## 4 Discussion
Additionally, the models had high predictive accuracy when using a single sequence, regardless of whether it was the T2 or ADC map alone. The single-institutionally trained models' AUCs were slightly higher when using the T2 sequence compared with the ADC map, which could be a function of its higher resolution (0.93-0.96 vs. 0.91-0.82). There were minimal to no gains in performance when both sequences were used and integrated by the algorithm, suggesting that a single sequence may be sufficient. While most prostate ML studies have used both T2 and ADC images in some manner, their optimized independent or integrated utilization in prostate ML has yet to be determined. The mpMRI consists of multiple sequences in multiple planes; there would be significant time and cost savings if MR exams could be shortened to fewer sequences with such models. There are several limitations to this study given some differences between the datasets obtained from the two institutions. The Gleason score of the ProstateX-2 lesions came from MRI-guided in-bore biopsy, while the local institutional GS came from surgical pathology of prostatectomy specimens. MRI-guided biopsy was unavailable at our institution, and lesions initially underwent MR-US fusion-guided biopsy. Given the risk of tumor under-sampling and Gleason under-scoring with US-fusion biopsies, we used prostatectomy specimens to obtain the \"ground truth.\" While a MRI-guided in-bore biopsy overcomes many of the shortcomings of a MR-US fusion-guided biopsy and is recognized as being superior to US-guided-MRI fusion biopsy [51], it nevertheless has the potential risk of under-sampling. Another limitation is that the sample size of both the original publicly available dataset and our local dataset are small. Ideally, thousands of scans, if not every scan potentially available at the time, would be used to train the model. However, due to practical limitations, the cohort was limited by the scan availability of each institution. The Gleason score of the prostatic lesions were also assigned by different pathologists at different institutions. While each Gleason score has defined features, some inter-observer variability and subjectivity is recognized in the pathology literature [52]. This study defined hrPCA as GS \(\geq 4+3\), similar to other major studies [6; 7], in order to identify patients who would definitively benefit from treatment; however, other studies have defined clinically significant prostate cancer as GS \(\geq\) 3 + 4 [53]. By using prostatectomy patients, the local institutional cohort had a higher initial incidence of hrPCA and likely had more skew towards higher PIRADS lesions; the PX2 dataset consisted of lesions \(\geq\) PIRADS 3, but lesion-specific PIRADS scores were not included in the dataset. PIRADS 2 pseudo-lesions were introduced into the local cohort to augment the low-risk/benign subset of lesions in this group. Such truly benign lesions were unlikely to be within the ProstateX-2 image dataset and modelPX2 was not trained on them, which also likely impacted modelPX2's performance on local institutional image data (T2 AUC 0.44). ProstateX-2 lesions were identified by a radiologist using PIRADS version 1, while local institutional lesions were identified using PIRADS version 2.0. ProstateX-2 lesions consisted of 3.6 mm thick T2 sequences, while the local institutional exams had 3 mm thick T2 slices. The ratio of peripheral to transitional zone tumors at the two institutions could not be balanced, although sub-analysis was performed and demonstrated similar results when compared to the full cohort analysis. While these differences between the two institutional groups also likely had an impact on the results, these differences will also be encountered by other single-institutionally trained models in the future should they be applied to multi-institutional image data. PIRADS scoring criteria should be revised and models trained on PIRADS versions 2 and 2.1 may be applied to image data interpreted using the future version 3.