## 3 Results
### Diagnosis
#### 3.1.2 Imaging and Radiomics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Author**} & **Al Model** & \begin{tabular}{c} **Patients** \\ **(Training/Validation/Testing)** \\ \end{tabular} & **Parameters** & **Predicted Outcomes** \\ \hline [PERSON] et al. [2018] & CNN algorithm (U-net & 335 PCa patients (001/34) & T2-weighted (T2-w) MR images & Detection of PCa on MRI with CAD algorithm required 5.5 h) to learn to analyze 2 million images, operating at approximately 30 ms/ image for evaluation and showed AUC of 0.645 and 0.636, 16/7 and 7/7 patients \\ \hline [PERSON] et al. [2018] & SVM & 34 PCa patients & \begin{tabular}{c} T2-w and dynamic \\ contrast/channel (ICD) IT+ MR \\ images labeled as malignant, benign, or normal regions \\ \end{tabular} & T2 values alone achieved a diagnostic AUC of 0.85 (0.27-0.92) with a significantly improved discrimination performance of 0.98 (0.81-0.95), when combined with DCE IT-w features. \\ \[PERSON] et al. [2018] & SVM & 42 cancer ROIs, 49 suspicious ROIs, and 124 benign ROIs & For the discrimination of the malignant versus nonmalignant tissues, AUC was 0.89 (0.81-0.94) and 0.82 (0.73-0.90) for the discrimination between malignant versus suspicious tissues. \\ \hline [PERSON] et al. [2018] & Computer Vision and Med Imaging Graph, 2015 [2018] & SVM and lesions & MR images with anatomical and pharmacokinetic features & For the discrimination of the malignant versus nonmalignant tissues, AUC was 0.97 (18-3d quartile quartile; 0.83-0.989), with a median sensitivity and specificity of 0.84 (18-3d quartile; target-0.73-0.98) and 0.86 (18-3d quartile; 0.76-0.95) respectively. \\ \hline [PERSON] et al. [2018] & Unsupervised ML & 37 PCa patients & 4 images features on T2-W MRI & CAD detection and localization of PCa within the peripheral zone with 86\% of accuracy, 87\% and 86\% of sensitivity and specificity respectively. \\ \hline [PERSON] et al. [2018] & \begin{tabular}{c} DCNN vs. a non-DL \\ with SIFT image \\ feature and \\ bag-of-word (BoW) \\ \end{tabular} & 172 PCa patients & \begin{tabular}{c} Clincoputhological and MR \\ images \\ \end{tabular} & \begin{tabular}{c} Statistically higher area under the recent operating characteristics curve (AUC) for DCNN than not (p.0) = 0.007 (0.007) = 0.001), the AUCs were respectively 0.84 (0.53-0.78-0.89) for DL method and 0.07 (0.95-0.63-0.77) for non-deep learning method. \\ \end{tabular} \\ [PERSON] et al. [2018] & \begin{tabular}{c} DCNN \\ (Python skin-learn \\ and R software) \\ \end{tabular} & 195 (159/17/19) patients with localized PCa & MR images labeled as “cancer” or \"no cancer\" & Strong diagnostic performance for DCNN in distinguishing between PCa and non-cancerous issues: AUC of 0.94 (95.15 0.876-0.994), 87.07% and 90.6% of sensitivity and specificity respectively, PPV of 87.0%, and NPV of 90.6%. Igniting P-KADS and DCNN provided additional net benefits rather than in particular. \\ \hline [PERSON] et al. [2018] & 2 ANN models & 18 PCa patients 530s voxels & \begin{tabular}{c} Model 1: 256 MRI variables \\ model 2: 256 MRI variables + 4 \\ anatomical variables (percentage of \\ PZ, TZ, U, and O). \\ \end{tabular} & \begin{tabular}{c} By adding training with anatomic segmentation PCa, performance of model 12: 256 MRI variables + 4 model 2 was significantly higher than model 1 with respectively AUC \\ anatomical variables (percentage of \\ PZ, TZ, U, and O). \\ \end{tabular} \\ [PERSON] et al. [2018] & \
\begin{tabular}{c} ANN \\ (In-house software) \\ \end{tabular} & 71 patients (35 with PCa and \\ Life Sci 2015 [2018] &
\begin{tabular}{c} 12 radiomic features on T2-W \\ (in-house software) \\ \end{tabular} & 12 radiomic features on T2-W & In the PZ, 10/12 radiomic features had significant difference (\(p<0.01\)) between PCa and non- PCa. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the studies reporting on Artificial Intelligence (AI) in the imaging prostate cancer diagnosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Al Model** &