# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 2 Application of Radiomics in the Female Pelvis: From Segmentation to Features Extraction  
To obtain a quantitative reliable result in radiomics studies, it is fundamental to follow a precise radiomics pipeline, composed of five necessary steps: (1) image acquisition, (2) segmentation, (3) features extraction, (4) features selection, and (5) statistical analysis and modelling [8]. All of them should be provided in the most robust way possible, especially to allow reproducibility between studies and centres.  
Image acquisition is the first and one of the most important steps. When setting up a radiomics study, imaging protocol(s) must be well delineated (e.g., contrast media administration, timing of dynamic sequences, mandatory and optional MRI sequences). Data regarding CT and MRI protocols for the study of the female pelvis have been comprehensively detailed in the literature [9; 10].  
Segmentation is a fundamental step in medical image analysis as it allows for the identification and isolation of specific anatomic structures or pathological regions within an image. For computing this aspect, region(s) of interest (ROIs) or volume(s) of interest (VOIs) should be drawn in the interested lesion, organ, or tissue. ROIs can be manually drawn by human readers, semi-automatically or automatically, each of them with advantages and disadvantages.  
Manual segmentation has the main disadvantage of being time consuming: the reader should sketch the contours slice by slice using pointing devices. Moreover, this procedure can lead to a reduction in the robustness of radiomics features [11].  
On the other hand, semi-automatic and automatic approaches are based on available software or custom-made algorithms; in the case of the semi-automatic approach, the preliminary segmentation will be refined by a human reader [11; 12]. Semi-automatic segmentation strategies, providing putative contours that the expert operator is asked to refine or correct, represent a solution to reduce both times and inter-operator variability. Automatic segmentation techniques rely on deep or machine learning (DL and ML, respectively) strategies [13]. These models learn step by step how to segment images if trained on a large amount of already labelled images.  
Once the ROI or VOI is segmented, a wide range of quantitative features can be extracted. These features can be categorized into different groups such as shape-based features, intensity-based features, texture-based features, and spatial-relationship-based features. The process that leads to obtaining this quantitative data is named \"feature extraction\". After that, feature selection is employed to identify the most relevant and discriminative features for a specific clinical task. This step helps reduce the computational burden and improve the robustness of subsequent analysis.  
Finally, the extracted and selected radiomic features are subjected to statistical analysis and modelling. Various statistical methods, machine learning algorithms, or deep learning architectures can be applied to explore the relationships between these features and clinical outcomes or other relevant parameters [14].  
One of the most important limitations to be underlined regards radiomics features obtained by different scanners and institutions. In fact, radiomic features are usually strictly dependent on different factors, such as acquisition data, MR technical features, and contrast media, being employed. For these reasons, whether it is necessary to compare radiomics data deriving from different scanners or from multiple institutions, it is of utmost importance to provide a post-processing step, in order to reduce potential bias. Different methods were proposed, including denoising [15], N4 bias field correction [16], voxel size resampling and interpolation, discretization [17], and ComBat harmonization [18]. Technical information regarding these aspects are out of the scope of this present review.