Trial Prospector: Matching Patients with Cancer Research Studies Using an Automated and Scalable Approach

Overview

Journal Cancer Inform

Publisher Sage Publications

Specialty Biomedical Engineering

Date 2014 Dec 16

PMID 25506198

Citations 16

Authors

Satya S Sahoo

Shiqiang Tao

Andrew Parchman

Zhihui Luo

Licong Cui

Patrick Mergler

Robert Lanese

Jill S Barnholtz-Sloan

Neal J Meropol

Guo-Qiang Zhang

Affiliations

Soon will be listed here.

Abstract

Cancer is responsible for approximately 7.6 million deaths per year worldwide. A 2012 survey in the United Kingdom found dramatic improvement in survival rates for childhood cancer because of increased participation in clinical trials. Unfortunately, overall patient participation in cancer clinical studies is low. A key logistical barrier to patient and physician participation is the time required for identification of appropriate clinical trials for individual patients. We introduce the Trial Prospector tool that supports end-to-end management of cancer clinical trial recruitment workflow with (a) structured entry of trial eligibility criteria, (b) automated extraction of patient data from multiple sources, (c) a scalable matching algorithm, and (d) interactive user interface (UI) for physicians with both matching results and a detailed explanation of causes for ineligibility of available trials. We report the results from deployment of Trial Prospector at the National Cancer Institute (NCI)-designated Case Comprehensive Cancer Center (Case CCC) with 1,367 clinical trial eligibility evaluations performed with 100% accuracy.

Citing Articles

"The Truth Is, We Must Miss Some": A Qualitative Study of the Patient Eligibility Screening Process, and Automation Perspectives, for Cancer Clinical Trials.

La Rosa A, Vaterkowski M, Cuggia M, Campillo-Gimenez B, Tournigand C, Baujat B Cancer Med. 2024; 13(23):e70466.

PMID: 39624972 PMC: 11612666. DOI: 10.1002/cam4.70466.

Searching of Clinical Trials Made Easier in cBioPortal Using Patients' Genetic and Clinical Profiles.

Unberath P, Mahlmeister L, Reimer N, Busch H, Boerries M, Christoph J Appl Clin Inform. 2022; 13(2):363-369.

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A systematic review on natural language processing systems for eligibility prescreening in clinical research.

Idnay B, Dreisbach C, Weng C, Schnall R J Am Med Inform Assoc. 2021; 29(1):197-206.

PMID: 34725689 PMC: 8714283. DOI: 10.1093/jamia/ocab228.

Landscape Analysis of Breast Cancer and Acute Myeloid Leukemia Trials Using the My Cancer Genome Clinical Trial Data Model.

Jain N, Holt M, Micheel C, Levy M JCO Clin Cancer Inform. 2021; 5:975-984.

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A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes.

Alcaide D, Aerts J PeerJ Comput Sci. 2021; 7:e430.

PMID: 33954230 PMC: 8049127. DOI: 10.7717/peerj-cs.430.

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