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Prescreening in Oncology Using Data Sciences: The PreScIOUS Study.

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  • 1CHRU Bretonneau, University Hospital, 2 Boulevard TonnelĂ©, 37044 Tours, France.

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Summary
This summary is machine-generated.

An automated algorithm using natural language processing can efficiently extract patient and tumor data from multidisciplinary team meeting reports for precision oncology. This reduces prescreening time for targeted cancer therapies.

Keywords:
Lung Neoplasms/statistics and numerical dataMultidisciplinary team meeting consultationNatural language processingNeoplasm Staging/therapeutic usePatient Selection

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Area of Science:

  • Oncology
  • Computational Biology
  • Medical Informatics

Background:

  • Precision medicine requires detailed patient and tumor profiling for targeted cancer therapies.
  • Eligibility criteria for clinical trials and targeted treatments are increasingly complex.
  • Manual prescreening of patient data for trial eligibility is time-consuming and labor-intensive.

Purpose of the Study:

  • To develop and evaluate an automated algorithm for extracting key bioclinical information from multidisciplinary team meeting (MTM) reports.
  • To reduce the time and effort associated with prescreening patients for targeted therapies and clinical trials.
  • To identify patient and tumor characteristics relevant to eligibility criteria in oncology.

Main Methods:

  • Natural language processing (NLP) and regular expressions were employed to automate data extraction.
  • 640 anonymized multidisciplinary team meeting (MTM) reports for lung cancer patients were collected and annotated.
  • The algorithm was evaluated based on precision, recall, and F1-score for extracting 52 bioclinical data points.

Main Results:

  • The automated algorithm achieved a macroaverage F1-score of 93%, with 98% precision and 92% recall.
  • Significant variability in information completeness was observed across MTM reports (31.4% to 100%).
  • Genetic mutation and rearrangement test results were the least reported and most challenging data points for automated extraction.

Conclusions:

  • Automated NLP-based extraction from MTM reports is a viable strategy to streamline patient prescreening for precision oncology.
  • The developed algorithm demonstrates high performance in identifying crucial patient and tumor characteristics.
  • Addressing data completeness, particularly for genetic markers, remains a challenge for fully automated clinical trial matching.