## 4 Discussion  
In our results, the comparison of DIBH against FB, confirmed that DIBH is effective in reducing dose to cardiac structures. This result is in agreement with previous investigators. [PERSON] and co-authors found that the mean dose to LAD planning risk volume was reduced, from 31.7 to 21.9 Gy, because of DIBH [54]. More recently the LAD maximum dose reduction was from 31.9 Gy to 25.8 Gy [55]. The lower LAD doses obtained in our study (from 11.11 Gy in FB to 0.56 Gy in DIBH) could be explained with the use of IMRT in all of our patients, compared with 3D-conformal RT (3D-CRT) in the aforementioned study, which allows better dose distribution optimization [55]. [PERSON] and coauthors, using a hybrid 3D-CRT (80%) and IMRT(20%) technique, reduced the mean dose to the LAD from 11.4 to 5.5 Gy, which is more similar to our findings [56]. A more surprising result of our study, was the improvement in coverage of PTV with DIBH, perhaps also related to better sparing of organs at risk, which allows us to increase the dose to the target during inverse planning. We did not find a significant change in dose to the lung, whereas some previous studies experienced a significant increase in irradiated lung volume with DIBH, due to lung expansion within the tangential treatment fields [57]. By increasing the spatial separation between the target and the heart, DIBH can reduce the risk of heart disease [11], an event that can develop many years after the end of the treatment and result in increased rates of mortality [58], and therefore is a promising RT treatment option for left-sided breast cancer patients. DIBH also reduces the blurring of the static dose distribution caused by intra-fraction motion, which can result in a deviation between the calculated and the delivery dose distributions. However, not all patients are eligible for DIBH, due to difficulty in breath holding, or lack of dosimetric benefit. To date, there are no clear selection criteria to predict which patients will benefit most from the DIBH technique, other than left breast laterality. The dosimetric benefit of DIBH seems to be related to the change in lung volume achieved by the patient between FB and DIBH scans, and to the shape of the breast [46]. For selection criteria, it was proposed to use the distance between the heart and the treatment field, as it was related to the mean heart dose [59]. In the study of [PERSON] and co-workers [60] it  
Figure 5: Graph showing the accuracy of the BLSTM neural network during the 250 epochs of training, both in the training (blue), and test sets (red). was reported that, for left-sided breast cancer patients, the mean heart dose was related to the distance between the heart and the treatment field. In another study, the association of patient-related parameters, such as body mass index, age, PTV, cardiac contact distance, and lung volume at FB fully, with improved dosimetric indexes such as mean heart dose of 20%, were studied, but no significant correlations were found [61]. In order to select patients eligible for DIBH, a machine learning model which uses input variables such as heart and breast volumes, and distances, was proposed [30], resulting in accuracy of 0.88, and AUC 0.80 in cross validation. In our study, we adopted a random train -split validation, which is considered to be a stricter validation method, according to The Transparent Reporting of a Multivariable Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines [62], which may explain our apparently lower performance. Methods based on anatomical descriptors such as organ volumes and distances, suffer from limitations, as they do not account for the patient's ability in maintaining breath hold. The respiratory signal acquired during the take-over of the pre-treatment CT, can provide a surrogate for this information. The importance of breathing signal was confirmed by the work of [PERSON] et al. [63], wherein the increase in amplitude from FB to DIBH, was correlated with the reduction in cardiac dose.