Computer modeling of lung cancer diagnosis-to-treatment process

Feng Ju, Hyo Kyung Lee, Raymond U. Osarogiagbon, Xinhua Yu, Nick Faris, Jingshan Li

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

Original languageEnglish (US)
Pages (from-to)404-414
Number of pages11
JournalTranslational Lung Cancer Research
Issue number4
StatePublished - 2015
Externally publishedYes


  • Analytical model
  • Closed formula
  • Discrete event simulation (DES)
  • Lung cancer quality improvement
  • Markov chain
  • Process modeling

ASJC Scopus subject areas

  • Oncology


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