Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA.
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontorio, Canada.
Br J Radiol. 2022 Mar 1;95(1131):20210563. doi: 10.1259/bjr.20210563. Epub 2021 Dec 3.
Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.
前列腺癌（PCa）是西方男性最常见的癌症类型。MRI 通过引导活检在 PCa 的诊断中发挥着重要作用。由于 MRI 引导的 PCa 诊断途径具有多步骤的复杂性，因此其诊断性能存在很大差异。使用机器学习（尤其是深度学习）开发人工智能（AI）模型在放射学中发挥着越来越重要的作用。具体而言，对于前列腺 MRI，文献中已经定义了几种 AI 方法，用于前列腺分割、病灶检测和分类，目的是提高诊断性能和观察者间的一致性。在这篇综述文章中，我们总结了 AI 在前列腺 MRI 中的放射学应用。
