Urine cytology is a non-invasive, cost-efficient, and sensitive test to detect high-grade urothelial carcinoma. The Paris System (TPS) for Reporting Urinary Cytology is an evidence-based system that uses the risk of malignancy to guide patient management. Since its inception, TPS has standardized urine cytology reports, facilitating communication among pathologists and between pathologists and clinicians. It is imperative to correlate the urine cytology findings with the concurrent tissue sample to avoid false-negative and false-positive results when possible. Several ancillary tests and artificial intelligence algorithms are being developed to increase the accuracy of urine cytology interpretation.

BACKGROUND: Standardized basic morphology and the algorithmic approach make the Paris System (TPS) for Reporting Urinary Cytology understandable and applicable. This study examined how well the TPS categories are understood by pathology residents and how well these criteria are enabling them reaching accurate diagnosis.
METHODS: A hundred consecutive cases representing all categories were selected. Authors reevaluated slides using TPS regardless of their original diagnosis. In the next step, the TPS was explained to four residents and trained them by five optimal urine cytology samples from each category. Then they were asked to diagnose the selected slides according to the TPS. The diagnoses were compared to authors. The agreement was assessed using kappa. Discordant diagnoses were classified as high and low impact based on potential on clinical practice.
RESULTS: The sensitivity of authors was 62.8%, and residents\' were 24-31.8%. The specificity of authors was 98.8%, and residents\' were 82.3-92.8%. Reproducibility of TPS was 40-46%. Kappa values were below 0.40 except for one resident. The highest rate of concordance was for negative for high-grade urothelial carcinoma (NHGUC): authors assigned 38 NHGUC (35 biopsy-proven benign cases). Twenty to twenty-six of them were assigned as NHGUC by residents. While authors assigned 42 cases as suspicious for high-grade urothelial carcinoma (SHGUC) or high-grade urothelial carcinoma (HGUC) (35 biopsy-proven malignant cases), residents assigned 22-29 of them. Discordant diagnosis with high clinical implication was 56-63%.
CONCLUSIONS: Diagnostic accuracy rates of junior pathology residents using the TPS were unsatisfactory. The best agreement was observed in NHGUC and HGUC categories. Combining HGUC and SHGUC doubled the sensitivity of residents.

Urine cytology is a long-used technique for the detection of high grade neoplastic urothelial lesions. Since 2016, «The Paris System» classification has revolutionized this field by introducing a standardized terminology widely adopted by cytopathologists and urologists. In this article, we explain this classification and discuss its impact on the clinical management of patients with urothelial lesions, as well as its role in the secondary prevention of these lesions.
La cytologie urinaire est une technique utilisée depuis longtemps dans la détection des lésions urothéliales tumorales de haut grade. Depuis 2016, la classification «The Paris System» a révolutionné ce domaine en introduisant une terminologie standardisée largement adoptée par les cytopathologistes et les urologues. Dans cet article, nous expliquons cette classification et discutons de son impact sur la prise en charge clinique des lésions urothéliales, ainsi que son rôle dans la prévention secondaire de ces lésions.

OBJECTIVE: Recently, the nuclear area has attracted attention as a morphological parameter to differentiate high-grade urothelial carcinoma (HGUC) cells from benign reactive cells. The nuclear long diameter (NLD) strongly correlates with the nuclear area and is easy to subjectively estimate. Therefore, this study examined the usefulness of the NLD-to-neutrophil diameter ratio for detecting HGUC cells in urine cytology.
METHODS: This study included 29, 26 and 18 patients with HGUC, glomerular disease and urolithiasis respectively. An image analysis system was used to measure the NLD of HGUC and benign reactive cells (reactive renal tubular cells and reactive urothelial cells) and the neutrophil diameter that appeared in the voided urine in these cases. The NLD index was calculated using the NLD-to-neutrophil diameter ratio. We subsequently compared HGUC and benign reactive cells with respect to NLD and NLD indices. In addition, the HGUC cell group and benign reactive cell group were compared by selecting the five cells with the largest NLD and NLD index on each slide.
RESULTS: The NLD and NLD indices of HGUC cells were significantly higher than those of benign reactive cells in all cells and in the five cells with the largest NLD and NLD indices. The cut-off value of the NLD index for detecting HGUC cells was 1.25 in all cells and 1.80 in the five cells with the largest NLD index.
CONCLUSIONS: The NLD index is a useful parameter that can be introduced into routine microscopic examinations to differentiate HGUC cells from benign reactive cells.

The second version of the Paris System for reporting urine cytology was published in 2022. It follows the first version of 2016, which was very successful and widely adopted by many cytopathologists from different countries. Thus, numerous publications using the Paris System have made possible to refine the criteria as well as discussing the limits. The diagnostic accuracy of urinary cytology is high for detection of high-grade urothelial carcinoma, but not for low-grade carcinoma where there are few cytological abnormalities. So, the chapter individualizing low-grade urothelial neoplasms was deleted; the latter were included in the category \"negative for high-grade urothelial carcinoma\". Indeed, the risk of malignancy is replaced by the risk of high-grade urothelial carcinoma. A new chapter has been devoted to urothelial tumors of the upper tract. Finally, the pitfalls linked to cellular degeneration are discussed for each category. The risk of high-grade malignancy associated with each category will help communication with the clinician and help patient care.

BACKGROUND: Urine cytology, although a useful screening method for urothelial carcinoma, lacks sensitivity. As an emerging technology, artificial intelligence (AI) improved image analysis accuracy significantly.
OBJECTIVE: To develop a fully automated AI system to assist pathologists in the histological prediction of high-grade urothelial carcinoma (HGUC) from digitized urine cytology slides.
METHODS: We digitized 535 consecutive urine cytology slides for AI use. Among these slides, 181 were used for AI development, 39 were used as AI test data to identify HGUC by cell-level classification, and 315 were used as AI test data for slide-level classification.
METHODS: Out of the 315 slides, 171 were collected immediately prior to bladder biopsy or transurethral resection of bladder tumor, and then outcomes were compared with the histological presence of HGUC in the surgical specimen. The primary aim was to compare AI prediction of the histological presence of HGUC with the pathologist\'s histological diagnosis of HGUC. Secondary aims were to compare the time required for AI evaluation and concordance between the AI\'s classification and pathologist\'s cytology diagnosis.
CONCLUSIONS: The AI capability for predicting the histological presence of HGUC was 0.78 for the area under the curve. Comparing the AI predictive performance with pathologists\' diagnosis, the AI sensitivity of 63% for histological HGUC prediction was superior to a pathologists\' cytology sensitivity of 46% (p = 0.0037). On the contrary, there was no significant difference between the AI specificity of 83% and pathologists\' specificity of 89% (p = 0.13), and AI accuracy of 74% and pathologists\' accuracy of 68% (p = 0.08). The time required for AI evaluation was 139 s. With respect to the concordance between the AI prediction and pathologist\'s cytology diagnosis, the accuracy was 86%. Agreements with positive and negative findings were 92% and 84%, respectively.
CONCLUSIONS: We developed a fully automated AI system to assist pathologists\' histological diagnosis of HGUC using digitized slides. This AI system showed significantly higher sensitivity than a board-certified cytopathologist and may assist pathologists in making urine cytology diagnoses, reducing their workload.
RESULTS: In this study, we present a deep learning-based artificial intelligence (AI) system that classifies urine cytology slides according to the Paris system. An automated AI system was developed and validated with 535 consecutive urine cytology slides. The AI predicted histological high-grade urothelial carcinoma from digitized urine cytology slides with superior sensitivity than pathologists, while maintaining comparable specificity and accuracy.

BACKGROUND: In urinary diversion after radical cystectomy, the incidence of recurrent urothelial carcinoma (UC) in upper urinary tract or urethra are reported in 2%-17% of the patients. Urine cytology plays a pivotal role in detecting the recurrence of UC. However, cytologic diagnosis in urinary diversion including neobladder is often challenging due to significant degenerative changes and necro-inflammatory background. Since the proposal of The Paris System (TPS) for reporting cytology, the utility of TPS in urinary diversion specimen has not been studied yet. The objective of this study is to evaluate the diagnostic usefulness of TPS compared with the original diagnosis and correlate with the matched histopathological results.
METHODS: Urinary diversion cytology specimens with concurrent or subsequent biopsy or resection at EUMC in recent 16 years (from January 2002 to December 2018) are retrospectively reviewed and reclassified according to TPS criteria. The TPS categories and the original diagnoses were compared and correlated with follow-up histology.
RESULTS: Concurrent or subsequent biopsy or resection within a 6-month period was available in 45 cases from 28 patients. When applying TPS, the rate of atypical and suspicious categories decreased by 13.4% and 11.1%. Using TPS increased the value of sensitivity, NPV, and accuracy to 93.75%, 93.75%, and 90.91%, respectively.
CONCLUSIONS: Application of TPS reduced the rate of indeterminate diagnoses and moreover, improved the sensitivity and accuracy of urinary diversion cytology. Therefore, we believe that diversion urine cytology diagnosis according to TPS is useful to screen patients for detection of recurrence in routine clinical practice.

Urine cytology is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological examinations for assessment and early detection of recurrence, beyond identifying a positive finding that requires more invasive methods to confirm recurrence and decide on therapeutic options. Because screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists, and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk-stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer.
In this study, a computational machine learning tool, AutoParis-X, was leveraged to extract imaging features from urine cytology examinations longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk.
Results indicate that imaging predictors extracted using AutoParis-X can predict recurrence as well or better than traditional cytological/histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence.
Further research will clarify how computational methods can be effectively used in high-volume screening programs to improve recurrence detection and complement traditional modes of assessment.

During endoscopic procedures for suspected urothelial tumors of the upper urinary tract, radiographic imaging using an iodinated contrast medium is often required. However, following ureteropyelography, we detected changes in cytology characteristics not correlating with real cytology findings in naive urine. The aim of our study was to assess cytology changes between naive and postcontrast urine according to The Paris System of cytology classification. Methods: We prospectively assessed urine samples from 89 patients (23 patients with histologically proven urothelial cancer and 66 healthy volunteers). The absence of malignancy was demonstrated by CT urography and/or ureteroscopy. The study was single blind (expert cytopathologist) and naïve Paris system for urine cytology assessment was used. Furthermore, additional cytological parameters were analyzed (e.g., specimen cellularity, degree of cytolysis, cytoplasm and nucleus color, chromatin and nucleo-cytoplasmic ratio). Results: Our study showed statistically significant differences when comparing naïve and postcontrast urine in healthy volunteers (only 51 % concordance, p = 0.001) versus malignant urine specimens (82 % concordance). The most important differences were in the shift from The Paris System category 2 (negative) to 1 (non-diagnostic) and from category 2 (negative) to 3 (atypia). Other significant changes were found in the assessment of specimen cellularity (p = 0.0003), degree of cytolysis (p = 0.001), cytoplasm color (p = 0.003), hyperchromasia (p = 0.001), course chromatin (p = 0.002), nucleo-cytoplasmatic ratio (p = 0.001) and nuclear borders\' irregularity (p = 0.01). Conclusion: Our unique study found crucial changes in the cytological assessment of naive and postcontrast urine and we confirm that postcontrast urine is more often assessed as abnormal, suspect or non-diagnostic. Therefore, before urine collection for cytology, the clinician should avoid administration of iodinated contrast into the urinary tract.

Urine cytology of urinary tract condylomas has not been systematically studied. We analyzed cytologic features of urinary tract condylomas and evaluated potential diagnostic challenges and pitfalls. We retrospectively reviewed urine cytology of urinary tract condylomas from 2 academic institutions (2015-2022). Among 20 patients with urinary tract condylomas, 6 had urine cytology (2 samples in 1 patient), including 3 men and 3 women (mean age, 74.3 years; range, 65-86 years). Original interpretations were negative for high-grade urothelial carcinoma (NHGUC; n = 4), atypical urothelial cells (n = 1), reactive urothelial cells (n = 1), and negative for malignancy (n = 1). Squamous cells were noted in 3 cases, atypical squamous cells (ASC) consistent with low-grade squamous intraepithelial lesion (LSIL) were noted in 1 case, and in 3 cases, the presence of squamous cells was not mentioned. All urines were reclassified according to The Paris System as NHGUC. Specimens were composed of benign urothelial cells and groups or isolated ASC consistent with LSIL (n = 4), atypical keratinized squamous cells (n = 2), and ASC that did not meet LSIL criteria (n = 1). The LSIL cells showed nuclear enlargement (n = 4), hyperchromasia (n = 4), perinuclear halo (n = 3), nuclear membrane irregularity (n = 4), orangeophilic cytoplasm (n = 3), and binucleation (n = 4). The atypical keratinized squamous cells showed hyperchromasia (n = 2), nuclear membrane irregularity (n = 2), keratin pearls (n = 2), and binucleation (n = 1). The ASC that did not meet LSIL criteria showed nuclear enlargement and orangeophilic cytoplasm. Many urinary tract condylomas (57%) demonstrate classic LSIL features in urine cytology. Less frequent cases can mimic keratinizing squamous cell carcinoma (28%) or demonstrate ASC not diagnostic of LSIL (15%).