Track event model is superior to linear quadratic cell survival model for predicting TCP of SBRT treatments of NSCLC.
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[PURPOSE] The linear-quadratic (LQ) model is widely used in clinical practice, particularly for estimating equivalent fractionation schemes that yield a given isoeffect.
APA
Díaz Hernández KV, Schneider U, et al. (2026). Track event model is superior to linear quadratic cell survival model for predicting TCP of SBRT treatments of NSCLC.. Zeitschrift fur medizinische Physik. https://doi.org/10.1016/j.zemedi.2025.12.006
MLA
Díaz Hernández KV, et al.. "Track event model is superior to linear quadratic cell survival model for predicting TCP of SBRT treatments of NSCLC.." Zeitschrift fur medizinische Physik, 2026.
PMID
41484045 ↗
Abstract 한글 요약
[PURPOSE] The linear-quadratic (LQ) model is widely used in clinical practice, particularly for estimating equivalent fractionation schemes that yield a given isoeffect. While its validity for low-dose per fraction treatments is well established for in-vivo applications, its accuracy in predicting outcomes for high-dose fractionated radiotherapy remains debated. In response, several mechanistic models have been proposed as alternatives to this empirical approach. Among them, the track-event model (TEM) uniquely incorporates a two-parameter structure that exhibits exponential behavior at high doses and a finite gradient at null dose -an effect supported by experimental evidence. In this study, we compared the predictive accuracy of the LQ and TEM models specifically for stereotactic radiotherapy regimens.
[MATERIALS AND METHODS] A population-based TCP model incorporating variability in radiosensitivity and tumor volume was used to fit clinical data from 15 NSCLC studies involving low-dose per fraction treatments, using a log-likelihood optimization approach. These fitted models were then used to predict responses at high-dose levels and evaluated against observed stereotactic treatment outcomes from 24 additional studies. Model performance was assessed using log-likelihood values, along with the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) for comparative analysis.
[RESULTS] When comparing observed treatment outcomes with model-based predictions, the LC-TEM formulation demonstrates significantly greater predictive accuracy than the LC-LQ model at high doses per fraction. This is reflected in the evaluation metrics: log-likelihood per point, AIC, and BIC values were -2532 vs. -4413, 5075 vs. 8837, and 5088 vs. 8850 for LC-TEM and LC-LQ, respectively.
[CONCLUSION] The TEM offers a more accurate alternative to the LQ cell survival model for translating between different dose fractionation schemes, particularly those involving high doses per fraction. As a mechanistic model, TEM retains the simplicity of its empirical counterpart by relying on only two parameters, while providing improved predictive performance.
[MATERIALS AND METHODS] A population-based TCP model incorporating variability in radiosensitivity and tumor volume was used to fit clinical data from 15 NSCLC studies involving low-dose per fraction treatments, using a log-likelihood optimization approach. These fitted models were then used to predict responses at high-dose levels and evaluated against observed stereotactic treatment outcomes from 24 additional studies. Model performance was assessed using log-likelihood values, along with the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) for comparative analysis.
[RESULTS] When comparing observed treatment outcomes with model-based predictions, the LC-TEM formulation demonstrates significantly greater predictive accuracy than the LC-LQ model at high doses per fraction. This is reflected in the evaluation metrics: log-likelihood per point, AIC, and BIC values were -2532 vs. -4413, 5075 vs. 8837, and 5088 vs. 8850 for LC-TEM and LC-LQ, respectively.
[CONCLUSION] The TEM offers a more accurate alternative to the LQ cell survival model for translating between different dose fractionation schemes, particularly those involving high doses per fraction. As a mechanistic model, TEM retains the simplicity of its empirical counterpart by relying on only two parameters, while providing improved predictive performance.