RANO review maps AI clinical readiness across neuro-oncology domains

AI applications closest to neuro-oncology clinical use include imaging segmentation and quantification, AI-assisted image reconstruction, DNA methylation-based classification, and workflow optimization; radiogenomics and other promising tools still need prospective validation.

• This joint Response Assessment in Neuro-Oncology working group review spans six domains: neuroimaging, neuropathology, laboratory medicine, neurosurgery, drug discovery, and clinical trials.
• Radiogenomics, whole-slide imaging molecular prediction, AI-guided surgical targeting, liquid biopsy interpretation, AI-guided drug discovery, digital twins, and in silico trials remain promising but require stronger prospective validation.
• Key challenges include limited large diverse datasets, variability in data acquisition and reference standards, and limited external validation; the authors emphasize standardization, reproducibility, multi-institutional validation, and human oversight.

Automated summary

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