NCT06888089

Deep Clinical Trajectory Modeling to Optimize Accrual to Cancer Clinical Trials

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 20,707 (actual)
• Sponsor: Dana-Farber Cancer Institute · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This study aims to evaluate the effectiveness of proactive notifications to treating oncologist to optimize participant accrual to clinical trials by utilizing the MatchMiner AI platform. This study compares the standard MatchMinder AI access method to two enhanced recruitment methods.

Detailed description

The goal of this medical record data analysis and health system implementation study is to evaluate the effectiveness of proactive notifications to treating oncologist to optimize participant accrual to clinical trials by utilizing the MatchMiner platform. This study compares the standard MatchMinder access method to two enhanced recruitment methods. In the first phase, investigators will provide qualitative feedback to improve AI algorithm impact on clinical trial accrual and the delivery of information from the MatchMiner platform that is utilized by treating oncologists and investigators. In the second phase, medical records identified by the MatchMiner platform as available or a "match" for clinical trial enrollment will be randomized into three cohorts with the randomization occurring at the participant level. In Group 1, treating oncologists can use MatchMiner in its traditional form to identify potential clinical trial candidates based on structured genomic data and cancer type. In Group 2, treating oncologists will automatically receive emails with lists of potential genomically matched clinical trials identified by MarchMiner for patients in whom our AI algorithm detects an elevated probability of changing treatment based on imaging reports; oncologists can also still use traditional MatchMiner workflows. In Group 3, treating oncologists will receive email lists of genomically matched clinical trials identified by Matchminer for patients with AI-detected elevated probability of treatment change, after additional manual review to confirm that patients had progressive diseased based on their imaging reports and did not meet one of the common exclusion criteria for most cancer trials (including uncontrolled brain metastases, multiple primary cancers, poor performance status, lack of measurable disease, already having changed treatment, and hospice enrollment). Of note, this study was not itself considered a clinical trial during the initial NCI grant application process or on subsequent discussion with NIH staff, since the outcomes were research processes (whether patients enrolled in other therapeutic clinical trials), not health-related patient outcomes as per the NIH definition of a clinical trial. However, for publication, a medical journal determined that the study met ICMJE criteria for a clinical trial and requested that it be registered.

Conditions

• Cancer

Interventions

Type	Name	Description
OTHER	AI-assisted MatchMiner Platform	A medical record data analysis tool that uses conjunction machine learning and natural language processing models to predict changes in treatment and prognosis and ascertain progression of disease and metastatic sites using retrospective imaging reports. MatchMiner is an established clinical operations tool at Dana-Farber Cancer Institute that links OncoPanel next-generation sequencing data to basic clinical information and clinical trial eligibility criteria to suggest biomarker-selected therapeutic trials for participants.

Timeline

• Start date: 2023-01-30
• Primary completion: 2024-07-15
• Completion: 2024-07-15
• First posted: 2025-03-21
• Last updated: 2025-03-21

Locations

1 site across 1 country: United States