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Title: SU-F-T-685: Evaluation of Tumor Hypoxic Fraction Using Serial Volumetric Imaging During Radiation Therapy

Abstract

Purpose: To develop a tumor response model which could be uses to compute tumor hypoxic fraction using serial volumetric tumor imaging. This algorithm may be used for treatment response assessment and also for guidance of more expensive PET imaging of hypoxia. Methods: Previously developed two-level cell population tumor response model was modified to include a third cell level describing hypoxic and necrotic cells. This third level was considered constant value during radiotherapy treatment; therefore, inclusion additional parameter did not compromise stability of model fitting to imaging data. Fitting the model to serial volumetric imaging data was performed using a least squares objective function and simulated annealing algorithm. The problem of reconstruction of radiobiological parameters from serial imaging data was considered as inverse ill-posed problem described by the Fredholm integral equation of the first kind. Variational regularization was used to stabilize solutions. Results: To evaluate performance of the algorithm, we used a set of serial CT imaging data on tumor-volume for 14 head and neck cancer patients. The hypoxic fractions were reconstructed for each patient and the distribution of hypoxic fractions was compared to the distribution of initial hypoxic fractions previously measured using histograph. The measured and reconstructed from imaging datamore » distributions of hypoxic fractions are in good agreement. The reconstructed distribution of cell surviving fraction was also in better agreement with in vitro data than previously obtained using the two-level cell population model. Conclusion: Our results indicate that it is possible to evaluate the initial hypoxic tumor fraction using serial volumetric imaging and a tumor response model. This algorithm can be used for treatment response assessment and guidance of more expensive PET imaging.« less

Authors:
 [1]
  1. University of Washington, Seattle, WA (United States)
Publication Date:
OSTI Identifier:
22649240
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ALGORITHMS; BIOMEDICAL RADIOGRAPHY; DISTRIBUTION; FREDHOLM EQUATION; LEAST SQUARE FIT; NEOPLASMS; POSITRON COMPUTED TOMOGRAPHY; RADIOTHERAPY; SIMULATION

Citation Formats

Chvetsov, A. SU-F-T-685: Evaluation of Tumor Hypoxic Fraction Using Serial Volumetric Imaging During Radiation Therapy. United States: N. p., 2016. Web. doi:10.1118/1.4956871.
Chvetsov, A. SU-F-T-685: Evaluation of Tumor Hypoxic Fraction Using Serial Volumetric Imaging During Radiation Therapy. United States. doi:10.1118/1.4956871.
Chvetsov, A. Wed . "SU-F-T-685: Evaluation of Tumor Hypoxic Fraction Using Serial Volumetric Imaging During Radiation Therapy". United States. doi:10.1118/1.4956871.
@article{osti_22649240,
title = {SU-F-T-685: Evaluation of Tumor Hypoxic Fraction Using Serial Volumetric Imaging During Radiation Therapy},
author = {Chvetsov, A},
abstractNote = {Purpose: To develop a tumor response model which could be uses to compute tumor hypoxic fraction using serial volumetric tumor imaging. This algorithm may be used for treatment response assessment and also for guidance of more expensive PET imaging of hypoxia. Methods: Previously developed two-level cell population tumor response model was modified to include a third cell level describing hypoxic and necrotic cells. This third level was considered constant value during radiotherapy treatment; therefore, inclusion additional parameter did not compromise stability of model fitting to imaging data. Fitting the model to serial volumetric imaging data was performed using a least squares objective function and simulated annealing algorithm. The problem of reconstruction of radiobiological parameters from serial imaging data was considered as inverse ill-posed problem described by the Fredholm integral equation of the first kind. Variational regularization was used to stabilize solutions. Results: To evaluate performance of the algorithm, we used a set of serial CT imaging data on tumor-volume for 14 head and neck cancer patients. The hypoxic fractions were reconstructed for each patient and the distribution of hypoxic fractions was compared to the distribution of initial hypoxic fractions previously measured using histograph. The measured and reconstructed from imaging data distributions of hypoxic fractions are in good agreement. The reconstructed distribution of cell surviving fraction was also in better agreement with in vitro data than previously obtained using the two-level cell population model. Conclusion: Our results indicate that it is possible to evaluate the initial hypoxic tumor fraction using serial volumetric imaging and a tumor response model. This algorithm can be used for treatment response assessment and guidance of more expensive PET imaging.},
doi = {10.1118/1.4956871},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}