ideasIdea

AI-assisted central imaging reads to cut endpoint cost and variability

Measuring tumours on scans for trials is slow, expensive and inconsistent between readers. Software that measures lesions and flags changes, checked by a radiologist, could make trial endpoints cheaper and more reliable.

Validated AI segmentation and lesion-tracking tools perform RECIST 1.1 measurements with radiologist adjudication only on flagged discordances, replacing dual blinded independent central review. Performance is locked and validated against historical BICR-adjudicated trial datasets before use; regulators accept the tool under a qualification pathway.

Hypothesis

AI-assisted central reads will match BICR progression dates within one assessment interval in more than 95 percent of cases, at less than half the cost and with lower inter-reader variance, without changing trial conclusions on re-analysis.

Rationale

Central imaging review is one of the largest fixed costs in phase 3 oncology trials and reader disagreement drives discordance between local and central PFS. Segmentation models now perform at expert level on common lesion types.

What would test it

Re-read the imaging archives of three completed phase 3 trials with the AI-assisted workflow and compare PFS hazard ratios and progression dates with the original BICR.

Maturity

early clinical

Who has to act

engineering

Cost to try

Medium ($1M to $50M)

Years to first evidence

Bottlenecks it attacks

Trial design, endpoints and cost · A phase 3 trial takes years and hundreds of millions of dollars, and often answers a question that has already moved on.
AI that is built but not validated or deployed · Thousands of cancer AI models are published; a handful are in clinical use, and fewer have shown they help patients.

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