## 4 Discussion
Their results indicated that 3D-CRT yielded the lowest estimated risks for secondary thyroid and esophageal cancers, while PBS offered advantages in reducing risks to the lung and contralateral breast. Conversely, VMAT was associated with the highest overall secondary cancer risks. In our analysis, a paired _t_-test (_p_-value < 0.01) comparing _LAR_ values between SF and HF schedules showed that HF is associated with a statistically significant reduction in RT-induced lung cancer risk. This reduction is due to the lower exposure of the ipsilateral lung to low-dose regions in HF treatments. Because RED depends exponentially on both total dose (_D_) and dose per fraction (\(d_{f}\)), the reduced lung dose translates into a meaningful decrease in carcinogenic risk with the HF regimen. In this study, the use of SF 3D-CRT was estimated to result in a _LIR_ increase of 26-86%, while HF treatment may lead to an increase of 23-76%. The corresponding _RR_ values, derived from _EAR_ estimates using the Schneider model, are comparable to those reported in the meta-analysis of 762,468 patients conducted by [PERSON] et al. [15] on the risk of second non-breast cancers following RT for BC. They reported RR values of 1.39 (95% CI: 1.28-1.51). Using the Schneider model, [PERSON] et al. [35] reported a LIR increase of 60-81% for the SF approach and 53-73% for the HF approach. The uncertainty in the risk estimates arises from several factors [41], each potentially introducing significant variability. These include tissue modeling, the repopulation process linked to the prescribed dose, the biological mechanisms leading to tumor cell formation, the parameters of dose-response curves, and dosimetric considerations. [PERSON] et al. [7], through independent fits to observed _EAR_ variations, reported a Relative Standard Deviation (RSD) of 1.31 for \(\alpha\) and 0.06 for R, while [6] found an RSD of 0.19. From a dosimetric perspective, since the _OED_ is derived from the differential DVH, uncertainties are associated with the correspondence between the calculated and the delivered dose at specific points within the organ, particularly in regions with steep dose gradients or significant tissue inhomogeneity. The integral dose only approximates the risk to an individual organ. This is due to the non-linear dose-response relationship, meaning that the risk depends on the full 3D dose distribution rather than being directly proportional to the integral dose [42]. Another important aspect is the repeatability of the dose-volume association for organs at risk across treatment sessions. The implementation of patient immobilization systems, verification tools such as Cone Beam Computed Tomography (CBCT), Electronic Portal Imaging Devices (EPID), and Surface-Guided Radiation Therapy (SGRT) systems integrated with the LINAC has significantly reduced this source of uncertainty. However, clinical data remain limited for establishing epidemiological studies that fully account for these factors. Under these conditions, hybrid approaches combining mechanistic and empirical methods appear promising for risk model development. This is particularly true when incorporating factors known to influence the risk of second cancers following RT [4]. _EAR, LAR_, and _RR_ estimations require statistical data that must be representative of the study population to avoid overestimating or underestimating the risk. For this reason, a preliminary research phase was conducted to identify statistics specific to the Sicilian female population. In our view, this step is essential to enhance the reliability of the obtained results. When patients are treated with ionizing radiation, a stochastic risk of carcinogenesis is introduced. Our results show that, according to the RT-induced lung cancer risk model, the estimated risk for breast cancer patients is lower for 3D-CRT HF treatments compared to SF treatments. In this context, the use of customized scripts that interface with treatment planning systems such as Eclipse(tm) represents a valuable tool.