Molecular Imaging Branch, National Cancer Institute, National Institutes of Health Bethesda, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA.
Eur Radiol Exp. 2022 Jul 31;6(1):33. doi: 10.1186/s41747-022-00287-9.
The advent of precision medicine, increasing clinical needs, and imaging availability among many other factors in the prostate cancer diagnostic pathway has engendered the utilization of artificial intelligence (AI). AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate segmentation, anatomically segmenting cancer suspicious foci, detecting and differentiating clinically insignificant cancers from clinically significant cancers on a voxel-level, and classifying entire lesions into Prostate Imaging Reporting and Data System categories/Gleason scores. Multiple studies in all these areas have shown many promising results approximating accuracies of radiologists. Despite this flourishing research, more prospective multicenter studies are needed to uncover the full impact and utility of AI on improving radiologist performance and clinical management of prostate cancer. In this narrative review, we aim to introduce emerging medical imaging AI paper quality metrics such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification.
精准医学的出现、不断增长的临床需求以及前列腺癌诊断途径中成像技术的可用性等诸多因素，催生了人工智能（AI）的应用。人工智能在前列腺癌诊断途径的每一个步骤都具有广泛的潜在应用，包括分类/改善前列腺多参数磁共振图像质量、前列腺分割、解剖分割癌可疑焦点、在体素水平上检测和区分临床意义不显著的癌症与临床意义显著的癌症，以及将整个病变分类为前列腺影像报告和数据系统（PI-RADS）类别/格里森评分。在所有这些领域的多项研究都显示出了许多有前途的结果，其准确性可与放射科医生相媲美。尽管这项研究蓬勃发展，但仍需要更多的前瞻性多中心研究来揭示人工智能在提高放射科医生的表现和前列腺癌的临床管理方面的全部影响和效用。在这篇叙述性综述中，我们旨在介绍新兴的医学影像人工智能论文质量指标，如医学影像人工智能检查表（CLAIM）和领域加权引文影响力（FWCI），并深入研究一些用于分割、检测和分类的顶级人工智能模型。
