## 2 Materials and Methods  
We present the method we designed for prostate cancer Gleason score detection by applying supervised machine learning. We detect Gleason score prostate cancer from prostate medical images i.e., MRIs. As stated in the Introduction, in order to evaluate the efficacy of the proposed approach, a dataset freely available for research purposes is exploited: [[https://wiki.cancerimagingarchive.net/display/Public/PROSTATE-DIAGNOSIS](https://wiki.cancerimagingarchive.net/display/Public/PROSTATE-DIAGNOSIS)]([https://wiki.cancerimagingarchive.net/display/Public/PROSTATE-DIAGNOSIS](https://wiki.cancerimagingarchive.net/display/Public/PROSTATE-DIAGNOSIS)) (accessed on 18 November 2022). The dataset is composed by multiparametric MRI images of patients affected by prostate cancers, confirmed by a pathologist; in particular, T1- and T2-weighted MRIs are considered. The medical images were acquired by exploiting a 1.5 T Philips Achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after I.V. administration of 0.1 mmol/kg body weight of Gadolinium-DTPA (pentetic acid). The dataset contains the segmentation (i.e., the ROI), pathology biopsy and excised gland tissue reports. Moreover, in the dataset for each medical examination, there is a radiology report: the radiomic features are extracted on the ROI area. We consider a total of 670 MRIs: in particular, 338 MRIs were marked by pathologists with a \(3+4\) Gleason Score, 173 were marked by pathologists with a Gleason Score equal to \(4+3\), while 159 were marked by pathologists with a Gleason Score equal to \(4+4\). With regard to the classification analysis, we developed a series of Python scripts invoking the scikit-learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)) accessed on 18 November 2022) library, a machine learning library for the Python programming language network. The radiomic features have been extracted using PyRadiomics ([[https://pyradiomics.readthedocs.io/en/latest/](https://pyradiomics.readthedocs.io/en/latest/)]([https://pyradiomics.readthedocs.io/en/latest/](https://pyradiomics.readthedocs.io/en/latest/)) accessed on 18 November 2022), a Python package aimed at gathering a set of radiomic features directly from MRIs [21]. To this aim, we consider 18 different radiomic features related to three different categories:  
* _First Order_ (FO): this category is related to the voxel distribution of the intensities within the ROI (i.e., the region of interest that in this study is represented by the areas interested by the cancer). We extract 1 feature belonging to the FO category;
* Gray Level Run Length Matrix (GLRLM): the features related to this category consider the grey level run length matrix, aiming to give the size of (homogeneous) runs for each grey level. In addition, in this category, 1 feature is considered;
* _Gray Level Size Zone Matrix_ (GLSZM): the features related to the GLSZM category are aimed at quantifying the gray level zones in a medical image under analysis. With the gray level zone, we refer to a zone defined as the number of (connected) voxels sharing the same gray level intensity. Sixteen different features are considered from this category. The considered features with the relative category are shown in Table 1.  
The aim of the proposed experiment is to understand if the exploited radiomic features are effective at automatically discerning between \(3+4\), \(4+3\) and \(4+4\) Gleason scores by verifying if the radiomic feature vector composed of 18 features is able to detect the Gleason Score from unseen MRIs. The classification is performed by exploiting four different classification algorithms with the radiomic features.