Neoplasms (Cancer / Tumors)FATHOMNCT07814872
A Preregistered Multi-Cohort Evaluation of the FATHOM AI System for Molecular Testing Prioritization to Support Clinical Trial Enrollment
An observational study of Neoplasms (Cancer / Tumors) and Colorectal Neoplasms, run by Harvard Medical School (HMS and HSDM) at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Neoplasms (Cancer / Tumors), Colorectal Neoplasms, Lung Neoplasms
- How many people
- 30,000 participants
- Where
- 1 siteUnited States
- Study dates
- Sep 2026to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- Patients with a histologically confirmed cancer
- Availability of relevant molecular profiling results
- At least one diagnostic hematoxylin and eosin (H\&E) whole-slide image
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site
Harvard Medical School
Boston, Massachusetts, 02115
Chi-Kang Pai, 6172334924, chikang_pai@fas.harvard.edu
Kun-Hsing Yu
About this study
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.
Read more
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.
Read the 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.
- Conditions
- Neoplasms (Cancer / Tumors), Colorectal Neoplasms, Lung Neoplasms, Endometrial Neoplasms, Breast Neoplasms, Pancreatic Neoplasms
- Keywords
- Deep Learning, Agentic AI, Digital Pathology, Artificial Intelligence, Multi-omics, Biomarkers, Cancer
- Medical subject headings
- Neoplasms, Colorectal Neoplasms, Lung Neoplasms, Endometrial Neoplasms, Breast Neoplasms, Pancreatic Neoplasms, Head and Neck Neoplasms, Kidney Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
Proportion of prespecified evaluation scenarios in which an AI-generated deployment policy demonstrates effective screening enrichment or rule-out performance
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.
Measured: Periprocedural (at the time of pathology slide evaluation)
Secondary outcomes (4)
Per-scenario performance of each AI-generated deployment policy
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.
Measured: Periprocedural (at the time of pathology slide evaluation)
Temporal generalizability for trials first posted on or after January 1, 2026
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.
Measured: Periprocedural (at the time of pathology slide evaluation)
Temporal performance for trials first posted on or before December 31, 2025
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.
Measured: 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
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.
Measured: Periprocedural (at the time of pathology slide evaluation)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Sep 2026
- Study startSep 2026 (estimated)
- Primary completionNov 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Neoplasms (Cancer / Tumors)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 30,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Harvard Medical School (HMS and HSDM)
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kun-Hsing Yu, Harvard Medical School (HMS and HSDM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07814872
- Sponsor's protocol ID
- FATHOM
- Acronym
- FATHOM
- Status last verified
- Sep 2026
- First submitted
- 6 Sep 2026
- Last updated
- 11 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Preregistered Multi-Cohort Evaluation of the FATHOM AI System for Molecular Testing Prioritization to Support Clinical Trial Enrollment
- ClinicalTrials.gov ID
- NCT07814872
- Acronym / sponsor ID
- FATHOM / FATHOM
- Conditions
- Neoplasms (Cancer / Tumors), Colorectal Neoplasms, Lung Neoplasms, Endometrial Neoplasms, Breast Neoplasms, Pancreatic Neoplasms
- Keywords
- Deep Learning, Agentic AI, Digital Pathology, Artificial Intelligence, Multi-omics, Biomarkers, Cancer
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 30,000 participants (estimated)
- Lead sponsor
- Harvard Medical School (HMS and HSDM)University, hospital or other
- Study start
- Sep 2026 (estimated)
- Primary completion
- Nov 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 11 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Patients with a histologically confirmed cancer
- Availability of relevant molecular profiling results
- At least one diagnostic hematoxylin and eosin (H\&E) whole-slide image
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
4 secondary outcomes
Per-scenario performance of each AI-generated deployment policy
Time frame: 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)
Temporal performance for trials first posted on or before December 31, 2025
Time frame: 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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Sep 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neoplasms (Cancer / Tumors) and Colorectal Neoplasms, run by Harvard Medical School (HMS and HSDM) at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 11 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Chi-Kang Pai (6172334924, chikang_pai@fas.harvard.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Sep 2026; AzTrials last refreshed its copy on 6 Nov 2026.