Faculty Bibliography

Introduction: The utility of The Paris System (TPS) in diagnosing low-grade urothelial neoplasm (LGUN) on urine cytology is controversial due to the strict requirement for fibrovascular cores, and low sensitivity/specificity. Many LGUNs are classified as atypical urothelial cells (AUC) on cytology, which compromises the performance and utility of TPS. Here, we studied cytomorphologic features of LGUN in urine samples to determine which features were commonly observed.
Material(s) and Method(s): Twenty-two urine cytology cases with corresponding (within 2 months) LGUN histologic diagnosis were retrieved for this pilot study and were evaluated by one cytopathologist for the presence of clusters, cercariform cells, hyperchromasia, irregular nuclear rim, papillary architecture +/-fibrovascular core, and nucleus:cytoplasm (N:C) ratio (Figure 1). Hierarchical cluster analysis (Ward's Method) was used to classify the features.
Result(s): Of the 22 urines, one was voided (4.5%) and 21 were instrumented (95.5%). Majority (77.3%) were diagnosed as AUC, 1 was suspicious for urothelial carcinoma (4.5%), 4 cases were graded as LGUN (18.2%, Table 1). Clustering analysis demonstrated that the morphologic features abundantly present in the urine specimen of LGUN included: clusters (77.3%), N:C ratio >50% (85.4%), and papillary architecture without a core (72.7%). The features that were mostly absent in LGUN specimens included: irregular nuclear rim (0%), papillary formation with a core (0%), hyperchromasia (9.1%), coarse chromatin (22.7%), and cercariform cells (36.3%). (Table 2).
Conclusion(s): Papillary formation with a fibrovascular core, the most convincing feature of LGUN, was not present in our pilot cohort of LGUN urines. However, our study describes additional cytomorphologic features that may be useful in identifying LGUN in urine cytology. Our research will continue with the evaluation of a larger cohort of LGUN cases with corresponding urine cytology in order to further investigate these findings

Prostate cancer is the most frequently diagnosed cancer in males and its development and progression remains an important area of study. Recently, long non-coding RNAs (lncRNAs) have been evidenced as key players in cancer pathogenesis. Specifically, dysregulation of long non-coding RNA (lncRNA) expression has shown to affect tumor proliferation and metastasis, acting as either tumor suppressors or oncogenes. However, its specific mechanisms and functions in prostate cancer remain unclear. This review provides an overview of currently available information on prostate cancer-related lncRNAs, including GAS5, GAS-007, MEG3, PCA3, PCAT14, PCAT1, PVT1, UCA1, SChLAP1, MALAT1, HOTAIR, and NEAT1. Notable tumor growth inhibitors include GAS5 and MEG3. GAS5 is evidenced to interfere with the AKT/MTOR signaling pathway through targeting microRNA mir-103. MEG3, however, is proposed to inhibit the cycle, sponge miR-9-5p, and induce gene silencing. PCAT1, PVT1, and UCA1 are important tumor growth promoters. PCAT1 is indicated to be a transcriptional repressor, a mir-145-5P sponge, and a P13K/AKT pathway activator. Studies suggest that PVT1 acts via microRNA targeting and regulating proliferating cell nuclear antigen. UCA1 may sponge miR-204 and miR-331-3p as well as regulate myosin VI. Thorough understanding of these lncRNAs may elucidate new aspects of prostate cancer pathology and serve a pivotal role in developing novel diagnostic and prognostic techniques.

Background: Artificial intelligence (AI) has been increasingly used in surgical pathology to assist pathologists for diagnosis, grading and staging purposes. We describe in this report a proof of concept study that pathologists themselves could use a commercial deep learning software to develop an AI model to assist the classification of flat urothelial lesions.
Design(s): 227 flat urothelial lesions including normal (N) (66), benign reactive (BR) (53), dysplasia (D) (49) and carcinoma in situ (CIS) (59) from one institution (TMC) were reviewed, and representative H&E micrographs of taken at 10X magnification by pathologists on various cameras attached to microscopes. Fifty % of these images were imported into Cognex VisionPro Deep Learning 2.0 software tool, which was trained to develop a flat urothelial classifier (Model 1) to distinguish between N, BR, D and CIS. The classifier comprised of a chain of 3 deep learning models to first remove the blank space in the images, to identify urothelial regions and finally to classify the urothelial regions. An F-score was used to measure the accuracy of the model to classify the images of flat urothelial lesions. 191 micrographs of flat urothelial lesions from a different institution (NYU), including N (35), BR (52), D (47) and CIS (57), were used as validation cases. Fifty % of latter images were used to train a new model (Model 2) based on the Model 1.
Result(s): The F-score for Model 1 on TMC cases was 96.1%. 97.1%, 85.2%, 100% and 100% of cases in N, BR, D and CIS were correctly classified. When Model 1 was used on NYU cases, the overall F-score was 44.9%. Model 1 was retrained using 50% of NYU images to generate Model 2. The F-score for Model 2 on NYU images improved to 86.8%, while it was 88.9% on TMC images.
Conclusion(s): This proof of concept study confirms that a pathologist with little background in deep learning software development can use a commercially available deep learning software to develop a robust AI model to assist the classification of flat urothelial lesions. While a model developed using one set of images from one lab may not be directly applied to images from a different lab, it could nevertheless be retrained using the images from the second lab to achieve high classification accuracy. Studies are underway to develop AI models for flat urothelial classification on whole slide images

Background: Magnetic resonance imaging (MRI) targeted prostate biopsy has become an increasingly common method of diagnosing prostate cancer. Previous study from our institution demonstrates the biopsy global Gleason grade (gGG) and highest Gleason grade (hGG) show substantial concordance with the radical prostatectomy Gleason grade (RPGG) while the discordance predominantly comprise of upgrading in gGG and downgrading in hGG. We performed a larger cohort focused analysis on the agreement of gGG and hGG to the RPGG when they differ.
Design(s): A retrospective review of radical prostatectomy specimens between 10/2016 and 12/2020 from our institution with prior MRI-targeted biopsies was conducted. A gGG (aggregate GG of all positive cores) and a hGG (highest GG in any core) was assigned to each MRI-targeted lesion. Only cases with different gGG versus hGG were selected for further analysis. The concordance of gGG and hGG with RPGG was evaluated using kappa coefficient analyses. The power of pre-biopsy PSA and PIRADS scores to predict upgrading based on gGG was also analyzed.
Result(s): Of the 489 radical prostatectomy specimens with prior MRI-targeted biopsies, 82 cases (17%) differed in gGG versus hGG. Using the gGG, 33 cases (40%), 46 cases (56%), and 3 cases (4%) were upgraded, unchanged, and downgraded at radical prostatectomy, respectively (Kappa = 0.302, weighted Kappa = 0.334). Based on the hGG, 9 cases (11%), 24 cases (29%), and 49 cases (60%) were upgraded, unchanged, and downgraded at radical prostatectomy, respectively (Kappa = 0.040, weighted Kappa = 0.198) (Figure 1). When stratified by RPGG, gGG shows the best concordance in RPGG2 and RPGG3 lesions. The hGG resulted in substantial downgrading at RPGG4 or less and upgrading at RPGG5 (Figure 2). No significant difference in the mean PSA [H(2) = 5.89, p = 0.053] or PI-RADS score [H(2) = 4.48, p = 0.107] was found among the cases upgraded, unchanged, and downgraded based on the gGG. Neither the pre-biopsy PSA (OR = 1.92, 95% CI = 0.65-5.64, p = 0.117) nor the PI-RADS score (OR = 0.899, 95% CI = 0.31-2.607, p = 0.423) was predictive of upgrading based on gGG.
Conclusion(s): When the gGG and hGG differ, the gGG correlates better with the RPGG than the hGG in the majority of cases for RPGG2 and RPGG3 lesions (46 cases, 74%). It results in upgrading in high grade lesions (GG4 and GG5) with potentially minimal impact on clinical management. Further studies are needed to substantiate a standard GG reporting method for MRI-targeted prostate biopsies