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Related Experiment Video

Updated: Jun 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Expert Validation of an Artificial Intelligence-Enabled Trial Matching Solution Using Real-World Data From Patients

Jai N Patel1,2,3,4, M Cyrus Maher5, Victoria Morris1,4

  • 1Atrium Health Levine Cancer, Charlotte, NC.

JCO Oncology Practice
|June 1, 2026
PubMed
Summary

An AI system (TRIAGE) accurately screened cancer trial eligibility using electronic health records (EHRs). It identified potential missed enrollment opportunities, demonstrating high performance in real-world oncology settings.

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Last Updated: Jun 2, 2026

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04:09

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Published on: October 10, 2018

Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Clinical trial enrollment is crucial for advancing cancer research.
  • Efficiently screening patients for trial eligibility using electronic health records (EHRs) remains a challenge.
  • AI holds potential to streamline the complex process of matching patients to suitable clinical trials.

Purpose of the Study:

  • To validate the performance of an AI system named TRIAGE for cancer clinical trial eligibility screening.
  • To assess TRIAGE's accuracy using real-world longitudinal EHR data, trial protocols, and expert adjudication.
  • To evaluate TRIAGE at both trial-level and criterion-level decision-making.

Main Methods:

  • A retrospective study utilized EHR data from August 2017 to April 2025.
  • The AI system (TRIAGE) was trained on 148 trials and 628 patients, and tested on 26 trials and 198 patients.
  • Manual adjudication by clinical research coordinators (CRCs) was performed on a subset of 100 patient-trial pairs for performance evaluation.

Main Results:

  • At a trial-level threshold of 0.40, TRIAGE achieved 78.3% sensitivity and 98.5% specificity.
  • Optimizing the threshold to 0.13 significantly increased sensitivity to 98.7% while maintaining high specificity (97.6%).
  • Criterion-level agreement between TRIAGE and CRCs was high (94.2% after readjudication), with 40% of initial disagreements resolved in favor of TRIAGE.

Conclusions:

  • The AI system TRIAGE demonstrated accurate and high-performance trial-level eligibility screening from real-world EHR data.
  • TRIAGE showed strong agreement at the criterion level for oncology protocols and identified potential missed enrollment opportunities.
  • Further prospective studies are underway to integrate TRIAGE into routine clinical research workflows.