## 4 Discussion  
Model\({}^{\text{PX2}}\) had high accuracies and AUCs (88-91% and 0.91-0.95) when trained and tested on image data from the ProstateX-2 Challenge, similar to published results in the challenge (AUCs of 0.81-0.84) [28; 29; 30; 31]) and in a subsequent study (AUC of 0.91) [18]). Model\({}^{\text{Local}}\), which was trained on image data from our institution, had similarly high  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**ResNet**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Training \& Testing Image Sequence**} \\ \multicolumn{2}{c}{**Algorithm**} & \multicolumn{2}{c}{**Image**} & \multicolumn{2}{c}{**Image**} & \multicolumn{2}{c}{**Training \& Testing Image Sequence**} & \multicolumn{2}{c}{**Training \& Testing Image Sequence**} \\ \multicolumn{2}{c}{**rithm**} & \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Training \& Testing Image Sequence**} & \multicolumn{2}{c}{**Training \& Testing Image Sequence**} \\ \hline \hline \multicolumn{2}{c}{} & \multicolumn{1}{c}{} &  
AUCs (0.82-0.98) when tested on local image data. While these results appear very promising and highly diagnostic, they should be regarded with reservation. The AUCs of these models decreased significantly (0.23-0.50) when trained on image data from one institution and tested on image data from the other institution. A similar pattern was also demonstrated when model performance was assessed using lesions only from the peripheral or transitional zones (PZ: 0.91-0.44, TZ: 0.93-0.61). Homogeneous image data used in the training of models can result in \"overfitting\", where the model is finely customized to the training data and cannot be generalized to \"new\" data [32; 33; 34]. Homogeneity in training image datasets can be a function of MRI institutional protocols and parameters. In this study, both institutions used 3T MRI scanners from the same vendor without an endorectal coil. ProstateX images were obtained on Siemens 3T Skyra and Trio scanners; images at our institution were obtained on a Siemens 3T Skyra. There were differences in the image resolution and slice thicknesses (3.6 mm vs. 3 mm) and in the b-values used in the construction of the ADC map (50/400 vs. 50/800). [PERSON] et al. demonstrated quantitative variations in texture analysis features based on sequence parameters in a phantom model [35]. Small differences in sequence parameters, receiver coils, body habitus, and local magnetic fields, can result in slightly different image quality, signal, and contrast-to-noise ratios. MRI signal intensity values also vary within and across institutions I. MR signal intensity is not correlated with an absolute standard reference, and is dependent on MR hardware, tissue characteristics, pulse sequence, method of k-space filling, etc. Standardization of MRI signal intensity scales may be able to further minimize multi-scanner and multi-institutional image differences, but this has been an ongoing challenge [36]. Signal intensity normalization techniques have been shown to impact prostate cancer radiomics [37]. [PERSON] et al. also reported improved prostate MRI lesion classification (as benign or malignant) in multi-institutional image data following an automated T2-weighted image normalization using both fat and muscle [38]. This MR signal-intensity normalization was not performed in this study given the already high performance of the models. Many of the published machine learning studies with highly accurate models are based on single-institution/single-scanner or single-institution/multi-scanner studies. One systematic review on the performance of machine learning applications in the classification of hrPCA found that 66% (18/27) of studies were performed at a single institution on a single scanner [39].