NCT07814872
A Preregistered Multi-Cohort Evaluation of the FATHOM AI System for Molecular Testing Prioritization to Support Clinical Trial Enrollment
Not Yet Recruiting · Not specified · Harvard Medical School (HMS and HSDM) · registry updated 2026-09-11
Inclusion and exclusion lines below are quoted from ClinicalTrials.gov. No match score is shown, because a score needs a person's age, biomarkers, and treatment dates. Confirm the record with the study team.
Many clinical trials evaluating cancer treatments require patients to undergo testing for specific molecular markers as part of eligibility screening, typically using immunohistochemistry or sequencing. Because relatively few patients may carry a required marker, trial investigators often test large numbers of patients to identify the few who may ultimately qualify for enrollment. Pathology laboratories routinely produce hematoxylin-and-eosin (H\&E) slides during cancer diagnosis. Pathology foundation models-large neural networks pretrained on millions of histology images-have shown promise in predicting molecular characteristics from these slides. Researchers can use these models to build classifiers that predict specific molecular markers and prioritize patients for confirmatory testing. This study evaluates FATHOM (Facilitating Accrual through Tumor Histology and Omics Matching), an autonomous research system powered by large multimodal models....
Inclusion
- Patients with a histologically confirmed cancer
- Availability of relevant molecular profiling results
- At least one diagnostic hematoxylin and eosin (H\&E) whole-slide image
Exclusion
- Poor-quality or unreadable slides, assessed independently of model output
- Patients whose slides were used to train a policy's classifier, for that policy's evaluation