AI-RANO Part 1: AI for Diagnosis, Prediction and Response Assessment in Neuro-Oncology
This Lancet Oncology policy review critically assesses the current landscape of AI tools in neuro-oncology, covering diagnostic models for genomic markers, predictive models of treatment response, and differentiation of true progression from treatment-related changes. The authors highlight the growing role of radiomics and deep learning while identifying key barriers to clinical translation, including generalisability and validation gaps.
The original study
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.
- Authors
- Villanueva-Meyer JE, Bakas S, Tiwari P, Lupo JM, Calabrese E, Davatzikos C, et al.
- Journal
- The Lancet. Oncology
- Type
- Journal Article, Review
- PMID
- 39481414
Original abstract
The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.