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NCT07307157

Head-to-Head Evaluation of the Cancer Ontology Supervised Multimodal Orchestration (COSMO) AI System Versus Pathologist-Only Review

Enrolling by Invitation
Not specifiedAll AgesObservational
Harvard Medical School (HMS and HSDM)
~30 participants
Updated 2025-12-29 on ClinicalTrials.gov
What's tested:Digital Pathology Evaluation

At a glance

Recruiting sites
0 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Diagnostic performance
Measured over Periprocedural (at the time of slide review)

Conditions

Brain Cancer
Lung Cancer (Diagnosis)
Renal Cancer

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Kidney CarcinomaLung CarcinomaMalignant Brain Neoplasm

Where it's being run

1 sites across 1 states
Massachusetts1

Study leadership

  • Kun-Hsing Yu, MD, PhD · PRINCIPAL_INVESTIGATOR · Harvard Medical School (HMS and HSDM)

Who to contact

This trial hasn't published a contact. View it on ClinicalTrials.gov

Do you actually qualify for this trial?

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Eligibility criteria

Inclusion

Board-certified pathologist with expertise in neuropathology, pulmonary pathology, urologic pathology, or general anatomical pathology
Minimum of 3 years of clinical diagnostic experience
Active clinical practice involving diagnostic pathology slide review
Willingness to independently review and diagnose up to 300 de-identified whole-slide images
Ability to access the study platform and complete case reviews within the specified study timeline
Provision of informed consent for study participation

Exclusion

Prior involvement in the design or validation of the COSMO AI system
Inability to commit sufficient time to complete assigned case reviews
Presence of significant financial conflicts of interest related to the study outcomes

What this trial measures

  • Diagnostic performancePeriprocedural (at the time of slide review)

    Diagnostic performance of the COSMO AI system and pathologists in identifying cancer subtypes across brain, lung, and kidney tumors, as assessed by accuracy, balanced accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). We will include both overall comparisons and stratified evaluations by anatomical site and cancer incidence category (common vs. rare or uncommon).