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. 2014 Dec 4;13:157-66.
doi: 10.4137/CIN.S19454. eCollection 2014.

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

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Free PMC article

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

Satya S Sahoo et al. Cancer Inform. .
Free PMC article

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.

Keywords: clinical decision support system; clinical oncology; clinical trial; gastrointestinal cancer; patient recruitment.

Figures

Figure 1
Figure 1
Architecture of Trial Prospector illustrating interface with three external data sources.
Figure 2
Figure 2
The Trial Builder module: (A) UI to define a new eligibility variable, (B) use of conditional constructs to compose nested eligibility criteria, and (C) composition of a clinical trial CALGB80702 (a phase III trial for resected stage III colon cancer with eight eligibility criteria).
Figure 3
Figure 3
The CDE module interface implemented by extending the Caisis tool interface.
Figure 4
Figure 4
The scalable matching algorithm for identifying clinical trials for a patient.
Figure 5
Figure 5
The Trial Prospector UI. (A) Results of search performed using first name of patient. (B) The four facets constituting the Trial Prospector interactive UI displaying match results.
Figure 6
Figure 6
The detailed explanation for exclusion of a patient from specific trial.
Figure 7
Figure 7
The results from deployment of Trial Prospector at the Seidman Cancer Center. (A) Distribution of exclusion conditions for patients. (B) Results of performance evaluation of Trial Prospector over increasing number of patients and trials.

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