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Tumor control probability modeling for stereotactic body radiation therapy of early-stage lung cancer using multiple bio-physical models. Radiother Oncol 2017 02;122(2):286-294



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Scopus ID

2-s2.0-85006942616   19 Citations


This work is to analyze pooled clinical data using different radiobiological models and to understand the relationship between biologically effective dose (BED) and tumor control probability (TCP) for stereotactic body radiotherapy (SBRT) of early-stage non-small cell lung cancer (NSCLC). The clinical data of 1-, 2-, 3-, and 5-year actuarial or Kaplan-Meier TCP from 46 selected studies were collected for SBRT of NSCLC in the literature. The TCP data were separated for Stage T1 and T2 tumors if possible, otherwise collected for combined stages. BED was calculated at isocenters using six radiobiological models. For each model, the independent model parameters were determined from a fit to the TCP data using the least chi-square (χ2) method with either one set of parameters regardless of tumor stages or two sets for T1 and T2 tumors separately. The fits to the clinic data yield consistent results of large α/β ratios of about 20Gy for all models investigated. The regrowth model that accounts for the tumor repopulation and heterogeneity leads to a better fit to the data, compared to other 5 models where the fits were indistinguishable between the models. The models based on the fitting parameters predict that the T2 tumors require about additional 1Gy physical dose at isocenters per fraction (⩽5 fractions) to achieve the optimal TCP when compared to the T1 tumors. In conclusion, this systematic analysis of a large set of published clinical data using different radiobiological models shows that local TCP for SBRT of early-stage NSCLC has strong dependence on BED with large α/β ratios of about 20Gy. The six models predict that a BED (calculated with α/β of 20) of 90Gy is sufficient to achieve TCP⩾95%. Among the models considered, the regrowth model leads to a better fit to the clinical data.

Author List

Liu F, Tai A, Lee P, Biswas T, Ding GX, El Naqa I, Grimm J, Jackson A, Kong FS, LaCouture T, Loo B Jr, Miften M, Solberg T, Li XA


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
Lung Neoplasms
Models, Biological
Neoplasm Staging
jenkins-FCD Prod-480 9a4deaf152b0b06dd18151814fff2e18f6c05280