Modeling of non-small cell lung cancer volume changes during CT-based image guided radiotherapy: Patterns observed and clinical implications

Hiram A. Gay, Quendella Q. Taylor, Fumika Kiriyama, Geoffrey T. Dieck, Todd Jenkins, Paul Walker, Ron R. Allison, Paolo Ubezio

Research output: Contribution to journalArticle

Abstract

Background. To characterize the lung tumor volume response during conventional and hypofractionated radiotherapy (RT) based on diagnostic quality CT images prior to each treatment fraction. Methods. Out of 26 consecutive patients who had received CT-on-rails IGRT to the lung from 2004 to 2008, 18 were selected because they had lung lesions that could be easily distinguished. The time course of the tumor volume for each patient was individually analyzed using a computer program. Results. The model fits of group L (conventional fractionation) patients were very close to experimental data, with a median Δ% (average percent difference between data and fit) of 5.1% (range 3.5-10.2%). The fits obtained in group S (hypofractionation) patients were generally good, with a median Δ% of 7.2% (range 3.7-23.9%) for the best fitting model. Four types of tumor responses were observed - Type A: "high" kill and "slow" dying rate; Type B: "high" kill and "fast" dying rate; Type C: "low" kill and "slow" dying rate; and Type D: "low" kill and "fast" dying rate. Conclusions. The models used in this study performed well in fitting the available dataset. The models provided useful insights into the possible underlying mechanisms responsible for the RT tumor volume response.

Original languageEnglish
Article number637181
JournalComputational and Mathematical Methods in Medicine
Volume2013
DOIs
Publication statusPublished - 2013

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)
  • Immunology and Microbiology(all)

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