A new index comparable to BED for evaluating the biological efficacy of hypofractionated radiotherapy schemes on early stage non-small cell lung cancer: Analysis of data from the literature

Background and purpose

Hypofractionated radiotherapy has been the principal curative treatment option for early stage NSCLC patients who are medically inoperable or those who refuse surgery and achieved favorable clinical outcomes. Evidence demonstrated that the linear quadratic model widely used in normally fractionated radiotherapy cannot work well to fit outcome data by use of BED to predict the effect of hypofractionation schemes. New models and the related metrics need to be developed to quantify the effect of high-dose ablative regimens for early stage NSCLC.

Patients and methods

Trials using hypofractionated radiotherapy without chemotherapy to treat early stage (T₁ or T₂N₀M₀) primary NSCLC and providing information on patient numbers, age, T stage and local control rates were eligible. The endpoint was local relapse and the covariates analyzed were total radiotherapy dose, dose per fraction or combinations of the two parameters, treatment duration, T stage and median age of patients within the trial. The model used was a multivariate logistic regression.

Results

19 trials were included (767 patients) in which 90 patients suffered local relapse. Only total dose×dose per fraction (D × d) and stage T had statistically significant effect on local control. Smaller T stage (p = 0.000) and increasing D × d (p = 0.006) were associated with improved probability of local control. In contrast, BED₁₀ had no significant impact on local control, which probably indicated that D × d might be a more effective metric than BED₁₀ to predict tumor control rate and assess the efficacy of the large dose fractionation schemes for early stage NSCLC.

Conclusions

BED was not an ideal metric to estimate the effect of the schemes of high-dose ablative radiotherapy for early stage NSCLC, and total dose×fraction dose could be considered as a comparable index, though the result need to be further validated.