###### Abstract  
The Gleason score was originally formulated to represent the heterogeneity of prostate cancer and helps to stratify the risk of patients affected by this tumor. The Gleason score assigning represents an on H&E stain task performed by pathologists upon histopathological examination of needle biopsies or surgical specimens. In this paper, we propose an approach focused on the automatic Gleason score classification. We exploit a set of 18 radiomic features. The radiomic feature set is directly obtainable from segmented magnetic resonance images. We build several models considering supervised machine learning techniques, obtaining with the RandomForest classification algorithm a precision ranging from 0.803 to 0.888 and a recall from to 0.873 to 0.899. Moreover, with the aim to increase the never seen instance detection, we exploit the sigmoid calibration to better tune the built model. prostate; cancer; radiomics; machine learning; classification +
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individual discrete well-formed glands (this category is representing the less aggressive one); (ii) \(3+4\): mainly composed by well-formed glands, but a small component of poorly formed, fused and or cribribiform glands appears; (iii) \(4+3\): mainly composed by poorly formed, fused and/or cribribiform glands with lesser well-formed glands; (iv) \(4+4\): usually composed only by poorly formed, fused and/or cribribiform glands; from this score, the cancer is considered as aggressive [8; 9; 10]. Prostate cancer diagnoses are essentially based on rectal exploration, PSA dosage, imaging techniques, and histological reports of prostatic needle biopsies. Multiparametric MRI plays a fundamental role in detecting prostate cancer in patients with clinical suspicion: the now consolidated multiparametric protocol, which includes T2-weighted, T1-weighted anatomical sequences in perfusion (Dynamic Contrast-Enhanced Magnetic Resonance Imaging, DCE-MRI) and diffusion weighting (Diffusion-Weighted Imaging, DWI), can provide a combination of anatomical, biological, and functional information necessary for a more precise definition of suspicious lesions. The indication to perform a prostate biopsy for diagnosis by histopathological examination is currently being formulated based on clinical and/or laboratory suspicion (pathological changes in biomarkers) and/or positive outcome of the multiparametric MRI [11]. Nowadays, multiparametric MRI, through its morphological T2 sequences, DWI, and functional MR modalities (MRSI), achieves the highest sensitivity (0.84) and negative predictive value (0.93) in prostate cancer detection [12]. The prostate is typically assigned a radiological score on MRI multiparametric images, which indicates the likelihood of a clinically significant cancer of the gland: this score is called PI-RADS [13]. In patients with a moderate/high PI-RADs score or increased PSA \(>10\), a prostate biopsy to confirm the diagnostic suspect might be suggested [14]. The information reported in the pathology report is extremely important to define the patient's therapeutic perspectives and prognosis. In the case of biopsy specimens positive for cancer, the pathologist assigns an individual Gleason score for each needle biopsy specimen, taking into account the site of collection of each of them. In order to give patients a favorable prognosis, early detection is crucial to avoid the disease progression and the involvement of other organs [15]. In recent years, radiomic has been emerging as a new research field in which medical images, for instance, magnetic resonance imaging (MRIs), are converted into numbers [16]. Radiomic has the potential to uncover disease characteristics that fail to be recognised by the naked human eye; in this way, it is possible to provide valuable information for personalised therapy [17]. Machine learning methods demonstrated the ability to learn feature representations automatically from numeric data under analysis.