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An analysis of tumor control probability of stereotactic body radiation therapy for lung cancer with a regrowth model. Phys Med Biol 2016 05 21;61(10):3903-13

Date

04/28/2016

Pubmed ID

27117498

DOI

10.1088/0031-9155/61/10/3903

Scopus ID

2-s2.0-84969508573   10 Citations

Abstract

We report a modeling study of tumor response after stereotactic body radiation therapy (SBRT) for early-stage non-small-cell lung carcinoma using published clinical data with a regrowth model. A linear-quadratic inspired regrowth model was proposed to analyze the tumor control probability (TCP) based on a series of published data of SBRT, in which a tumor is controlled for an individual patient if number of tumor cells is smaller than a critical value K cr. The regrowth model contains radiobiological parameters such as α, α/β the potential doubling time T p. This model also takes into account the heterogeneity of tumors and tumor regrowth after radiation treatment. The model was first used to fit TCP data from a single institution. The extracted fitting parameters were then used to predict the TCP data from another institution with a similar dose fractionation scheme. Finally, the model was used to fit the pooled TCP data selected from 48 publications available in the literature at the time when this manuscript was written. Excellent agreement between model predictions and single-institution data was found and the extracted radiobiological parameters were α  =  0.010  ±  0.001 Gy(-1), α /β  =  21.5  ±  1.0 Gy and T p  =  133.4  ±  7.6 d. These parameters were α  =  0.072  ±  0.006 Gy(-1), α/β  =  15.9  ±  1.0 Gy and T p  =  85.6  ±  24.7 d when extracted from multi-institution data. This study shows that TCP saturates at a BED of around 120 Gy. A few new dose-fractionation schemes were proposed based on the extracted model parameters from multi-institution data. It is found that the regrowth model with an α/β around 16 Gy can be used to predict the dose response of lung tumors treated with SBRT. The extracted radiobiological parameters may be useful for comparing clinical outcome data of various SBRT trials and for designing new treatment regimens.

Author List

Tai A, Liu F, Gore E, Li XA

Authors

Elizabeth M. Gore MD Professor in the Radiation Oncology department at Medical College of Wisconsin
X Allen Li PhD Professor in the Radiation Oncology department at Medical College of Wisconsin
An Tai PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Carcinoma, Non-Small-Cell Lung
Humans
Linear Models
Lung Neoplasms
Models, Biological
jenkins-FCD Prod-480 9a4deaf152b0b06dd18151814fff2e18f6c05280