Identifying Oncology Clinical Trial Candidates Using Artificial Intelligence Predictions of Treatment Change: A Pilot
Kenneth L Kehl1, Tali Mazor1, Pavel Trukhanov1
1Dana-Farber Cancer Institute, Boston, MA.
Purpose:
Precision oncology clinical trials often struggle to accrue, partly because it is difficult to find potentially eligible patients at moments when they need new treatment. We piloted deployment of artificial intelligence tools to identify such patients at a large academic cancer center.
Patients And Methods:
Neural networks that process radiology reports to identify patients likely to start new systemic therapy were applied prospectively for patients with solid tumors that had undergone next-generation sequencing at our center. Model output was linked to the MatchMiner tool, which matches patients to trials using tumor genomics. Reports listing genomically matched patients, sorted by probability of treatment change, were provided weekly to an oncology nurse navigator (ONN) coordinating recruitment to nine early-phase trials. The ONN contacted treating oncologists when patients likely to change treatment appeared potentially trial-eligible.
Results:
Within weekly reports to the ONN, 60,199 patient-trial matches were generated for 2,150 patients on the basis of genomics alone. Of these, 3,168 patient-trial matches (5%) corresponding to 525 patients were flagged for ONN review by our model, representing a 95% reduction in review compared with manual review of all patient-trial matches weekly. After ONN review for potential eligibility, treating oncologists for 74 patients were contacted. Common reasons for not contacting treating oncologists included cases where patients had already decided to continue current treatment (21%); the trial had no slots (14%); or the patient was ineligible on ONN review (12%). Of 74 patients whose oncologists were contacted, 10 (14%) had a consult regarding a trial and five (7%) enrolled.
Conclusion:
This approach facilitated identification of potential patients for clinical trials in real time, but further work to improve accrual must address the many other barriers to trial enrollment in precision oncology research.
Insights
Artificial intelligence (AI) tools helped identify patients for precision oncology trials by analyzing radiology reports. This AI approach significantly reduced manual review time, aiding in real-time patient recruitment for clinical studies.
Area of Science:
- Oncology
- Medical Informatics
- Clinical Trial Management
Background:
- Precision oncology aims to match cancer patients with targeted therapies based on genomic profiles.
- Accrual challenges in precision oncology clinical trials stem from difficulties in identifying eligible patients promptly.
- Artificial intelligence (AI) offers potential solutions for streamlining patient identification and trial matching.
Purpose of the Study:
- To pilot the deployment of AI tools for identifying potentially eligible patients for precision oncology clinical trials.
- To assess the efficiency of AI in flagging patients for new systemic therapy at an academic cancer center.
Main Methods:
- Prospective application of neural networks processing radiology reports to identify patients likely to start new systemic therapy.
- Integration of AI model output with the MatchMiner tool for patient-trial matching based on tumor genomics.
- Weekly reports provided to an oncology nurse navigator (ONN) to facilitate communication with treating oncologists regarding potential trial eligibility.
Main Results:
- AI model flagged 3,168 patient-trial matches (5% of 60,199) for 525 patients, a 95% reduction in manual review effort.
- Oncology nurse navigators contacted treating oncologists for 74 patients after review.
- Ten patients (14%) had a trial consult, and five (7%) enrolled in early-phase trials.
Conclusions:
- The AI-driven approach facilitates real-time identification of potential clinical trial candidates.
- Further research is needed to overcome existing barriers and improve clinical trial accrual in precision oncology.
- AI tools show promise in optimizing patient recruitment for precision cancer medicine studies.
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