A Preregistered Multi-Cohort Evaluation of the FATHOM AI System for Molecular Testing Prioritization to Support Clinical Trial Enrollment

Part of paid clinical trials in Boston, Massachusetts.

Sponsor
Harvard Medical School (HMS and HSDM)
Study ID
NCT07814872
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Not accepted

Study Details

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. Its agents read registered clinical trial records, identify molecular markers used as enrollment criteria, build prediction models using pathology foundation models, select the individual models or model combinations that best meet prespecified criteria, set their decision thresholds, and determine whether to deploy them. Together, a prediction model, its decision threshold, and the decision to deploy it constitute an AI prediction policy. Before FATHOM runs, the investigators preregister the clinical trial records that its agents may read, the cutoff date that defines which trial information they may use, the rules governing the agents, and the analysis plan. The system timestamps and locks each policy immediately after an agent produces it. The investigators then apply the policies to archived patient slides and compare their predictions with existing molecular marker results. The primary outcome is the proportion of prespecified evaluation scenarios in which an agent-generated policy, compared with universal molecular testing, either enriches the population selected for confirmatory testing with marker-positive patients or safely spares patients from confirmatory testing while meeting prespecified performance criteria. This study analyzes existing pathology images and clinical trial records only. It does not enroll or contact patients, influence patient care, or affect participation in any clinical trial.

Key Dates

First listed
Sep 11, 2026
Start date
Sep 30, 2026
Status verified
Sep 2026
Primary completion
Nov 30, 2026
Completion
Dec 31, 2026

Study Design

Enrollment
30,000 participants (estimated)

Arms

  • Arm: Archived evaluation cohorts
    Patient records and data from archived multi-institutional cohorts with routine H\&E whole-slide images and molecular profiles. No intervention is assigned, and no patient is contacted.

Primary Outcome Measure

Proportion of prespecified evaluation scenarios in which an AI-generated deployment policy demonstrates effective screening enrichment or rule-out performance [ Time Frame: Periprocedural (at the time of pathology slide evaluation) ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Harvard Medical SchoolBostonMassachusetts02115
Chi-Kang Pai
[email protected]
6172334924
Kun-Hsing Yu (PRINCIPAL_INVESTIGATOR)

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