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NCT Number: NCT07814872

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

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.

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Key information

Conditions

Neoplasms (Cancer / Tumors) Adnexal Diseases Breast Diseases Breast Neoplasms Colonic Diseases Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Endocrine Gland Neoplasms Endocrine System Diseases Endometrial Neoplasms Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Gastrointestinal Diseases Gastrointestinal Neoplasms Genital Diseases Genital Diseases, Female Genital Neoplasms, Female Glioma Gonadal Disorders Head and Neck Neoplasms Intestinal Diseases Intestinal Neoplasms Kidney Diseases Kidney Neoplasms Lung Diseases Lung Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Germ Cell and Embryonal Neoplasms, Glandular and Epithelial Neoplasms, Nerve Tissue Neoplasms, Neuroepithelial Neuroectodermal Tumors Ovarian Diseases Ovarian Neoplasms Pancreatic Diseases Pancreatic Neoplasms Rectal Diseases Respiratory Tract Diseases Respiratory Tract Neoplasms Skin Diseases Skin and Connective Tissue Diseases Stomach Diseases Stomach Neoplasms Thoracic Neoplasms Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms Uterine Diseases Uterine Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

Harvard Medical School

Boston, Massachusetts, 02115, United States

Location contact

Chi-Kang Pai

CONTACT

[email protected]

6172334924

Kun-Hsing Yu

PRINCIPAL_INVESTIGATOR

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. 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.

Secondary outcomes

  1. 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.

  2. 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.

  3. 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.

  4. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Chi-Kang Pai

CONTACT

[email protected]

6172334924

Sponsors and collaborators

Lead sponsor

Harvard Medical School (HMS and HSDM)

Other

Registry information

Acronym: FATHOM

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Sep 11, 2026
Registry last updated
Sep 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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