AI-RANO Part 2: Standardisation and Validation Recommendations for AI in Neuro-Oncology Response Assessment
This companion policy review to AI-RANO Part 1 provides concrete recommendations for standardising and validating AI approaches in neuro-oncology, addressing reproducibility, generalisability and regulatory considerations. The authors propose a pathway toward trustworthy AI that can enable next-generation clinical trials, with emphasis on open-source tools, standardised imaging protocols and robust validation frameworks.
The original study
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.
- Authors
- Bakas S, Vollmuth P, Galldiks N, Booth TC, Aerts HJWL, Bi WL, et al.
- Journal
- The Lancet. Oncology
- Type
- Journal Article, Review
- PMID
- 39481415
Original abstract
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.