Deep Clinical Trajectory Modeling to Optimize Accrual to Cancer Clinical Trials

Dana-Farber Cancer Institute

Status: Completed

Conditions: Cancer

Treatments: Other: AI-assisted MatchMiner Platform

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT06888089

R00CA245899 (U.S. NIH Grant/Contract)

19-536

1K99CA245899 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 20,707 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

-≥ 18 years of age

-adults with any type of cancer whose tumors underwent OncoPanel genomic sequencing from 2013-2022

Exclusion criteria

-≤ 18 years of age.

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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20,707 participants in 3 patient groups

Group 1: MatchMiner

No Intervention group

Description:

Treating oncologists and investigators can use the standard method of accessing the MatchMiner tool to identify potential clinical trials for eligible participants based on structured genomic criteria.

Group 2: MatchMiner Proactive Notification based on AI-detected progression

Experimental group

Description:

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.

Treatment:

Other: AI-assisted MatchMiner Platform

MatchMiner AI with Proactive Notification Based on AI-detected progression + Study Team Confirmation

Experimental group

Description:

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)

Treatment:

Other: AI-assisted MatchMiner Platform