## 4 Discussion
To further improve performance, attention modules were also employed, including channel attention, spatial attention, edge feature extraction modules, and even modules to weight information from different modalities, as proposed by [PERSON] et al. [51], such as Modality-Specific Attention and Modality Trusty Gating modules. However, a comprehensive quantitative comparison of all these alternatives is still missing, requiring further assessment to determine the most beneficial choices. Overall, in view of all the variability, a direct quantitative comparison of model performances is difficult. Most studies evaluate segmentation performance using the Dice score as a metric, obtaining values between 0.7 and 0.8 in most cases, lying within a range that is highly comparable with commonly encountered inter-operator variabilities. Some of the more recent models, trained with larger datasets, have also achieved impressive performances, with Dice scores exceeding 0.8. Despite the various proposed architectures and the high scores, some issues remain, such as missing very small lesions or false positive detections due to blood vessels, parenchymal enhancement, and axillary lymphadenopathies. These underline the potential for further improving the models, both by increasing the training sample sizes and variability, and by refining finer details of the architecture, as well as the preprocessing and postprocessing stages. Moreover, the use of the Dice score as the sole metric might be limiting. [PERSON][39] suggests including other metrics, such as HD95, to provide more comprehensive evaluations that are not solely correlated with segmentation loss. Considering instead the application of segmentation, while some studies focused exclusively on segmentation, there was also an important component of the examined studies that used the generated segmentation mainly for radiomic purposes. In particular, automatic segmentation was a useful baseline to extract radiomic features aiming at discrimination of malignant and benign lesions, characterization of molecular subtype, characterization of lymph node status, and prediction of pCR in view of neoadjuvant therapies. The use of automatic segmentation achieved comparable performances also in the radiomic analysis, suggesting it as a tool to obtain larger samples for advanced analysis. The increasing interest in radiomic studies prompts in parallel an increasing need for automatic unbiased tools to produce reliable and reproducible segmentation, showing overall the importance of this field. In summary, to provide a general course of action, the most common steps to achieve a segmentation approach are outlined in Figure 3. First, it is essential to define the input information that enables the segmentation, selecting the modality and the spatial and temporal dimensions of the data. If the dataset size is large enough, multimodal imaging and information from multiple time points can enhance model capabilities, especially when dealing with lesions of different types, benefiting from enhancement kinetics and complementary information from various modalities. Ideally, 3D images better encode small lesions between different slices; however, this comes with the disadvantage of a larger computational burden, making the choice dependent on the available hardware. Once the input is clearly defined, it is of utmost importance to determine whether the model will use only lesion information or the full information of both lesion and normal areas. The suggestion is to use full information in both training and testing to ensure a consistent estimation of performance, enabling the model to be used both on pathological and normal cases without issues. To enhance information, as the lesion is usually smaller than the whole image, different preprocessing steps can be applied, such as extracting bounding boxes around the lesion, pre-segmenting the breast ROI, dividing the left and right breasts, and extracting even smaller patches of tissue.