## 1 Introduction
### Applications of Breast MRI
A mass lesion is a 3D region larger than 5 mm, characterised by its shape (round, oval, lobulated, irregular), margins (smooth, irregular, spiculated), and internal enhancement (homogeneous, heterogeneous, etc.). In contrast, enhancements of size \(<\) 5 mm are considered foci and cannot be characterised morphologically. Conversely, non-mass-like enhancements have no space-occupying effects, covering an area that is neither a mass nor blood vessels. In this case, the lesion is characterised by internal enhancement, symmetry, and overall enhancement distribution [9]. Mass lesions can often be discriminated as malignant or benign simply by considering the shape and margins, with benign lesions having a regular appearance. On the other hand, for non-mass enhancement, there is an overlap in the appearance, making the differentiation particularly challenging [10]. In the era of precision medicine, new tools capable of enhancing clinical practice are being developed. One area of growing interest is the radiomic process, which involves extracting quantitative features from medical images, converting these images into mineable data that can support decision-making [11]. Numerous studies have combined radiomics with machine learning techniques to classify lesions, improving screening procedures by distinguishing benign from malignant lesions. These studies have also focused on classifying different molecular subtypes associated with varying risk factors, treatment responses, and disease progression, as well as investigating the classification of receptor status and proliferation markers [12]. Moreover, the use of radiomics has also been investigated for predicting the response to NAC, demonstrating improvements in predictive models when integrated with radiomic features as reported by [13; 14; 15]. The radiomic process involves a series of steps: image acquisition and reconstruction, identification and segmentation of the region of interest, image preprocessing, extraction of radiomic features, dimensionality reduction, and model development. Of all these steps, segmentation is the most critical and challenging, as all subsequent features are generated based on the delineated region and are highly dependent on its accuracy [11]. This poses a significant challenge, as many tumours have indistinct borders, and variations in segmentation can undermine the reproducibility of the extracted features and subsequent analyses. In general, segmentation of malignant lesions with irregular shapes and non-mass enhancement is particularly challenging and time-consuming, even for experienced operators. Traditionally, segmentation is performed by highly experienced radiologists who have worked in the field for many years. However, radiomic features are significantly affected by variations in operator segmentations, with only a small fraction of features remaining robust under inter-observer manual segmentation variability [16; 17]. Such differences can undermine the reproducibility and robustness of the obtained results, leading to poorly generalisable models that are difficult to share and implement in clinical practice. The need for advanced diagnostic and prognostic tools is becoming increasingly important, particularly in countries where healthcare is less accessible, as interest grows alongside the development of the artifical intelligence (AI) era. Furthermore, the use of more complex ML models demands larger datasets to effectively tune parameters and avoid overfitting. However, manual segmentation is highly time-consuming. To address both reproducibility issues and the substantial time investment required for segmentation, as well as to facilitate the creation of larger datasets, research into automated methods for segmentation has been intensifying. The growing interest in ML techniques has already been examined in various studies [12; 18; 19; 20; 21], which have highlighted the diversity of applications for machine learning and deep learning in breast cancer, ranging from diagnostic tools to predictive models.