Purpose: To improve the dosimetric accuracy of archived lung treatment plans, we use a novel Monte Carlo recalculation method based on pencil beam optimization methods. The impact of the dose corrections on outcome modeling of pneumonitis was assessed. Method and Materials: For 189 archived non‐small cell lung cancer plans, dose distributions were re‐calculated using the VMC++ Monte Carlo code (I.Kawrakow). Nominal input spectra for 6 or 18 MV photons were used; only radiation transport through the patient was modeled, using each patient's pre‐treatment CT scan. We derived approximate beam weights and wedge effects with a novel method based on optimization of MC‐derived pencil beams: MC and treatment planning results were matched for the water‐based (non‐heterogeneity corrected) results. Heterogeneity‐corrected plans were then produced using Monte Carlo with the derived beam profiles and weights. Results: The method showed good agreement when compared against a small series of treatment plans using a convolution‐superposition dose calculation. For the lung plans, the average absolute differences in metrics of interest (V20, maximum lung dose, and mean GTV dose) between water‐based TPS and water‐based MC data were 0.5%, 0.9 Gy, and 0.8 Gy; for water‐based TPS versus heterogeneity‐corrected MC data the absolute differences were greater: 2.0%, 1.8 Gy, and 2.5 Gy (typically heterogeneity corrected dose distributions produced higher dose values). The correlations between V20 and occurrence of pneumonitis for water‐based TPS, water‐based MC, and heterogeneity corrected MC data were (using Spearman's rank correlation coefficient) 0.13, 0.13, and 0.14 (respectively). For maximum lung dose, the correlations were 0.15, 0.14, and 0.09. Conclusion: The differences in some metrics (e.g., maximum lung dose) between water‐based and heterogeneity corrected data may have a significant impact on modeling treatment outcome. This method could be applied to any multi‐institutional data sets for which RTOG format plan archives are available.