Research ArticleClinical Studies
Open Access

Novel UF-5000-based Detection, Integrating Inflammatory Parameters, for Urothelial Carcinoma of the Urinary Bladder

KAZUHIRO OKADA, YOSHIKI NAITO, SAORI IRIE, CHIKA NAKAMOTO, KENJI KUBOYAMA, TOSHIKI TARUMIZU, MISAKI IKEDA, YUKO TAKUMA, RYO MAKINO, AKIHIKO KAWAHARA and HIROYUKI KAWANO
Anticancer Research August 2025, 45 (8) 3531-3541; DOI: https://doi.org/10.21873/anticanres.17714

Abstract

Background/Aim: Urothelial carcinoma (UC) is the most common epithelial bladder malignancy. Although urine cytology is widely used for screening, its sensitivity in detecting low-grade UC is limited. This study evaluated the diagnostic accuracy of the research-use parameter “Atyp.C” from the fully automated urine particle analyzer UF-5000, in combination with the neutrophil-to-lymphocyte ratio (NLR), for UC detection.

Patients and Methods: Urine samples from 57 noninvasive UC, 41 invasive UC, and 61 non-UC cases (n=159) were examined at Kurume University Hospital between 2020 and 2023. Specimens with atypical cells were excluded from the study. Receiver operating characteristic curve analysis was conducted using Atyp.C data from the UF-5000 to determine the optimal cutoff value. An UC detection algorithm incorporating the NLR was examined using the AI platform DataRobot, and its diagnostic accuracy was assessed.

Results: The diagnostic accuracy for invasive UC was assessed using an Atyp.C cut-off of 0.1/μl, yielding an area under the curve (AUC) of 0.824, sensitivity 70.7%, and specificity 90.3%. Noninvasive UC showed lower accuracy (AUC=0.565; sensitivity, 22.8%; specificity, 90.3%). Incorporating NLR improved invasive UC detection (AUC=0.892; sensitivity, 75.0%; specificity, 100%). NLR was the most influential factor in UC detection.

Conclusion: The UF-5000, when combined with the NLR, may enhance UC screening and contribute to a more effective diagnostic strategy.

Keywords:

Introduction

Urothelial carcinoma (UC) is the most common malignancy originating in the urinary bladder (1). UC exhibits invasive growth with inflammatory cell infiltration and hemorrhage. The neutrophil-to-lymphocyte ratio (NLR) has been reported as an important prognostic factor for UC (2-4). The NLR is calculated as the ratio of neutrophil-to-lymphocyte counts in the peripheral blood, and its elevation has been associated with prognosis in various cancers (5-7). Although the underlying mechanism remains unclear, lymphocytes play a crucial role in tumor immunity, with tumor-infiltrating lymphocytes being considered a favorable prognostic factor. In contrast, neutrophils suppress immune cells, including lymphocytes, thus contributing to an immunosuppressive tumor environment (8-10).

Hematuria is a characteristic early symptom of UC, making urine-based screening methods, such as urinalysis, urine sediment examination, and urine cytology, essential for UC detection (11-13). Urine cytology is recommended for screening high-risk UC (14). However, its sensitivity is limited in low-grade UC, owing to minimal nuclear atypia (15). Conventional urine cytology has a low diagnostic sensitivity for low-grade UC, and the Paris System, introduced in 2016, emphasizes screening for high-grade UC (16). Therefore, a novel screening approach that includes low-grade UC is required (Figure 1).

Figure 1.
Figure 1.

Histological and cytological features of urothelial carcinoma. (A, B) In high-grade invasive urothelial carcinoma, the histological architecture is disorganized, with an increased nuclear-to-cytoplasmic (N/C) ratio, marked nuclear pleomorphism, hyperchromasia, and pronounced cellular atypia. (C, D) In low-grade non-invasive urothelial carcinoma, tumor cells exhibit papillary growth around blood vessels, with mild cellular atypia. (A, C: Hematoxylin & Eosin staining, B, D: Papanicolaou staining).

Automated urine sediment analyzers have become increasingly common in UC screening. A fully automated UF-5000 urine particle analyzer (Sysmex Corporation, Kobe, Japan) uses flow cytometry to measure urinary red blood cells, white blood cells, epithelial cells, casts, and bacteria (17). A key feature of the UF-5000 is its ability to quantify atypical cells (Atyp.C), including those associated with UC. Atyp.C measures cell size, internal structural complexity, and nucleic acid content via flow cytometry, enabling the quantification of atypical cells in urine. Given its capabilities, the UF-5000 is expected to play a crucial role in UC screening. However, its diagnostic accuracy remains a challenge (18, 19).

The diagnostic accuracy of Atyp.C in UC screening, using a cutoff value of 0.1/μl, demonstrates high specificity (82.1%) but relatively low sensitivity (59.0%) (18). When limited to recurrent non-muscle-invasive UC screening, sensitivity (83.3%) and positive predictive value (82.7%) are satisfactory at the 0.1/μl cutoff, but specificity (53.7%) and negative predictive value (53.0%) remain low (19). These findings suggest that Atyp.C alone is insufficient for the optimal detection of UC.

In this study, we aimed to enhance UC screening sensitivity by incorporating inflammatory parameters, such as NLR, into UF-5000 Atyp.C data. Using the AI platform DataRobot, we analyzed the UC detection accuracy and evaluated improvements in diagnostic performance.

Patients and Methods

Study design. This retrospective study aimed to evaluate the diagnostic accuracy of the UF-5000 parameters combined with inflammatory markers, including the NLR, for UC detection using an AI-based approach. This study was approved by the Ethics Committee of Kurume University (approval #23167) and conducted in accordance with the guidelines of the Declaration of Helsinki. An opt-out policy was also applied.

This study followed the protocol outlined in Figure 2. A total of 202 patients who underwent urinalysis, urine sediment examination, and urine cytology, followed by histopathological diagnosis, at Kurume University Hospital between 2020 and 2023 were analyzed. The patients were initially classified into two groups: 139 UC cases and 63 non-UC cases; the latter were confirmed as UC-negative by histopathology or cystoscopy. Among the UC cases, 60 were classified as invasive UC and 79 as noninvasive UC. However, 42 patients diagnosed with atypical cells in urine cytology were excluded owing to the potential impact on the UF-5000 analysis. Consequently, the final study population consisted of 57 noninvasive UC, 41 invasive UC, and 62 non-UC cases (Table I).

Figure 2.
Figure 2.

Study design. This figure shows the distribution of cases included in the study. The urothelial carcinoma (UC) group consisted of 60 invasive and 79 noninvasive UC cases, whereas the non-UC group included 63 patients. However, cases diagnosed with atypical cells by urine cytology were excluded: 19 invasive UC, 22 non-invasive UC, and 1 non-UC case.

View this table:
Table I.

Patient characteristics.

Diagnostic accuracy of UF-5000 and examination of a UC detection algorithm using DataRobot. This study used UF-5000 parameters, systemic inflammatory markers, and urine sediment data to examine a UC detection algorithm using the AI-platform DataRobot (version 9.2) (Table II). Among the UF-5000 parameters, Atyp.C quantifies atypical cells based on nucleic acid content, cellular size, and internal complexity and serves as an indicator for UC detection. Because an optimal cut-off value for Atyp.C has not been firmly established, we determined the most appropriate cut-off values for UC, non-invasive UC, and invasive UC using receiver operating characteristic (ROC) curve analysis.

View this table:
Table II.

Evaluation criteria used in DataRobot’s classification algorithms.

The UC detection algorithm was constructed using DataRobot, targeting three groups: UC, noninvasive UC, and invasive UC. The dataset was divided into three subsets: training, validation, and holdout. The distribution was as follows: i) The UC group included 102 cases for training (63.7%), 26 for validation (16.3%), and 32 for holdout (20.0%). ii) The noninvasive UC group included 76 cases for training (63.7%), 19 for validation (16.3%), and 24 for holdout (20.0%). iii) The invasive UC group included 65 cases for training (63.1%), 17 for validation (16.9%), and 21 for holdout (20.0%).

Comparison of detection accuracy and statistical analysis. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Atyp.C alone in detecting UC, non-invasive UC, and invasive UC were calculated. The detection accuracy of the AI-based UC detection algorithm examined using DataRobot was then assessed and compared with the results obtained using Atyp.C alone. Statistical analyses were performed using JMP Pro 16 (JMP Statistical Discovery LLC, Cary, NC, USA).

Results

Detection accuracy of UF-5000 Atyp.C for UC stratified by depth of invasion. The diagnostic accuracy for UC at different invasion depths using the UF-5000 Atyp.C is summarized in Table III. ROC curve analysis determined that the optimal cut-off value for Atyp.C was 0.1/μl for UC, invasive UC, and non-invasive UC. The overall detection accuracy for UC was an area under the curve (AUC) of 0.673, sensitivity of 42.9%, specificity of 90.3%, PPV of 87.5%, and NPV of 50.0% (Figure 3A). For noninvasive UC, the detection accuracy was AUC 0.565, sensitivity of 22.8%, specificity of 90.3%, PPV of 68.4%, and NPV of 56.0%. In contrast, for invasive UC, Atyp.C at 0.1/μl achieved an AUC of 0.824, sensitivity of 70.7%, specificity of 90.3%, PPV of 82.9%, and NPV of 82.3%, demonstrating higher sensitivity, PPV, and NPV (Figure 3B and C).

View this table:
Table III.

Detection accuracy of urothelial carcinoma (UC) using Atyp.C as a single parameter.

Figure 3.
Figure 3.

Receiver operating characteristic (ROC) curve for UF-5000 Atyp.C (0.1/μl). The figure shows ROC curves for UF-5000 Atyp.C at a cut-off value of 0.1/μl. The area under the curve (AUC) represents the total area beneath the ROC curve and serves as an indicator of model classification performance. The AUC values range from 0-1, with higher values indicating superior discriminatory ability. [A: urothelial carcinoma (UC), B: noninvasive UC, C: invasive UC].

Detection accuracy of the DataRobot-based UC detection algorithm incorporating inflammatory markers. The diagnostic performance of the UC detection algorithm examined using DataRobot is summarized in Table IV. For UC detection, the algorithm achieved an AUC of 0.729, sensitivity of 100%, specificity of 33.3%, PPV of 71.4%, and NPV of 100% (Figure 4A). For noninvasive UC, the detection accuracy was AUC 0.750, sensitivity of 83.3%, specificity of 58.3%, PPV of 66.7%, and NPV of 77.8 %, indicating high sensitivity. For invasive UC, the algorithm demonstrated an AUC of 0.892, sensitivity of 75.0%, specificity of 100%, PPV of 100%, and NPV of 86.7%, indicating excellent specificity and PPV (Figure 4B and C).

View this table:
Table IV.

Detection accuracy of urothelial carcinoma (UC) using DataRobot’s anomaly detection algorithm.

Figure 4.
Figure 4.

Receiver operating characteristic curve for DataRobot’s anomaly detection algorithm. In all three categories, the area under the curve (AUC), a measure of the model’s classification performance, with values closer to 1 indicating better accuracy, was higher than that of the UF-5000 Atyp.C alone. [A: urothelial carcinoma (UC), B: noninvasive UC, C: invasive UC].

The most influential factors in UC detection were NLR, urine sediment parameters, and Atyp.C, with similar trends observed for invasive UC. However, in non-invasive UC, the contribution of urine sediment parameters and Atyp.C was lower, whereas inflammatory markers, particularly NLR, had a greater impact on the detection accuracy (Figure 5A-C).

Figure 5.
Figure 5.

Factors contributing to urothelial carcinoma (UC) detection accuracy using DataRobot’s anomaly detection algorithm. In all three groups, (A) UC, (B) noninvasive UC, and (C) invasive UC, the neutrophil-to-lymphocyte ratio (NLR) ranked among the top contributing factors to detection accuracy. Atyp.C and atypical cells in urine sediment, both reflecting cellular atypia, were also major contributors to detection accuracy in (A) UC and (C) invasive UC. In contrast, in (B) non-invasive UC, where cellular atypia is generally low, these parameters were negatively associated with detection accuracy.

Discussion

Previous studies on the UF-5000 and UC have primarily focused on the diagnostic accuracy of Atyp.C, its prognostic implications, and comparisons with urine sediment analysis and cytology (18-22). In this study, the UF-5000 demonstrated a high specificity (90.3%) and PPV (87.5%) when the Atyp.C cut-off was set at 0.1/μl, indicating its robust ability to identify UC. Furthermore, incorporating inflammatory markers, particularly the NLR, significantly enhanced detection sensitivity, addressing a key limitation of the UF-5000 in UC screening.

The UF-5000 is a minimally invasive, urine-based screening tool that makes it a promising candidate for routine UC surveillance. By applying an Atyp.C cut-off of 0.1/μl, UF-5000 achieved high specificity (90.3%) and PPV (87.5%), reinforcing its potential diagnostic utility. However, an important observation in this study was the differential detection accuracy between invasive and noninvasive UC. UC is characterized by nuclear enlargement, irregular nuclear contours, and chromatin hyperdensity (23). Invasive UC, typically high-grade, was effectively detected by the UF-5000, achieving a sensitivity of 70.7%, demonstrating its suitability as a screening tool for invasive UC. In contrast, non-invasive UC, which generally exhibits lower cellular atypia, was more difficult to detect, with a sensitivity of only 22.8%, indicating that the UF-5000 alone was insufficient for detecting low-grade UC. Therefore, additional testing is required to establish a more comprehensive screening approach for UC.

To further improve detection accuracy, the UF-5000 was integrated with an AI-based detection algorithm using DataRobot, incorporating inflammatory parameters, such as NLR. This approach significantly enhanced diagnostic performance, achieving a sensitivity of 100% for UC and an AUC of 89.2% for invasive UC, with a sensitivity of 75.0%, specificity of 100%, PPV of 100%, and NPV of 86.7%. These results highlight the critical role of inflammation in tumor progression (24). High-grade UC, often classified as invasive UC, is strongly associated with an inflammatory response (25). In this study, NLR emerged as one of the most influential factors for UC detection accuracy, further supporting its clinical relevance.

NLR is widely recognized as a prognostic biomarker in pancreatic, colorectal, and small cell lung cancers (5-7). Additionally, the NLR has been identified as a predictor of prognosis, treatment response, and immune-related adverse events associated with immune checkpoint inhibitors in UC (2-4, 26, 27). Given its established association with UC, the integration of the UF-5000 with the NLR represents an effective strategy for enhancing UC detection accuracy.

Study limitations. First, the detection accuracy for noninvasive UC remained suboptimal, even with DataRobot-based analysis. Neither Atyp.C nor the urine sediment parameters significantly contributed to noninvasive UC detection. Low-grade UC exhibits minimal cytological atypia, making it inherently difficult to detect using conventional urine cytology (15). Our findings suggest that a more comprehensive UC screening protocol should integrate additional tumor-related and inflammatory markers. Second, this study was retrospective, and the sample size was relatively small. A multicenter prospective study is needed to validate the clinical utility of the UF-5000-based screening and to confirm the effectiveness of the proposed detection algorithm in a broader patient population.

Conclusion

This study demonstrated that the UF-5000 significantly enhances UC screening, particularly for invasive UC, by achieving high specificity and PPV. Furthermore, incorporating the NLR improved sensitivity, addressing previous limitations in UC detection. These findings suggest that the UF-5000 can serve as the foundation for a novel, minimally invasive UC screening system with enhanced diagnostic accuracy.

Acknowledgements

The Authors would like to thank Editage (www.editage.com) for the English language editing.

Footnotes

  • Authors’ Contributions

    KO, YN, and HK designed the experiments; KO, SI, CN, MI, and RM performed the experiments; KO analyzed the data; YN, AK, and HK helped with the discussion; KO and YN wrote the manuscript; and KO, YN, and HK supervised the project. All the Authors reviewed the manuscript.

  • Conflicts of Interest

    This study was conducted with research funding provided by the Sysmex Corporation. However, this did not influence the research results or interpretations.

  • Artificial Intelligence (AI) Disclosure

    The AI platform DataRobot (version 9.2; DataRobot, Tokyo, Japan, https://www.datarobot.com/) was used to examine a UC detection algorithm utilizing parameters implemented in the UF-5000 analyzer, systemic inflammatory markers, and urinary sediment findings. The manuscript was not written using artificial intelligence tools. However, ChatGPT was utilized for preliminary English language editing under human oversight, and subsequent professional editing was performed by Editage (https://www.editage.jp).

  • Funding

    This work was supported by research funding from Sysmex Corporation.

  • Received May 23, 2025.
  • Revision received June 10, 2025.
  • Accepted June 11, 2025.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Novel UF-5000-based Detection, Integrating Inflammatory Parameters, for Urothelial Carcinoma of the Urinary Bladder
KAZUHIRO OKADA, YOSHIKI NAITO, SAORI IRIE, CHIKA NAKAMOTO, KENJI KUBOYAMA, TOSHIKI TARUMIZU, MISAKI IKEDA, YUKO TAKUMA, RYO MAKINO, AKIHIKO KAWAHARA, HIROYUKI KAWANO
Anticancer Research Aug 2025, 45 (8) 3531-3541; DOI: 10.21873/anticanres.17714
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