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

September 6, 2026 updated by: Kun-Hsing Yu, Harvard Medical School (HMS and HSDM)

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.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

WHAT IS REGISTERED. This study evaluates screening policies generated by autonomous research agents using archived pathology slides and existing molecular marker profiles. The study does not prospectively enroll or contact patients. The investigators register the clinical trial corpus that the agents may read, the trial-record cutoff date, the information that the agents may access, the rules governing their work, and the methods used to score their policies.

THE AGENTS. Each agent may access the registered clinical trial corpus, published literature, out-of-fold performance estimates for its own classifiers, and any development data identified in its manifest. The agent receives no results from any sealed evaluation cohort. For visual recognition, each agent uses pathology AI models as feature extractors and trains classifiers on the extracted features. The agent determines which molecular markers to model, which model to use, how to set each operating threshold, and whether to deploy the resulting policy. The agent records each decision and its rationale.

THE MANIFEST. Each agent run produces a timestamped manifest listing every policy generated and each policy's final deployment decision. On the study start date, the investigators designate the autonomous-agent approach and its comparators for the primary evaluation.

TRIAL DEMAND CUTOFF. Trials first posted before January 1, 2026, define retrospective trial demand. Trials first posted on or after January 1, 2026, are used for the temporal generalization evaluation.

SEALED ANALYSIS RULE. Each policy result corresponds to an evaluation scenario, defined as one cohort paired with one molecular marker. For each scenario, the study logs and publishes the date on which investigators first compare any model output with the ground-truth marker result.

COHORTS. The study uses archived institutional and consortium cohorts containing routine diagnostic H&E slides linked to molecular profiles. No clinician uses model output from this study to make patient-care decisions.

Study Type

Observational

Enrollment (Estimated)

30000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • United States
    • Massachusetts
      • Boston, Massachusetts, United States, 02115
        • Harvard Medical School
        • Contact:
        • Principal Investigator:
          • Kun-Hsing Yu

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patient records from archived, multi-institutional cohorts of patients with histologically confirmed cancers, relevant molecular profiling results, and at least one diagnostic hematoxylin and eosin (H&E) whole-slide image. For the evaluation of a given policy, patients whose slides were used to train that policy's classifier are excluded. No patients are enrolled or contacted; enrollment counts refer to patient records analyzed.

Description

Inclusion Criteria:

  • 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 Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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)
An evaluation scenario consists of one molecular marker evaluated in one study cohort. For each prespecified scenario, the study assesses whether the AI system generates a policy that either prioritizes patients more likely to carry the marker for confirmatory testing or identifies patients who may safely be spared testing, compared with testing everyone. The outcome is the proportion of scenarios in which the policy meets these performance criteria.
Periprocedural (at the time of pathology slide evaluation)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Per-scenario performance of each AI-generated deployment policy
Time Frame: Periprocedural (at the time of pathology slide evaluation)
For each evaluation scenario, the study reports sensitivity, negative predictive value, positive predictive value, the proportion of patients spared confirmatory testing, and the applicable enrichment or depletion ratio with its confidence interval.
Periprocedural (at the time of pathology slide evaluation)
Temporal generalizability for trials first posted on or after January 1, 2026
Time Frame: Periprocedural (at the time of pathology slide evaluation)
Among trials first posted on or after January 1, 2026 that require a molecular biomarker for enrollment, the study evaluates: (1) the proportion of biomarkers and trials for which the agentic AI screening approach is useful; and (2) the estimated number of patients who would benefit from AI-guided screening compared with universal molecular testing. Estimates are based on model performance and biomarker prevalence observed in the evaluation cohorts.
Periprocedural (at the time of pathology slide evaluation)
Temporal performance for trials first posted on or before December 31, 2025
Time Frame: Periprocedural (at the time of pathology slide evaluation)
Among trials first posted on or before December 31, 2025, that require a molecular biomarker for enrollment, the study evaluates: (1) the proportion of biomarkers and trials for which the agentic AI screening approach is useful; and (2) the estimated number of patients who would benefit from AI-guided screening compared with universal molecular testing. Estimates are based on model performance and biomarker prevalence observed in the evaluation cohorts.
Periprocedural (at the time of pathology slide evaluation)
Proportion of evaluation scenarios in which a non-default AI-generated deployment policy demonstrates effective screening enrichment or rule-out performance
Time Frame: Periprocedural (at the time of pathology slide evaluation)
Among scenarios in which the deployed policy is not the default strategy of testing everyone, the study assesses whether the AI system generates a policy that either prioritizes patients more likely to carry the marker for confirmatory testing or identifies patients who may safely be spared testing, compared with testing everyone. The outcome is the proportion of these scenarios in which the policy meets these performance criteria.
Periprocedural (at the time of pathology slide evaluation)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Harvard Medical School (HMS and HSDM)

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

September 1, 2026

Primary Completion (Estimated)

November 1, 2026

Study Completion (Estimated)

December 1, 2026

Study Registration Dates

First Submitted

September 6, 2026

First Submitted That Met QC Criteria

September 6, 2026

First Posted (Actual)

September 11, 2026

Study Record Updates

Last Update Posted (Actual)

September 11, 2026

Last Update Submitted That Met QC Criteria

September 6, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • FATHOM

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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