impact of dose calculation algorithms and radiobiological parameters on prediction of cardiopulmonary complications in left breast radiation therapy

Background: breast cancer requires evaluating treatment plans using dosimetric and biological parameters. considering radiation dose distribution and tissue response, healthcare professionals can optimize treatment plans for better outcomes.objective: this study aimed to evaluate the effects of the different dose calculation algorithms (dcas) and biologically model-related parameters (bmrps) on the prediction of cardiopulmonary complications due to left breast radiotherapy.material and methods: in this practical study, the treatment plans of 21 female patients were simulated in the monaco treatment planning system (tps) with a prescribed dose of 50 gy in 25 fractions. dose distribution was extracted using the three dcas [pencil beam (pb), collapsed cone (cc), and monte carlo (mc)]. cardiopulmonary complications were predicted by normal tissue complication probability (ntcp) calculations using different dosimetric and biological parameters. the lyman-kutcher-burman (lkb) and relative-seriality (rs) models were used to calculate ntcp. the endpoint for ntcp calculation was pneumonitis, pericarditis, and late cardiac mortality. the anova test was used for statistical analysis.results: in calculating tumor control probability (tcp), a statistically significant difference was observed between the results of dcas in the poisson model. the pb algorithm estimated ntcp as less than others for all pneumonia bmrps. conclusion: the impact of dcas and bmrps differs in the estimation of tcp and ntcp. dcas have a stronger influence on tcp calculation, providing more effective results. on the other hand, bmrps are more effective in estimating ntcp. consequently, parameters for radiobiological indices should be cautiously used s to ensure the appropriate consideration of both dcas and bmrps.