A software application for mining and presenting relevant cancer clinical trials per cancer Mutation

Lisa M. Gandy, Jordan Gumm, Amanda L. Blackford, Elana J. Fertig, Luis A. Diaz

Research output: Contribution to journalArticlepeer-review

Abstract

ClinicalTrials.org is a popular portal which physicians use to find clinical trials for their patients. However, the current setup of ClinicalTrials.org makes it difficult for oncologists to locate clinical trials for patients based on mutational status. We present CTMine, a system that mines ClinicalTrials.org for clinical trials per cancer mutation and displays the trials in a user-friendly Web application. The system currently lists clinical trials for 6 common genes (ALK, BRAF, ERBB2, EGFR, KIT, and KRAS). The current machine learning model used to identify relevant clinical trials focusing on the above gene mutations had an average 88% precision/recall. As part of this analysis, we compared human versus machine and found that oncologists were unable to reach a consensus on whether a clinical trial mined by CTMine was “relevant” per gene mutation, a finding that highlights an important topic which deems future exploration.

Original languageEnglish (US)
JournalCancer Informatics
Volume16
DOIs
StatePublished - 2017

Keywords

  • Clinicaltrials.gov
  • Gene-specific therapies
  • Information retrieval
  • Machine learning
  • Natural language processing

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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