Purpose: This study introduces an advanced method to evaluate and extent the adjustment of the prescribed dose to maintain the same clinical results, when changing the dose calculation algorithm type (a), i.e density correction method to more recently type (b) algorithm, i.e AAA. Material and methods: 10 cases with lung cancer were studied. For each case, 3 treatment plans were generated. Plan 1 was generated using type (a) algorithm, and Plan 2 using type (b) algorithm. In plan 3 the dose was calculated with type (b) algorithm using monitor units from plan 1 as input. A global analysis based on 2D and 3D gamma (γ) was made to evaluate the under / overestimation of calculated dose. Clinical evaluation was carried out using Tumour Control Probability (TCP) and Normal Tissue Complication Probabilities (NTCP) based on Uniform Equivalent Dose model. Assuming a constant TCP, the ratio “R= TCP/NTCP” and Uncomplicated Tumor Control Probability (UTCP) were calculated to measure the clinical benefit – toxicity. Wilcoxon test was used to evaluate the significance of the differences and the correlation coefficient (r) was calculated using Spearman’s rank test. Results: The dose calculated with algorithm type (b) was significantly overestimated to organs at risks while the delivered dose in MU was underestimated, p < 0.001. Therefore, γ maps confirmed the dosimetric results. Moreover, there were a significant difference for NTCP for lung and heart. The ratio “R” from plan 1 and plan 2 were significantly different, indicating that to maintain the same effect benefit and toxicity the prescribed dose should be readjusted. Conclusion: We assessed the prescribed dose using the radiobiological models. The ratio of benefit was significantly changed when moving from type (a) algorithm to type (b) algorithm. This indicate that the prescribed dose should be readjusted when type (b) algorithm will be integrated in radiation oncology. A discussion between oncologist and physicist is quite necessary in order to readjust the prescribed dose.