Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

Author:

Alexander Marliese12,Solomon Benjamin23,Ball David L24,Sheerin Mimi5,Dankwa-Mullan Irene5,Preininger Anita M5ORCID,Jackson Gretchen Purcell5,Herath Dishan M3

Affiliation:

1. Department of Pharmacy, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

2. Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia

3. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

4. Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

5. IBM Watson Health, Cambridge, Massachusetts, USA

Abstract

Abstract Objective The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. Methods A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I–III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. Results The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53–100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243–4132). Median time for the system to run a query and return results was 15.5 s (range 7.2–37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen’s kappa 0.70–1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. Discussion and Conclusion The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.

Funder

IBM

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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