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Cell Surviving Fractions Derived From Tumor-Volume Variation During Radiotherapy for Non-Small Cell Lung Cancer: Comparison with Predictive Assays
A Chvetsov1*, S Yartsev2 , J Schwartz1 , N Mayr1 , (1) University of Washington, Seattle, WA, (2) London Health Sciences Centre, London, Ontario
Presentations
SU-E-T-427 Sunday 3:00PM - 6:00PM Room: Exhibit HallPurpose: To show that a distribution of cell surviving fractions Sâ‚‚in a heterogeneous group of patients can be derived from tumor-volume variation curves during radiotherapy for non-small cell lung cancer.
Methods: Our analysis was based on two data sets of tumor-volume variation curves for heterogeneous groups of 17 patients treated for non-small cell lung cancer with conventional dose fractionation. The data sets were obtained previously at two independent institutions by using megavoltage (MV) computed tomography (CT). Statistical distributions of cell surviving fractions Sâ‚‚and cell clearance half-lives of lethally damaged cells T1/2 have been reconstructed in each patient group by using a version of the two-level cell population tumor response model and a simulated annealing algorithm. The reconstructed statistical distributions of the cell surviving fractions have been compared to the distributions measured using predictive assays in vitro.
Results: Non-small cell lung cancer presents certain difficulties for modeling surviving fractions using tumor-volume variation curves because of relatively large fractional hypoxic volume, low gradient of tumor-volume response, and possible uncertainties due to breathing motion. Despite these difficulties, cell surviving fractions Sâ‚‚for non-small cell lung cancer derived from tumor-volume variation measured at different institutions have similar probability density functions (PDFs) with mean values of 0.30 and 0.43 and standard deviations of 0.13 and 0.18, respectively. The PDFs for cell surviving fractions Sâ‚‚reconstructed from tumor volume variation agree with the PDF measured in vitro. Comparison of the reconstructed cell surviving fractions with patient survival data shows that the patient survival time decreases as the cell surviving fraction increases.
Conclusion: The data obtained in this work suggests that the cell surviving fractions Sâ‚‚can be reconstructed from the tumor volume variation curves measured during radiotherapy with conventional fractionation. The proposed method can be used for treatment evaluation and adaptation.
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