成像（如T2加权图像）与功能成像（如扩散加权MRI和动态对比增强MRI）。通过分析DW-MRI与DCE-MRI数据获得的生理学QIBs，以及从mpMRI图像中提取的影像组学特征，在膀胱癌诊疗中发挥着重要作用。本文同时探讨了当前用于分析mpMRI数据的人工智能工具发展现状及其对膀胱成像领域的潜在影响。人工智能架构常基于卷积神经网络构建，专注于特定细分任务。AI的应用可显著优化膀胱成像临床工作流程，例如以自动分割工具替代耗时且存在观察者差异的手动肿瘤勾画。预计mpMRI与人工智能技术的结合将推动膀胱癌诊疗向个体化精准管理方向发展。
Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies
肿瘤(癌症)患者之家