Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

中文翻译：

---

用于神经肿瘤学反应评估的人工智能 （AI-RANO），第 2 部分：标准化、验证和良好临床实践的建议


技术进步使计算方法在各个领域（包括医疗保健）的扩展研究、开发和应用成为可能。正在不断探索越来越多的诊断、预测、预后和监测生物标志物，以改善神经肿瘤学的临床决策。这些进步描述了人工智能 （AI） 算法的日益普及，包括放射组学的使用。然而，AI 的广泛适用性和临床转化受到对通用性、可重复性、可扩展性和验证性的担忧的限制。本政策审查旨在作为医疗保健中 AI 方法的标准化和良好临床实践的主要建议资源，尤其是在神经肿瘤学中。为此，我们在对影响此类计算方法的因素的研究以及促进这些目标的公开开源数据和计算软件工具中，研究了人工智能在神经肿瘤学反应评估中的可重复性、再现性和稳定性。讨论了这些方法的标准化和验证途径，以可信的 AI 支持下一代临床试验。最后，我们对 AI 驱动的神经肿瘤学的未来进行了展望。