Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk

Abstract

Background:

Cystitis glandularis (CG) is a chronic inflammatory condition of the bladder characterized by a high recurrence rate, imposing a substantial burden on patients. The mechanisms underlying recurrence remain unclear.

Objectives:

This study aims to identify markers associated with CG recurrence and develop a predictive model for recurrence risk.

Design:

Retrospective cohort study of patients with confirmed recurrence based on outpatient visits or readmissions were included in this study, which was subsequently divided into training and test set.

Methods:

Patients diagnosed with CG from four hospitals between 2013 and 2023 were retrospectively included and followed for one year. Recurrence was defined as the appearance of new nonneoplastic lesions on cystoscopy after complete resection of the primary disease. A total of 161 patients were divided into a training set (n = 98) from XiangYa Hospital and a test set (n = 63) from Shaoyang Central Hospital, the Second Affiliated Hospital of South China University, and the First People’s Hospital of Changde City. Cox regression analysis was performed in the training set to identify serological indicators associated with recurrence, which were further validated at the histological level by immunohistochemistry. A prognostic model was then constructed using LASSO regression, and its predictive performance was evaluated using receiver operating characteristic (ROC) curves. A nomogram was also developed for clinical application.

Results:

Among 161 patients followed for 12 months, the recurrence rate was 49.6% (n = 80). Univariate and multivariate Cox regression analyses revealed that serological eosinophil and basophil counts were significantly associated with CG recurrence, with histological validation confirming their relevance. The LASSO-based risk model demonstrated good predictive ability, with an area under the ROC curve exceeding 0.75.

Conclusion:

Serological indicators, specifically eosinophil and basophil counts, are closely linked to CG recurrence. A risk score model based on these markers was developed, providing effective prediction of recurrence in clinical practice.

Plain language summary

Using blood tests and tissue markers to predict the recurrence of cystitis glandularis: building and validating a risk prediction model

Background: Cystitis glandularis (CG) is a chronic bladder condition that often comes back after treatment, which can be frustrating and burdensome for patients. However, the reasons why it recurs remain unclear.

Purpose: This study aimed to find simple, accessible markers in the blood and tissue samples that could help predict which patients are more likely to experience CG recurrence.

Methods: We collected data from 161 patients with CG treated at four hospitals between 2013 and 2023. All patients were followed for one year after their initial treatment. We used statistical methods to find links between certain blood test results—especially counts of eosinophils and basophils—and the chances of recurrence. We also confirmed these findings using tissue staining techniques. Based on these markers, we built a prediction model to estimate each patient’s risk of recurrence, and tested how accurate it was using real patient data.

Results: Almost half of the patients (49.6%) had a recurrence within one year. Two blood markers—eosinophil and basophil levels—were strongly associated with this recurrence risk. The model we built using these markers showed good performance in predicting recurrence, with an accuracy score (AUC) above 0.75.

Conclusion: Simple blood tests, combined with tissue markers, can help predict the risk of CG coming back. This model may help doctors monitor patients more closely and guide follow-up strategies after treatment.

Keywords

cystitis glandularis recurrence riskscore

Introduction

Cystitis glandularis (CG) is a relatively rare proliferative bladder disease, with an incidence of about 1%, and the incidence is increasing year by year.¹ It primarily stems from the abnormal proliferation of bladder transitional epithelial cells and basal cells triggered by factors such as bladder stones, infections, and obstructions, culminating in the formation of pathologically specific Brunn’s nests – a proliferative response of the bladder epithelium.² Clinical presentations mainly encompass gross hematuria, frequent and urgent urination, pelvic floor discomfort, and other urinary tract irritation symptoms. However, the nonspecific clinical manifestations often complicate the diagnosis of CG.³ According to relevant research, while currently viewed as a benign bladder lesion, CG may progress to bladder cancer in later stages.⁴ Clinically, the current mainstay of management for CG is transurethral resection of visible lesions and removal of underlying causes such as infection, stones, or obstruction. There is no established role for routine intravesical chemotherapy or standardized adjuvant therapy, and postoperative management mainly involves regular cystoscopic surveillance. However, recurrence rates remain high. If recurrent lesions are not treated in time, the growth of CG tissue in the triangle of the bladder may lead to obstruction of the ureteral orifice, resulting in hydronephrosis, and in severe cases may lead to irreversible renal dysfunction, which causes significant harm to patients.⁵

Despite the growing incidence, there remains a dearth of studies on the pathogenesis of CG recurrence. Ma et al. identified CircTHBS1 as a promoter of CG cell proliferation and migration via the miR-211/CCND2 pathway.⁶ However, previous studies have overlooked the risk factors linked with CG recurrence. Therefore, there is an urgent need to explore these factors. Concurrently, specific serological assays are capable of predicting the prognosis of certain diseases. For instance, the white blood cell count is a crucial prognostic indicator for rectal cancer, as referenced in study.⁷ Preoperative peripheral blood eosinophils can predict the recurrence of chronic subdural hematoma.⁸ However, the serological markers correlated with the recurrence of CG remain to be elucidated.

In this study, we gathered serological data from CG patients to pinpoint potential risk factors associated with disease recurrence. Furthermore, we harnessed paraffin-embedded CG tissue specimens to substantify the prognostic significance of these factors at the histological level. The overarching aim of our study is to develop a comprehensive predictive model that integrates serological markers with histological indices, thereby helping determine the prognostic risk of patients.

Methods

Patients enrolled

A total of 161 patients diagnosed with CG were included in this retrospective analysis. Patients diagnosed with CG through cystoscopy and histopathology, who underwent complete transurethral resection and completed one year of follow-up with complete clinical and serological data, were included. Individuals with concurrent bladder malignancy, other chronic inflammatory bladder disorders, prior intravesical therapy, or incomplete records were excluded. The training set (n = 98) was recruited from Xiangya Hospital, Central South University, spanning from July 2013 to July 2023. Simultaneously, the external test set comprised 63 patients recruited from Shaoyang Central Hospital, the Second Affiliated Hospital of South China University, and the First People’s Hospital of Changde City during the same period. All patients underwent a 1-year follow-up, including urinalysis, urosonography, cystoscopy, and biopsy of bladder lesions. This study was reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.⁹

Ethical approval

This study received ethical approval from the Ethics Review Committee of Xiangya Hospital, Central South University (Changsha, P. R. China), the approval number is 202312246 and all patients signed written informed consent. All methods are carried out in accordance with relevant guidelines and regulations.

Data collection

Demographic data, including age and sex, serological measures, and follow-up details (relapse status and time to relapse), were collected for analysis. Missing serological test results were defined using the mean or median based on the data type. CG recurrence in this study was defined as the identification of a new CG lesion by cystoscopy following the complete resection of the primary CG lesion.

Immunohistochemistry

The paraffin sections of CG, confirmed by a pathologist, underwent dewaxing using turpentine and gradient ethanol. Subsequently, the sections were subjected to antigen retrieval in Ethylenediaminetetraacetic acid (EDTA) antigen repair solution, followed by closure with peroxidase blocking and bovine albumin. The sections were then incubated overnight at 4°C overnight with primary antibodies. On the subsequent day, the secondary antibody was applied, followed by DAB (3,3’-diaminobenzidine) and hematoxylin staining. Finally, neutral resin tablets were used to seal the sections for subsequent analysis. Details of the antibodies utilized in the experiment are provided in Table S1.

Computational pathology analyses

Calculation of immunohistochemistry H-Score involved four steps: (1) utilization of the Vectra 2.0 automatic quantitative pathological imaging system (PerkinElmer, Waltham, MA, USA) to capture images of the Immunohistochemistry (IHC)-stained tissues at a 20× field of view. The average values of three random fields of view were obtained for subsequent statistical analysis. (2) Pathologists employed InForm Cell analysis software (PerkinElmer) to process the images. This involved manually labeling the tissue and defining the training area, followed by training the machine learning algorithm for tissue segmentation. Once trained, this segmentation was applied to all tissue images. (3) Following tissue segmentation, color deconstruction of the images was performed, and the number of cells was estimated using the Counting Objects module of the software. (4) The IHC H-Score was determined using a semi-quantitative approach to grade the expression of four validated antibodies. The H-Score, which ranges from 0 to 300, was calculated by multiplying the percentage of stained cells by the staining intensity (0, 1+, 2+, and 3+). Validation and comparison of the automatic assessment with pathologist counts are depicted in (Figure S1(A)–(D)).

Key marker screening and risk model construction and validation

We incorporated five serological indicators and four histological markers for subsequent analysis. Utilizing the “glmnet” software package, LASSO regression analysis was conducted to determine the distribution characteristics. Employing the 1 standard error (1-SE) criteria, basophil, eosinophil, CD123, 2D7, and CCR3, along with their respective coefficients, were identified for constructing the Riskscore. This Riskscore was subsequently utilized in Kaplan–Meier survival analysis and receiver operating characteristic curve (ROC curve) assessment to evaluate its predictive capacity in patients with CG and patients were stratified into high- and low-risk groups according to the median Risk score.

Nomogram construction

Integrating the Riskscore with clinical parameters, a nomogram was developed using the “rms” package. To enhance usability, the Riskscore was adjusted to a range of 0 to 100 (Figure S1(E) and (F)). Additionally, basic clinical parameters such as age and sex were included in the model.

Statistical analysis

All statistical analyses were conducted using SPSS (version 25.0, IBM, America) and R (Version 4.2.0; http://www.r-project.org, New Zealand). ROC curves were generated using the “pROC” and “TimeRoc” packages, while the DCA Curve was plotted using the “dca.R” package. Variable comparisons between groups were performed using the t-test. Statistical tests were two-tailed, and a p-value less than 0.05 was considered statistically significant.

Results

Identification of serological risk factors for CG recurrence

We recruited a total of 161 patients, with 98 patients from XiangYa Hospital forming the training set. The remaining 63 patients were sourced from three other hospitals, constituting the test set. Among the 161 patients, 80 experienced recurrences. Table 1 shows the parameters of the training set and the test set, and there is no statistical difference, which proves that the two sets are comparable. Figure 1 presents the schematic diagram of this study. To investigate the risk factors for CG recurrence, univariate and multivariate Cox regression analysis was conducted on the training set, which comprised 55 patients with recurrence. Our analysis identified serum eosinophil and basophil counts as independent risk factors associated with CG recurrence, with HR (95% CI) values of 1.81 (1.24–2.65) and 3.47 (2.24–5.36), respectively (Figure 2(a)). Figure 2(b)–(f) shows the distribution of five serological indicators, with eosinophils and basophils being the least abundant. Subsequently, survival analysis was conducted on these five indicators, revealing that neutrophil, lymphocyte, and monocyte counts had no significant influence on CG recurrence (Figure 2(g)–(i)). Notably, the recurrence risk of patients with elevated eosinophil and basophil counts was significantly higher than those with reduced counts, indicating statistical significance. This finding underscores the importance of these indicators in predicting CG recurrence (Figure 2(j) and (k)).

Table 1.

Patient characteristics.

Characteristic	Training set	Testing set	p
Recurrence (%)
Yes	55 (56.1)	25 (39.7)
No	43 (43.9)	38 (60.3)	0.25
Gender (%)
Male	56 (57.1)	34 (52.5)
Female	42 (42.9)	29 (47.5)	0.33
Age	47.4 ± 12.2	45.8 ± 12.1	0.89
Neutrophil (10⁹/L)	3.84 ± 1.62	3.73 ± 1.74	0.57
Lymphocyte (10⁹/L)	1.41 ± 0.46	1.33 ± 0.41	0.21
Eosinophil (10⁹/L)	0.17 (0.0775,0.4)	0.13 (0.1,0.3)	0.73
Basophilic (10⁹/L)	0.02 (0,0.05)	0.02 (0,0.04)	0.93
Monocyte(10⁹/L)	0.4 (0.4,0.5)	0.4 (0.4,0.5)	0.93
CCR3	25 (7,35)	27 (8.38)	0.46
PRG2	17 (6,38)	19 (7,42)	0.73
2D7 antigen	12 (3.75,34)	14 (3.76,33)	0.62
CD123	15.5 (4,31.5)	16 (4,33)	0.96

Figure 1.

Flow chart for patient enrollment.

Figure 2.

Screening of serological indicators associated with CG prognosis. (a) Univariate and multivariate Cox regression analysis identified serological indicators associated with CG recurrence. (b–f) Five serological indicators across the training set and the test set. (g–k) Kaplan–Meier survival curves of five serological indicators in the training set and test set.

Identification of relative inflammatory cells in CG tissue

After identifying serum eosinophil and basophil counts as closely associated with CG recurrence, we aimed to investigate whether eosinophil and basophil counts in CG tissue could serve as prognostic indicators for CG. To achieve this, we utilized markers specific to eosinophils: PRG2 (known as MBP)¹⁰ and CCR3.^11,12 as well as markers specific to basophils: 2D7 antigen^13,14 and CD123,¹⁵ to stain CG tissue.

A cohort of 161 patients from both the training and test sets was chosen for tissue staining, with all CG tissues confirmed by pathologists. Figure 3(a) depicts the expression levels of the four markers in both relapsed and nonrelapsed CG tissues. Subsequently, employing an automated scoring method, we quantified the expression levels of the four markers over all tissues and identified no discernible differences between the training and test sets (Figure 3(b)). The automated counting method utilized in this study demonstrated high consistency with the counting results achieved by two independent pathologists (Supplemental Material, Figure S1(A)–(D)), validating the reliability of this approach. Relevant clinical characteristics of both patient groups are provided in Table 1.

Figure 3.

Verification of serological inflammatory cells across tissue sections. (a) Immunohistochemical staining of four cell markers in CG samples. (b) Expression levels of four markers in the training set and test set. (c-f) Kaplan–Meier survival curves of four markers in the training set and test set.

Using survival analysis, we observed that CG patients with high expression levels of these four markers demonstrated significantly worse prognoses relative to those with low expression levels. This finding validates the significance of the four markers for the prediction of CG recurrence and highlights the essential role of eosinophils and basophils in CG recurrence (Figure 3(c)–(f)).

Development of predictive riskscore for CG recurrence

Further correlation analysis revealed no significant correlations among the serological markers. However, the count of eosinophils showed some associations with PRG2 and CCR3, while the basophil count demonstrated connections with 2D7 antigen and CD123. These correlations highlight their potential impact on CG recurrence (Figure 4(a)).

Figure 4.

Establishment, verification, and application of the Riskscore. (a) LASSO regression analysis identified key prognostic factors and built the Riskscore. (b) Correlation between serological indicators and their markers. (c–d) Kaplan–Meier survival analysis demonstrated that Riskscore high discrimination capability on both the training set and test set. (e–f) ROC curves identify the survival prediction accuracy of the Riskscore in training and test sets. (g) ROC curve analysis of 3, 6, and 12-month survival prediction accuracy for the Riskscore’s entire training set. (h) A nomogram was established by combining the Riskscore and basic clinical parameters. (i) ROC curve analysis of 3, 6, and 12-month survival prediction accuracy for the nomograms’s entire training set.

Following this, we amalgamated five serological markers and four marker scores into the patient’s prognosis. Utilizing LASSO regression, we pinpointed key indicators and formulated a risk prediction model (Figure 4(b)): Riskscore = 0.08517131*Eosinophil + 14.58153629*Basophil + 0.01018139*PRG2 + 0.01555792*2D7 antigen.

To authenticate the predictive efficacy of the Riskscore on CG patients’ prognosis, survival analysis was conducted on both the training set (n = 98) and test set (n = 63) (Figure 4(c) and (d)). In the training set, the high-risk group displayed a significantly higher recurrence probability compared to the low-risk group, with an area under the survival curve (AUC) of 0.8084 (Figure 4(e)). Similarly, the Riskscore demonstrated robust predictive capability in the test set, with an AUC of 0.793 for survival (Figure 4(f)). Additionally, the AUC values for March, June, and December were 0.868, 0.839, and 0.786, respectively, indicating the consistency of its predictive ability (Figure 4(g)). Furthermore, for clinical convenience, we merged clinical characteristics with the adjusted Riskscore formula to devise a nomogram, offering a more user-friendly approach for prognosticating CG patients’ outcomes with good prediction effect. (Figure 4(h) and (i) and Figure S1(E) and (F)). Moreover, the utilization of the Riskscore and the nomogram exhibited a greater net clinical benefit in CG patients compared to gender and age indicators (Figure S1(G)).

Discussion

CG, a chronic inflammatory condition of the urinary system, manifests through symptoms like frequent urination, urgency, pain, hematuria, and difficulty in urination.^16–18 Given its propensity for recurrence, CG imposes a substantial burden on patients, including diminished quality of life, psychological distress, and economic strain. In our study, approximately half of the patients experienced a recurrence within a year. To alleviate this burden, we aim to identify markers associated with disease recurrence for early detection and intervention.

The intricate pathogenesis of CG poses a challenge in identifying prognostic markers. Previous studies have suggested that serological indicators could serve as prognostic markers for the disease.^19–21 Thus, we investigated serological indicators in CG patients and discovered significant correlations between eosinophils and basophils with prognosis. These findings were further validated at the histological level. Subsequently, employing a machine learning algorithm, we identified four key indicators to construct the Riskscore, including eosinophil count, basophil count, eosinophil marker PRG2, and basophil marker 2D7. Our Riskscore demonstrated robust predictive capability upon validation. Additionally, we developed a nomogram to facilitate the practical application of the Riskscore. In clinical practice, patients with a Risk score above the median may be classified as high-risk, and could be recommended for more frequent cystoscopic surveillance, whereas patients below the median may follow standard intervals. The nomogram may provide clinical utility by stratifying patients into high- and low-risk groups. High-risk patients could be considered for shorter surveillance intervals or more intensive monitoring, whereas low-risk patients may require less-frequent cystoscopy, thereby optimizing resource allocation and reducing patient burden. This stratification may also guide individualized re-treatment decisions in recurrent cases, helping clinicians design more tailored management plans.

Despite the lack of specific studies on the role of these cell types in CG, our research revealed a negative correlation between serum eosinophil and basophil counts and CG prognosis. These findings were consistent at the histological level, indirectly suggesting their potential role in promoting recurrence in CG patients. Eosinophil and lymphocyte counts are common serological indicators of inflammation and are closely associated with pathophysiological processes such as infection and allergy.^22–25 Eosinophils have been implicated in the prognosis of various conditions, including COVID-19 and different cancers.^26,27 Moreover, the proliferation of basal plasma cells linked to eosinophilia serves as an indicator for the early detection of inflammatory bowel disease and exhibits a strong correlation with histological diagnosis.²⁸ Significant eosinophilic infiltration in the lamina propria observed in colon biopsy samples from patients with ulcerative colitis stands out as a primary predictor of poor response to pharmaceutical interventions.²⁹ In the presence of inflammation, basophils undergo multiplication.³⁰ Concurrently, the IL-4 they produce plays a pivotal role in regulating various immune cells, such as T cells, B cells, and macrophages, thereby modulating tissue inflammation.³¹ Additionally, the alkaline granulocyte population has the capability to express MHCII and co-stimulatory molecules. This population migrates to draining lymph nodes, presenting antigens to naive CD4+ T cells, facilitating Th2 cell differentiation, and subsequently modulating immune responses within the body.³² While eosinophil and basophil counts showed promise as biomarkers, they are not intended to replace cystoscopy. Instead, patients with persistently elevated counts may benefit from closer surveillance or earlier cystoscopic evaluation. However, the optimal frequency of blood testing requires further prospective studies.

Limitations

Despite its significant findings, our study has specific limitations that warrant consideration. Recurrence was defined histologically by cystoscopy, and minor variations in the timing of follow-up cystoscopies may have introduced detection bias. Although all patients underwent baseline and follow-up cystoscopic examinations, differences in surveillance intervals could have influenced recurrence detection. The retrospective approach may introduce selection bias and could impact the generalizability of the proposed model. Another important consideration is that eosinophil and basophil counts can be influenced by confounders such as allergic conditions, parasitic infections, or medication uses. These factors may reduce external validity and should be carefully controlled in future studies. Moreover, the specific mechanism underlying the impact of eosinophils and basophils on CG recurrence remains unclear. This is a critical portion of our future investigation. By addressing these limitations and delving deeper into the underlying mechanisms, we aim to improve the clinical applicability and comprehension of our findings. Another limitation of our study is that the external validation cohort was restricted to hospitals within the same geographic and healthcare region. This may limit the generalizability of our findings to broader populations. Therefore, further prospective studies with multicenter cohorts from different geographic regions are necessary to validate and expand the applicability of our prediction model.

Conclusion

In our study, we found that both eosinophils and basophils can forecast CG recurrence at both serological and histological levels, and constructed a risk score model for predicting CG recurrence. By evaluating the AUC and decision curve, our Riskscore and Riskscore-based nomogram show good predictive ability.

Supplemental Material

sj-docx-1-tau-10.1177_17562872261419577 – Supplemental material for Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk

Supplemental material, sj-docx-1-tau-10.1177_17562872261419577 for Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk by Yuhang Wang, Xuhao Liu, Tailai Zhou, Chuyang Huang, Yong Li, Yuzhong Yan and Minfeng Chen in Therapeutic Advances in Urology

Supplemental Material

sj-pdf-2-tau-10.1177_17562872261419577 – Supplemental material for Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk

Supplemental material, sj-pdf-2-tau-10.1177_17562872261419577 for Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk by Yuhang Wang, Xuhao Liu, Tailai Zhou, Chuyang Huang, Yong Li, Yuzhong Yan and Minfeng Chen in Therapeutic Advances in Urology

Supplemental Material

sj-pdf-3-tau-10.1177_17562872261419577 – Supplemental material for Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk

Supplemental material, sj-pdf-3-tau-10.1177_17562872261419577 for Construction and validation of a prediction model for cystitis glandularis using serological markers combined with histological indexes to predict recurrence risk by Yuhang Wang, Xuhao Liu, Tailai Zhou, Chuyang Huang, Yong Li, Yuzhong Yan and Minfeng Chen in Therapeutic Advances in Urology

Footnotes

Acknowledgements

We would like to thank members of our team for their suggestions in processing the project and helpful feedback in drafting previous versions of this manuscript.

Declarations

ORCID iD

Minfeng Chen

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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