Department of Radiology, Charité University Hospital, Augustenburger Platz 1, 13354, Berlin, Germany.
Berlin Institute of Health, Berlin, Germany.
Eur Radiol. 2021 Dec;31(12):9567-9578. doi: 10.1007/s00330-021-08021-6. Epub 2021 May 15.
Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.
人工智能的发展对于成功部署全社区范围的、基于 MRI 的前列腺癌诊断至关重要。人工智能系统应确保在避免活检的主要益处的同时,保持一致的高特异性,在各种疾病流行率下。由于目前所有用于前列腺癌检测的人工智能/计算机辅助检测系统都是实验性的,因此仍需要进行多项开发工作,以实现这一愿景。最初的工作需要集中在开发系统作为诊断支持辅助工具,以便将其结果整合到放射科医生的工作流程中,包括融合活检的腺体和目标勾画任务。开发人工智能系统作为临床决策工具将需要更多的努力。后者包括更大的多中心、多供应商数据集,其中考虑了不同诊断环境、疾病流行率、患者偏好和临床环境分层的患者的不同需求。基于人工智能的、稳健的标准操作程序将增加患者和支付方的信心,从而能够更广泛地采用 MRI 指导的方法进行前列腺癌诊断。关键点:
人工智能系统需要确保在各种疾病流行率下,通过避免活检来实现一致的高特异性。
最初的工作重点是开发系统作为诊断支持辅助工具,用于勾画任务,以便将其整合到放射科医生的工作流程中,以支持 MRI 指导的活检。
决策支持工具需要更多的工作,包括多中心、多供应商的研究,其中还需要定义临床需求、疾病流行率、患者偏好和临床环境。