## 4 Discussion
Another difference between the studies is the use of different types of lesions, thus segmentation performances on a specific type of lesion might not be generalisable to all the other types, with some cases that are highly challenging. In particular, [PERSON] et al. [32], who dealt with both malignant and benign cases, reported that the detection of ductal carcinoma in situ is particularly difficult, and this kind of lesion had a lower detection rate in their framework. [PERSON] et al. [34] reported that benign lesions with low contrast uptake were sometimes undetected. In general, few studies addressed non-mass lesions, as they are difficult to discriminate from the background, non-mass lesions having more irregular shapes and being possibly segmental, as reported by [PERSON] et al. [35]. In general, segmentation of mass lesions is more accurate with respect to non-mass lesions, as confirmed by the work of [PERSON] et al. [46]. Thus, investigation on this front, including datasets with a larger variety of lesions, is still necessary. In terms of data type, the most common approach is to use the standard DCE-MRI series; however, there is no standard choice, as the selected time points vary across the analysed studies. With larger datasets and performant hardware, the use of the full sequence encoding the complete enhancement kinetic information is possible; however, this is not always the case. Early approaches, such as the one proposed by [PERSON] et al. [33], tried to capture all available information while reducing data dimensionality. In most other cases, the choice instead involves using a pre-contrast and a post-contrast image or a single-phase subtracted image. [PERSON] et al. [37] suggested, as a compromise, the use of three time points corresponding to the pre-contrast image, and the images at 2 min and 6 min after contrast injection. [PERSON] et al. [46] found that using the second post-contrast image yielded higher performances compared to the first post-contrast image on their dataset. However, the decision to exploit the full kinetic information might also depend on the lesion type to be segmented, as segmentation pipelines for generic lesion types might benefit from more information, whereas models focused on specific lesion types could reduce computational burden by using limited information from the sequence. In general, few studies have investigated the impact and improvement of using multimodal data. For instance, [PERSON] et al. [34] included DWI and PET data, and there are also few multiparametric approaches incorporating T2-weighted images and diffusion image information, such as the work of [PERSON] et al. [51]. Another difference lies in the choice between 2D or 3D approaches, especially concerning the latter CNN possibilities. [PERSON] et al. [46] compared 2D and 3D U-Net models, showing higher performances for the 2D version; however, the results come mainly from comparisons with FCM-produced ground truth rather than 3D ground truth manually segmented by radiologists. Other studies, such as [PERSON] et al. [39], suggest upgrading the models to handle 3D data, since tumour volume is an important predictor for patient survival, and interslice information could also capture more detail in the case of small lesions. 3D approaches are often computationally expensive and require larger datasets; however, patch-based approaches could represent a possible solution to increase dataset size while maintaining a lower computational burden, as experienced by [PERSON] et al. [38]. Thus, the choice of 2D models often reflects the lack of large datasets or computational resources, preferring to use single 2D slices during the training phase and reconstruct the whole segmentation by fusion only at the end of the process. Further investigation should be conducted to understand whether the potential advantages of 3D models are actually realised with the current dataset sizes. Another crucial point is the use of only lesion-positive slices or 3D patches in training and evaluation in many of the reviewed works.