Prognostic modeling and analysis of tumor response to fractionated radiotherapy for patients with squamous cell lung cancer

Hualiang Zhong, Hoda Sharifi, Haisen Li, Weihua Mao, Indrin J. Chetty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The efficacy of radiotherapy depends on tumor response to radiation. The purpose of this study is to derive three basic tumor-response parameters from a sequence of measured tumor volumes, and then use these parameters to predict treatment outcome for patients with squamous cell lung cancer. 43 patients were investigated in this study. A tumor kinetic model was iterated daily, following the fractionation scheme of radiation treatment for each patient. By minimizing the difference between the model-predicted tumor regression and the measured changes in tumor volumes, three parameters were calibrated and then used to calculate tumor control probability (TCP). The results showed that the major portion of the patients have tumor doubling time (DT) less than 183 days and half time (HT) for dead cell resolving less than 50 days. The modeling differences from measured tumor volumes were summated over the 43 patients, and the sum reaches its minimum with DT at 90 days and HT at 30 days. The behavior of the kinetic model is stable to DT and HT as the modeling difference has only one minimum within the clinically meaningful ranges for the two parameters. One third of these patients have the cell survival fraction at 12 Gy (SF12) less than 0.1, and 80% of them with SF12 less than 0.6. In summary, kinetic modeling of tumor response may help quantify radiobiological parameters which can be used to predict treatment outcomes for individual patients.

Original languageEnglish (US)
Title of host publicationBioinformatics and Biomedical Engineering - 5th International Work-Conference, IWBBIO 2017, Proceedings
EditorsIgnacio Rojas, Francisco Ortuno
PublisherSpringer Verlag
Pages549-559
Number of pages11
ISBN (Print)9783319561530
DOIs
StatePublished - 2017
Externally publishedYes
Event5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017 - Granada, Spain
Duration: Apr 26 2017Apr 28 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10209 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017
Country/TerritorySpain
City Granada
Period4/26/174/28/17

Keywords

  • Kinetic modeling
  • Radiobiological parameters
  • Tumor control probability
  • Tumor regression

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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