Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications

Mika Katalinic, Martin Schenk, Stefan Franke, Alexander Katalinic, Thomas Neumuth, Andreas Dietz, Matthaeus Stoehr, Jan Gaebel

Abstract

BACKGROUND: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer.

METHODS: We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngeal cancer cases from the simulated population and deceased persons, stratified by the real-world age and sex distributions at diagnosis.

RESULTS: A module with age- and stage-specific treatment and prognosis for laryngeal cancer was successfully implemented. The synthesized population reflects RW prevalence well, extracting a cohort of 50,000 laryngeal cancer patients. Descriptive data on stage-specific and 5-year overall survival were in accordance with published data.

CONCLUSIONS: We developed a large cohort of realistic synthetic laryngeal cancer cases with Synthea. Such data can be shared and published open source without data protection issues.

Original languageEnglish
Article number639
JournalCancers
Volume16
Issue number3
ISSN2072-6694
DOIs
Publication statusPublished - 01.02.2024

Research Areas and Centers

  • Research Area: Center for Population Medicine and Public Health (ZBV)
  • Research Area: Luebeck Integrated Oncology Network (LION)

DFG Research Classification Scheme

  • 205-02 Public Health, Health Services Research and Social Medicine
  • 205-14 Haematology, Oncology

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