## 3 Results
### Diagnosis
#### 3.1.2 Imaging and Radiomics  
Radiomics is a method that extracts many features from medical imaging using data-characterization algorithms, with the potential to reveal disease characteristics that are impossible to appreciate by the naked-eye. Many studies assessed the potential benefits of AI in PCa imaging, especially with multi-parametric MRI. These studies are summarized in Table 1. The two main outcomes regarding imaging PCa diagnosis that addressed these studies are detection of malignant lesion and differentiation of significant and non-significant cancers. From MRI images, ML software can be trained with labeled, anatomical, or morphological data. [PERSON] et al. [20] demonstrated that CNN algorithms can automatically diagnose PCa by analyzing thousands of MR images in a matter of seconds. From the prostatic MR imaging, several authors [21; 22; 23; 24] have shown that the use of SVM models allows a PCa diagnosis with accuracies that ranged from 0.85 to 0.91. With another AI approach, two studies [25; 26] used CNN algorithms to bring complementary results which echo those from an SVM approach, with similar performance. [PERSON] et al. [27] showed that anatomical differentiation between prostatic areas (peripheral zone vs central zone) could significantly increase the classification capabilities of ANN models, highlighting the important role of anatomic segmentation. The authors used two ANN algorithms, one trained with information on zonal anatomy of the prostate and the other without to demonstrate that prostate anatomic segmentation improve the performance of PCa diagnosis significantly (96.8% vs. 94.9%; \(p=0.03\)). MRI provides a large amount of quantitative data through textural, morphological analysis. These data are available for analysis after the definition of a region of interest, a segmentation, and an extraction. The most frequently used descriptors are the average and median of grey levels, variance, skewness, or kurtosis. Recent studies included radiomics based on the integration of quantitative and texture features analysis, to help PCa detection on imaging. The work of [PERSON] et al. [28] led to the identification of radiomic features significantly associated with PCa in PZ. [PERSON] et al. [29] showed the incorporation of these radiomic features may be a useful tool in PCa diagnosis if the lesion was more than 0.5 cc. Nevertheless, some studies did not report any benefit from ML model trained with radiomic parameters. [PERSON] et al. [30] found that radiomic machine learning had similar but not superior performance than mean Apparent Diffusion Coefficient (ADC) assessment in distinguishing malignant from benign prostate lesion. Considering all these different approaches, [PERSON] et al. [31] empirically compared different machine classifiers. Within the framework of multicentric data from different MRI, with different acquisition protocols or resolution, boosted Quadratic Discriminant Analysis (QDA) was the more robust and efficient among the models tested for PCa detection, even if it was not the more accurate. Several studies also reported the ability of AI to help radiologists distinguish significant lesions. [PERSON] et al. [32] used CNN architecture to reach this objective with 91.5% accuracy. [PERSON] et al. [33] trained SMV algorithms with MRI variables to classify NS (non-significant) PCa and CS (clinically significant) PCa into two groups in the central gland (CG). [PERSON] et al. [34] demonstrated that adjunction of radiomic features in a ML algorithm could significantly improve (AUC = 98% vs. 88%) the performance of Prostate Imaging-Reporting and Data System (PI-RADS), a score related to clinically significant cancer [35]. In addition, a ML model trained with radiomic features was better than PI-RADS to distinguish transitional zone (TZ) from PCa tumor. [PERSON] et al. [36] also used a trained ML algorithm with radiomic features to differentiate among NS PCa and CS PCa.