Prognostic value of prostate volume and nomograms for predicting recurrence in patients with non-muscle invasive bladder cancer: a multi-institutional study.

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Dichao Hu, He Liu, Mingyang Li, Wenbo Wu, Chenxu Ma, Lujie Chen, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4728588/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2025 Read the published version in BMC Cancer → Version 1 posted 4 You are reading this latest preprint version Abstract Purpose We conducted an assessment to investigate the impact of prostate volume on the recurrence of patients with non-muscle invasive bladder cancer (NMIBC). Subsequently, we developed and validated nomograms to accurately evaluate recurrence in NMIBC patients. Additionally, we examined the potential improvement in predictive capability achieved by introducing prostate volume as a variable in the model. Methods We conducted a retrospective analysis, enrolling 555 eligible patients from seven independent medical institutions across China. We first evaluate recurrence-free survival outcomes in patients with varying prostate volumes. Subsequently, we divided patients into a training cohort and an external validation cohort. Univariate and multivariate Cox regression analyses were conducted within the training cohort. Accordingly, two nomogram models with and without prostate volumes were developed. Their performance was compared by concordance index, calibration curves, receiver operating characteristics curves, and decision curve analysis. Furthermore, a risk classification model utilizing the nomogram incorporating prostate volume was developed. Results The 3-year recurrence-free survival was markedly lower in patients with large prostate volumes (> 30 ml) compared to those with relatively small prostate volumes (< 30 ml) (p < 0.001). The AUC for the model incorporating prostate volume at 3 years in the training cohort and external validation cohort was 0.803 and 0.776, surpassing the AUC for the model excluding prostate volume at the corresponding intervals, which was 0.787 and 0.767. The 1- and 2-year AUC for the two models also exhibited similar differences. The decision curve analysis results demonstrated the significant superiority of the nomogram incorporating prostate volume over the one without it. Conclusion Our investigation revealed that prostate volume significantly influences recurrence in patients with NMIBC. We successfully developed a more accurate nomogram by introducing prostate volume as a variable and provided new insights to further guide clinical management and individualized treatment of NMIBC patients. Prostate volume Non-muscle invasive bladder cancer Nomogram Recurrence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction According to the latest statistics from Global Cancer, bladder cancer (BC) is the tenth most common cancer in the world, with about 573,000 new cases and 213,000 deaths in 2020[ 1 ]. Around 75% of newly diagnosed urothelial carcinomas of the bladder are non-muscle-invasive, which is usually treated with transurethral resection of bladder tumors (TURBT) and bladder instillation of chemotherapeutic agents or bacillus Calmette-Guérin[ 2 ]. However, it exhibits a high rate of recurrence and progression despite localized treatment. Within five years of surgery, approximately 15–61% of cases recur as non-muscle invasive bladder cancer (NMIBC), while 1–45% progress to muscle-invasive bladder cancer (MIBC)[ 3 ]. Consequently, regular monitoring becomes indispensable for most NMIBC patients following TURBT[ 4 ]. Benign prostatic hyperplasia (BPH) is a common urological disorder in male, the prevalence of which increases with age. It not only causes lower urinary tract symptoms (LUTS)[ 5 ] but also exhibits an association with the development of BC. Recent study have demonstrated that BPH patients have been shown to face an elevated risk of subsequent BC[ 6 ]. Additionally, a recent meta-analysis has underscored that BPH is related to an augmented risk of BC[ 7 ]. In a Mendelian randomization investigation within a European cohort[ 8 ], gene-predicted BPH demonstrated a significant correlation with an increased risk of BC across diverse histological subtypes. However, the evidence supporting the relationship between genetically induced BC and BPH appears inconclusive. It has been demonstrated that in individuals with BPH, androgen deprivation therapy or androgen-suppression therapy involving the 5α-reductase inhibitor dutasteride is significantly linked to a reduced risk of intravesical recurrence[ 9 ]. Additionally, following the drug castration, there is a decrease in prostate volume (PV), accompanied by the relieved symptoms of LUTS, leading to therapeutic remission of BPH[ 10 ]. In this study, we gathered data of 1024 patients from seven independent institutions in China, and 555 eligible patients were enrolled in our study. The training cohort comprised patients from four independent institutions, while the external validation cohort consisted of patients from another three independent institutions. We first assessed the impact of PV on recurrence after the first TURBT in NMIBC patients. Then we develop nomograms to predict the recurrence of NMIBC patients based on the clinicopathological information and further explore the efficacy enhancement of introducing PV information. Ultimately, we intend to create a risk classification model to aid in clinical decision-making. Materials and methods Data Acquisition and Processing This retrospective study was approved by the Institutional Ethics Review Board. The requirement for informed content was waived. The data for this retrospective study were collected from seven independent medical institutions in China between 2013 and 2021, including Shanghai General Hospital, Weifang Hospital of Traditional Chinese Medicine, Linyi People’s Hospital, Weifang People’s Hospital, Mianyang Central Hospital, Ningde City Hospital, and Suzhou Kowloon Hospital. Inclusion criteria were as follows: (i) Male patients with NMIBC and diagnosed with bladder urothelial carcinoma after TURBT; (ii) availability of three or more years of follow-up information; (iii) normal function of major internal organs (heart, liver, lungs, kidneys) and no presence of other malignant tumors; (iv) no lymph node metastasis or distant metastasis; (v) no history of bladder surgery or excision. A study flow chart is shown in Fig. 1 . We used patients from Shanghai General Hospital, Weifang People’s Hospital, People’s Hospital, and Weifang Hospital of Traditional Chinese Medicine as the training cohort (n = 373), and the patients of the Mianyang Central Hospital, Ningde City Hospital, and Suzhou Kowloon Hospital as the external validation cohort (n = 182). Follow-up protocol Low-risk patients undergo cystoscopy within 3 months post-surgery. After a negative first cystoscopy, a second is conducted at 1 year after surgery, followed by annual checks until the 5th year. High-risk patients undergo urine cytology and cystoscopy every three months for the initial two years, transitioning to bi-annual checks in the third year, and then annually from the fifth year onward until the end of life. Additionally, high-risk patients undergo an annual upper urinary tract examination (CTU examination). The follow-up program for intermediate-risk patients is in between, contingent on individual recurrence factors and general conditions. Successful follow-up is defined as either surviving until at least one visit during the entire follow-up period or experiencing mortality. If tumor recurrence is suspected during cystoscopy, confirmation is achieved through histopathological examination. Variable definitions and the construction of nomograms The study encompassed various variables, including age at diagnosis, tumor number, tumor size, prostate volume, pathological grade, and clinical stage. Patients were divided into two groups by age at diagnosis of 65 years. PV data was obtained from the patient’s ultrasound and MRI reports, computed using the ellipsoid formula: 0.52 × [width (cm)] × [length (cm)] × [height (cm)] and the results were then averaged across both reports. Subsequently, the patients were divided into two groups with a PV of 30 ml as the threshold. Tumor size was obtained from the MRI reports of the patients and classified according to the diameter of 3 cm, then the patients were divided into large and small tumor groups. The histological grading classification was performed following the World Health Organization 2004/2016 system, while the clinical stage was performed according to the 2017 TNM classification by the American Joint Committee on Cancer. In this study, two nomograms were developed utilizing data from the training cohort. We built nomogram 1 using all the independent risk variables associated with RFS determined by the Cox regression model, while nomogram 2 contained all the other independent risk variables except PV. Prognostic value of prostate volume To assess the impact of PV on the recurrence of NMIBC patients, a chi-square test was employed to analyze the relationship between PV and the recurrence of NMIBC patients. Additionally, survival analysis was conducted using the Kaplan-Meier survival analysis, involving the plotting of survival curves. The log-rank test was then applied to compare the RFS of patients in the two PV groups to determine whether a statistically significant association existed. Statistical analysis Statistical analysis was conducted using SPSS software (version 25.0) and R statistical software (version 4.3.1). P < 0.05 was taken as a statistically significant difference. Differences between groups were assessed using the chi-square test. Survival analysis employed the Kaplan-Meier survival analysis. Univariable and multivariate Cox proportional hazards models estimated the hazard ratio (HR) and corresponding 95% confidence interval (95% CI) for all covariates. Covariates that exhibited statistically significant in the univariable analysis were included in a multivariable model for the development of nomograms. The calibration of the nomograms was assessed with a calibration curve. The discrimination of the nomograms was evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curve, and the area under the curve (AUC). DCA was employed to evaluate the clinical utility of the model. We calculated a risk score for each patient based on the established nomogram including PV information. The cutoff score is determined by using the “surv_cutpoint()” function within the “survminer” package in the R language to divide patients into high or low-risk groups. Subsequently, Kaplan-Meier survival curves were plotted for patients in two risk groups with differences scrutinized using Log-rank tests. Results Patient characteristics Based on the screening criteria, we enrolled a total of 555 eligible patients. Table 1 provides an overview of the demographic and clinicopathologic characteristics of all cohorts, distinguishing between the training cohort (n = 373) and the external validation cohort (n = 182). Patient characteristics included age ( 30 ml), and tumor size (< 3 cm, ≥ 3 cm). The difference in each variable between the two cohorts was not statistically significant. Table 1 Demographic and clinicopathologic characteristics of the training and validation cohorts. Training cohort External validation cohort Total P value (n = 373) (n = 182) (n = 555) Grade 0.317 High grade 141(37.8%) 64(35.1%) 205(36.9%) Low grade 195(52.3%) 94(51.6%) 289(52.1%) PUNLMP 37(9.9%) 24(13.2%) 61(11.0%) Age (year) 0.212 <65 188(50.4%) 102(56.0%) 290(52.3%) ≥ 65 185(49.6%) 80(44.0%) 265(47.7%) Stage 0.162 Ta 219(58.7%) 118(64.8%) 337(60.7%) T1 154(41.3%) 64(35.2%) 218(39.3%) Prostate volume 0.184 ≤ 30 ml 311(83.4%) 143(78.6%) 454(81.8%) >30 ml 62(16.6%) 39(21.4%) 101(18.2%) Tumor size 0.914 <3 cm 264(70.8%) 128(70.3%) 392(70.6%) ≥ 3 cm 109(29.2%) 54(29.7%) 163(29.4%) Tumor number 0.125 Single 236(63.3%) 127(69.8%) 363(65.4%) Multiple 137(36.7%) 55(30.2%) 192(34.6%) Abbreviations: PUNLMP, papillary urothelial neoplasms of low malignant potential Identification of prognostic predictors We did univariate Cox regression analyses for six variables for patients in the training cohort (Table 2 ), which showed that clinical T-stage (T1: HR = 2.78, 95%CI: 1.85–4.16, p < 0.001), prostate volume (HR = 2.72, 95%CI: 1.78–4.16, p < 0.001), tumor number (HR = 3.61, 95%CI: 2.40–5.42, p < 0.001), tumor size (HR = 2.20, 95%CI: 1.48–3.26, p < 0.001), pathologic grade (HR = 3.03, 95%CI: 2.10–4.38, p < 0.001), and age (HR = 1.52, 95%CI: 1.02–2.26, p < 0.05) were associated with recurrence. Table 2 Univariate and multivariate Cox regression analyses of factors associated with RFS in the training cohort. Univariate analysis Multivariate analysis Characteristic HR(95%CI) P value HR(95%CI) P value Age <65 years ≥ 65 years 1.52 (1.02–2.26) 0.041 1.10 (0.73–1.66) 0.660 Tumor number Single Multiple 3.61 (2.40–5.42) < 0.001 3.08 (2.02–4.68) < 0.001 Grade PUNLMP Low grade High grade 3.03 (2.10–4.38) < 0.001 2.30 (1.49–3.54) < 0.001 Tumor size <3 cm ≥ 3 cm 2.20 (1.48–3.26) 30 ml 2.72 (1.78–4.16) < 0.001 3.03 (1.96–4.68) < 0.001 Stage Ta T1 2.78 (1.85–4.16) < 0.001 1.68 (1.05–2.68) 0.030 Abbreviations: HR, hazard ratio; CI, confidence interval; PUNLMP, papillary urothelial neoplasms of low malignant potential; RFS, recurrence-free survival In addition, we conducted a multivariate analysis to further identify potential risk factors for recurrence in NMIBC patients. Multifactorial Cox regression analysis revealed independent risk factors including pathological grade (p < 0.001), tumor size (p < 0.05), T-stage (p < 0.05), prostate volume (p < 0.001), and tumor number (p < 0.001). Correlation between prostate volume and recurrence in NMIBC patients Our dataset comprised 555 patients divided into either a recurrence-free or a recurrence group. Among those in the non-recurrence group, 12.4% (50/403) of patients exhibited PV exceeding 30 ml, contrasting with 33.6% (51/152) of patients in the recurrence group (Fig. 2 A; p < 0.001). To assess the impact of PV on NMIBC recurrence, we employed the Kaplan-Meier survival analyses. The mean RFS for patients with a large PV was 23.129 ± 1.478 months, significantly inferior to that of patients with a small PV (30.912 ± 0.580 months, P < 0.001; Fig. 2 B). This suggests that PV constitutes a recurrence factor affecting male NMIBC patients. Multifactorial Cox regression analysis demonstrated that PV remained an independent risk factor for RFS after adjustment for clinicopathologic variables (HR = 3.03; 95%CI, 1.96–4.68; P < 0.001; Table 2 ). Development and validation of nomograms We further constructed two nomograms based on the results of the univariate and multivariate Cox regression analyses (Table 2 ). Nomogram 1 includes six prognostic factors (age at diagnosis, number of tumors, prostate volume, tumor size, pathological grade, and clinical stage), and although age at diagnosis was not statistically significant in the training cohort, it was included in the model because of its clinical significance. In comparison to nomogram 1, Nomogram 2 only includes five variables, excluding prostate volume. Nomogram 2 serves as a control to investigate the impact of prostate volume on the model. In nomogram 1, given the values of the 6 prognostic factors for patients, we can graphically calculate the predicted probabilities of recurrence-free within 1, 2, and 3 years after surgery (Fig. 3 A). Similarly, using the values of the 5 prognostic factors in nomogram 2, we can estimate the probabilities of recurrence-free at the 3 time points mentioned above (Fig. 3 B). The nomogram 1 indicated the most significant contribution from pathological grade, followed by tumor number, prostate volume, clinical stage, tumor size, and age at diagnosis. Validation and comparison of two nomograms In the training cohort, the C-index of the model incorporating PV was 0.781 (95%CI 0.740–0.820), surpassing the C-index of the model excluding PV, which was 0.766 (95%CI 0.724–0.808) (p < 0.001; Table S1 ). Calibration curves demonstrated excellent performance across all cohorts, as evidenced by the overlap of 1-, 2-, and 3-year calibration curves of the training and external validation cohorts with standard curves (diagonal lines with a slope of 1) (Fig. 4 ; Figure S1 ). To further compare the two nomograms, ROC curves and DCA curves were plotted. As can be seen from Fig. 5 , Model 1 exhibited a higher AUC in both the training and external validation cohorts, indicating superior accuracy compared to Model 2. Moreover, the DCA curves for Model 1 showed greater net benefit over a larger range of threshold probabilities than Model 2, suggesting enhanced clinical utility of this nomogram (Fig. 6 ). This underscores that the inclusion of the PV variable in the nomogram significantly improves the accuracy and clinical applicability of the model. Risk classification model based on nomogram The cutoff value for the risk score is set at 194.306. Subsequently, patients in the training cohort with risk scores ≤ 194.306 are classified into the low-risk group (n = 308), while those with risk scores > 194.306 are classified into the high-risk group (n = 65). The performance of our risk classification model was then assessed by plotting the Kaplan-Meier survival curves for the training cohort, the external validation cohort, and the combined cohort, respectively (Fig. 7 ). In brief, the 3-year recurrence rates for the low-risk group were 16% in the training cohort, 22% in the external validation cohort, and 18% in the combined cohort. In contrast, the 3-year recurrence rate was 62%, 56%, and 60% in the high-risk groups in the three aforementioned cohorts. What’s more, the 1-year, and 2-year recurrence rates showed similar results. The significant RFS differences between the high-risk group and the low-risk group demonstrate the precise accuracy of our model. Discussion There are numerous factors influencing the prognosis of NMIBC patients, such as advanced age, female gender, tumor size, multifocality, clinical staging, and tumor grading. Our primary focus is to explore the impact of PV on the recurrence of NMIBC patients. LUTS are common in adult men[ 11 ], LUTSs caused by BPH promote the accumulation and concentration of carcinogens involving chemical substances such as polycyclic aromatic hydrocarbons and 2,4-diamino toluene in the bladder, extending the contact time with the bladder mucosa, which results in the induction of BC[ 12 ]. The obstruction of urine flow, accompanied by increased residual urine, also raises the likelihood of urinary tract infections. Inflammation can stimulate the proliferation or metaplasia of transitional epithelial cells in the bladder. The bladder epithelial cells undergoing proliferation or metaplasia, when exposed to carcinogenic substances in concentrated urine, gradually develop tumors[ 13 ]. Our study utilized multi-institutional data to validate the correlation between PV and recurrence in NMIBC patients. Our study findings indicate an increased susceptibility to NMIBC recurrence in patients with PV exceeding 30 ml. What’s more, Kaplan-Meier survival curves demonstrate a notably diminished RFS for patients with larger PV. Multifactorial Cox regression analysis reveals a negative correlation between increasing PV and decreasing RFS. However, the specific mechanism remains unclear. We hypothesize that patients with BPH exhibit larger PV, and larger PV could be associated with LUTS, potentially leading to the accumulation and concentration of carcinogens in urine. Consequently, this process may increase the risk of NMIBC recurrence. Further studies are needed to elucidate the precise underlying mechanism. In recent years, several nomograms have been proposed to assist doctors in making treatment decisions for NMIBC patients[ 14 ], a review by Kluth et al. [ 15 ]identified five NMIBC nomograms. Unfortunately, only one of these nomograms underwent external validation and is applicable to the Japanese population[ 16 ]. In comparison to the aforementioned nomograms, our models are based on a Chinese cohort from multiple medical institutions, encompassing a larger patient sample of 555 cases. We introduced two nomograms and identified that PV plays a significant role in predicting NMIBC recurrence after comparing their performances. Additionally, we implemented an external validation cohort to validate the utility of the models. Moreover, establishing a predictive model for the recurrence of NMIBC patients, incorporating factors such as PV, will aid in counseling patients and making treatment decisions. To make the model more clinically applicable, we introduced a risk classification model. Calculating individual risk scores using the established nomogram, we determined a cutoff value and divided patients into low-risk and high-risk groups. Kaplan-Meier curves exhibit clear differentiation between the two groups. In clinical practice, grouping by risk classification is preferable to simply calculating risk probabilities. This approach provides valuable information for tailoring subsequent treatment regimens to individual patients. However, there are some limitations in our study. Although we established the relationship between PV and NMIBC patient recurrence, the underlying mechanisms remain unclear. Another notable constraint is the potential sensitivity of our results to selection bias, given the essentially retrospective and non-randomized nature of this study. Additionally, the follow-up time for the included patients in this study may also be relatively long. Nonetheless, our research indicates that PV is an independent recurrence factor for NMIBC. Elevated PV correlates with poorer RFS, and the reduction of PV may play a crucial role in preventing the recurrence of NMIBC. Conclusion We included a large multi-institutional cohort of the Chinese population to discover the prognostic value of PV on NMIBC recurrence. We further established as well as validated a more accurate nomogram for predicting recurrence probability in patients with NMIBC by including PV. Abbreviations BC bladder cancer TURBT transurethral resection of bladder tumors NMIBC non-muscle invasive bladder cancer MIBC muscle-invasive bladder cancer BPH benign prostatic hyperplasia LUTS lower urinary tract symptoms PV prostate volume HR hazard ratios CI confidence intervals C-index concordance index ROC receiver operator curve AUC area under the curve DCA decision curve analysis Declarations Data availability statement The datasets analyzed during the current study are available from the corresponding author upon reasonable request. Author contributions LHT conceived and designed the study. HDC, LMY was involved in writing the initial draft of the manuscript. HDC, LP, LYQ, LMY, LH, WWB, MCX, and CLJ were involved in data collection. LHT, WYC, QL, and SZQ were involved in manuscript drafting and revision. Funding This work was supported by project from National Nature Science Foundation of China (81972371); Shanghai Songjiang District Medical and Health Science and Technology Research Project (21SJKJGG97). Ethics declarations Ethics approval and consent for publication The Shanghai General Hospital Institutional Review Board approved this retrospective study. The requirement for informed content was waived. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. https://doi.org/10.3322/caac.21492 . 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Androgen deprivation therapy for volume reduction, lower urinary tract symptom relief and quality of life improvement in patients with prostate cancer: degarelix vs goserelin plus bicalutamide. BJU Int. 2012;110:1721–8. https://doi.org/10.1111/j.1464-410X.2012.11107.x . Gravas S, Gacci M, Gratzke C, et al. Summary Paper on the 2023 European Association of Urology Guidelines on the Management of Non-neurogenic Male Lower Urinary Tract Symptoms. Eur Urol. 2023;84:207–22. https://doi.org/10.1016/j.eururo.2023.04.008 . Hecht SS. Human urinary carcinogen metabolites: biomarkers for investigating tobacco and cancer. Carcinogenesis. 2002;23:907–22. https://doi.org/10.1093/carcin/23.6.907 . Nesi G, Nobili S, Cai T, et al. Chronic inflammation in urothelial bladder cancer. Virchows Arch. 2015;467:623–33. https://doi.org/10.1007/s00428-015-1820-x . Wang D, Ning H, Wu H, et al. Construction and evaluation of a novel prognostic risk model of aging-related genes in bladder cancer. Curr Urol. 2023;17:236–45. https://doi.org/10.1097/CU9.0000000000000218 . Kluth LA, Black PC, Bochner BH, et al. Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature. Eur Urol. 2015;68:238–53. https://doi.org/10.1016/j.eururo.2015.01.032 . Yamada T, Tsuchiya K, Kato S, et al. A pretreatment nomogram predicting recurrence- and progression-free survival for nonmuscle invasive bladder cancer in Japanese patients. Int J Clin Oncol. 2010;15:271–9. https://doi.org/10.1007/s10147-010-0049-6 . Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 15 Jul, 2024 First submitted to journal 12 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4728588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328147415,"identity":"65622905-aef5-44fc-9184-576d0bd011cd","order_by":0,"name":"Dichao Hu","email":"","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dichao","middleName":"","lastName":"Hu","suffix":""},{"id":328147417,"identity":"990dd0f4-7db3-4c21-a436-cb76f3fd598a","order_by":1,"name":"He Liu","email":"","orcid":"","institution":"Shanghai First People's 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Qiao","suffix":""},{"id":328147431,"identity":"836b0223-28dc-4507-b381-5870980fa493","order_by":11,"name":"Yongchuan Wang","email":"","orcid":"","institution":"Weifang Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongchuan","middleName":"","lastName":"Wang","suffix":""},{"id":328147432,"identity":"0a7b9832-6eae-4b36-be8f-155005f3a1eb","order_by":12,"name":"HaiTao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDCCA2DSIoGBvfngg4SKGqK0MDYwMEgk8PAcSzZ4cOYYKVokfMwkH7YwE9bBd7z5+YOPeyTy7CV4zCoSG9gY+Nu7E/BqkTxzzLBxxjOJYh7ptrIbiTtkGCTOnN2AV4vBjRzGZp4DEok9Moe33Ug8w8ZgIJFLrBaJBLOCxDZmkrSkmDEQpQXkl5kzQFrOHEuWSDhzjIegX4Ah9uDDhwM2ie3tzQc//qiokeNv78WvBQPwkKZ8FIyCUTAKRgFWAAD+7k/DqqestQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai First People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"HaiTao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-12 07:15:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4728588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4728588/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-15028-5","type":"published","date":"2025-11-05T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62222566,"identity":"6c97f55d-f122-4b6c-9dae-71e679149db5","added_by":"auto","created_at":"2024-08-11 12:38:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343101,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure 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legend.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4728588/v1/5f65368bb23db2c84d35163d.png"},{"id":95564152,"identity":"beb71b2f-770d-488d-ac75-28ee4134c2a6","added_by":"auto","created_at":"2025-11-10 16:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3436437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4728588/v1/5273b5fc-d56e-4772-a522-f0ace8331032.pdf"},{"id":62222563,"identity":"ab31fe04-e51d-4940-a41c-0c6da67522bc","added_by":"auto","created_at":"2024-08-11 12:38:33","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":2153624,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4728588/v1/899f48dfca17d65d99a55d26.tif"},{"id":62222564,"identity":"55613a17-60dd-48b0-9390-cb74e2f1f53c","added_by":"auto","created_at":"2024-08-11 12:38:33","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":16872,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4728588/v1/b782a4586486ef1f9da0b5b3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of prostate volume and nomograms for predicting recurrence in patients with non-muscle invasive bladder cancer: a multi-institutional study.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the latest statistics from Global Cancer, bladder cancer (BC) is the tenth most common cancer in the world, with about 573,000 new cases and 213,000 deaths in 2020[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Around 75% of newly diagnosed urothelial carcinomas of the bladder are non-muscle-invasive, which is usually treated with transurethral resection of bladder tumors (TURBT) and bladder instillation of chemotherapeutic agents or bacillus Calmette-Gu\u0026eacute;rin[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, it exhibits a high rate of recurrence and progression despite localized treatment. Within five years of surgery, approximately 15\u0026ndash;61% of cases recur as non-muscle invasive bladder cancer (NMIBC), while 1\u0026ndash;45% progress to muscle-invasive bladder cancer (MIBC)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, regular monitoring becomes indispensable for most NMIBC patients following TURBT[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBenign prostatic hyperplasia (BPH) is a common urological disorder in male, the prevalence of which increases with age. It not only causes lower urinary tract symptoms (LUTS)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] but also exhibits an association with the development of BC. Recent study have demonstrated that BPH patients have been shown to face an elevated risk of subsequent BC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, a recent meta-analysis has underscored that BPH is related to an augmented risk of BC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a Mendelian randomization investigation within a European cohort[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], gene-predicted BPH demonstrated a significant correlation with an increased risk of BC across diverse histological subtypes. However, the evidence supporting the relationship between genetically induced BC and BPH appears inconclusive.\u003c/p\u003e \u003cp\u003eIt has been demonstrated that in individuals with BPH, androgen deprivation therapy or androgen-suppression therapy involving the 5α-reductase inhibitor dutasteride is significantly linked to a reduced risk of intravesical recurrence[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, following the drug castration, there is a decrease in prostate volume (PV), accompanied by the relieved symptoms of LUTS, leading to therapeutic remission of BPH[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we gathered data of 1024 patients from seven independent institutions in China, and 555 eligible patients were enrolled in our study. The training cohort comprised patients from four independent institutions, while the external validation cohort consisted of patients from another three independent institutions. We first assessed the impact of PV on recurrence after the first TURBT in NMIBC patients. Then we develop nomograms to predict the recurrence of NMIBC patients based on the clinicopathological information and further explore the efficacy enhancement of introducing PV information. Ultimately, we intend to create a risk classification model to aid in clinical decision-making.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and Processing\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the Institutional Ethics Review Board. The requirement for informed content was waived. The data for this retrospective study were collected from seven independent medical institutions in China between 2013 and 2021, including Shanghai General Hospital, Weifang Hospital of Traditional Chinese Medicine, Linyi People\u0026rsquo;s Hospital, Weifang People\u0026rsquo;s Hospital, Mianyang Central Hospital, Ningde City Hospital, and Suzhou Kowloon Hospital.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: (i) Male patients with NMIBC and diagnosed with bladder urothelial carcinoma after TURBT; (ii) availability of three or more years of follow-up information; (iii) normal function of major internal organs (heart, liver, lungs, kidneys) and no presence of other malignant tumors; (iv) no lymph node metastasis or distant metastasis; (v) no history of bladder surgery or excision. A study flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe used patients from Shanghai General Hospital, Weifang People\u0026rsquo;s Hospital, People\u0026rsquo;s Hospital, and Weifang Hospital of Traditional Chinese Medicine as the training cohort (n\u0026thinsp;=\u0026thinsp;373), and the patients of the Mianyang Central Hospital, Ningde City Hospital, and Suzhou Kowloon Hospital as the external validation cohort (n\u0026thinsp;=\u0026thinsp;182).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up protocol\u003c/h2\u003e \u003cp\u003eLow-risk patients undergo cystoscopy within 3 months post-surgery. After a negative first cystoscopy, a second is conducted at 1 year after surgery, followed by annual checks until the 5th year. High-risk patients undergo urine cytology and cystoscopy every three months for the initial two years, transitioning to bi-annual checks in the third year, and then annually from the fifth year onward until the end of life. Additionally, high-risk patients undergo an annual upper urinary tract examination (CTU examination). The follow-up program for intermediate-risk patients is in between, contingent on individual recurrence factors and general conditions. Successful follow-up is defined as either surviving until at least one visit during the entire follow-up period or experiencing mortality. If tumor recurrence is suspected during cystoscopy, confirmation is achieved through histopathological examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariable definitions and the construction of nomograms\u003c/h2\u003e \u003cp\u003eThe study encompassed various variables, including age at diagnosis, tumor number, tumor size, prostate volume, pathological grade, and clinical stage. Patients were divided into two groups by age at diagnosis of 65 years. PV data was obtained from the patient\u0026rsquo;s ultrasound and MRI reports, computed using the ellipsoid formula: 0.52 \u0026times; [width (cm)] \u0026times; [length (cm)] \u0026times; [height (cm)] and the results were then averaged across both reports. Subsequently, the patients were divided into two groups with a PV of 30 ml as the threshold. Tumor size was obtained from the MRI reports of the patients and classified according to the diameter of 3 cm, then the patients were divided into large and small tumor groups. The histological grading classification was performed following the World Health Organization 2004/2016 system, while the clinical stage was performed according to the 2017 TNM classification by the American Joint Committee on Cancer. In this study, two nomograms were developed utilizing data from the training cohort. We built nomogram 1 using all the independent risk variables associated with RFS determined by the Cox regression model, while nomogram 2 contained all the other independent risk variables except PV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic value of prostate volume\u003c/h2\u003e \u003cp\u003eTo assess the impact of PV on the recurrence of NMIBC patients, a chi-square test was employed to analyze the relationship between PV and the recurrence of NMIBC patients. Additionally, survival analysis was conducted using the Kaplan-Meier survival analysis, involving the plotting of survival curves. The log-rank test was then applied to compare the RFS of patients in the two PV groups to determine whether a statistically significant association existed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS software (version 25.0) and R statistical software (version 4.3.1). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was taken as a statistically significant difference. Differences between groups were assessed using the chi-square test. Survival analysis employed the Kaplan-Meier survival analysis.\u003c/p\u003e \u003cp\u003eUnivariable and multivariate Cox proportional hazards models estimated the hazard ratio (HR) and corresponding 95% confidence interval (95% CI) for all covariates. Covariates that exhibited statistically significant in the univariable analysis were included in a multivariable model for the development of nomograms. The calibration of the nomograms was assessed with a calibration curve. The discrimination of the nomograms was evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curve, and the area under the curve (AUC). DCA was employed to evaluate the clinical utility of the model.\u003c/p\u003e \u003cp\u003eWe calculated a risk score for each patient based on the established nomogram including PV information. The cutoff score is determined by using the \u0026ldquo;surv_cutpoint()\u0026rdquo; function within the \u0026ldquo;survminer\u0026rdquo; package in the R language to divide patients into high or low-risk groups. Subsequently, Kaplan-Meier survival curves were plotted for patients in two risk groups with differences scrutinized using Log-rank tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eBased on the screening criteria, we enrolled a total of 555 eligible patients. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the demographic and clinicopathologic characteristics of all cohorts, distinguishing between the training cohort (n\u0026thinsp;=\u0026thinsp;373) and the external validation cohort (n\u0026thinsp;=\u0026thinsp;182). Patient characteristics included age (\u0026lt;\u0026thinsp;65 years, \u0026ge; 65 years), grade (PUNLMP, low grade, high grade), T-stage (Ta, T1), number of tumors (single, multiple), prostate volume (\u0026le;\u0026thinsp;30 ml, \u0026gt; 30 ml), and tumor size (\u0026lt;\u0026thinsp;3 cm, \u0026ge; 3 cm). The difference in each variable between the two cohorts was not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinicopathologic characteristics of the training and validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExternal validation cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;182)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;555)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141(37.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205(36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195(52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e289(52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePUNLMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61(11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102(56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290(52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185(49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e265(47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219(58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118(64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337(60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154(41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218(39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProstate volume\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e311(83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143(78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e454(81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;30 ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;3 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264(70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128(70.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e392(70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163(29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236(63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127(69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363(65.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137(36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192(34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: PUNLMP, papillary urothelial neoplasms of low malignant potential\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of prognostic predictors\u003c/h2\u003e \u003cp\u003eWe did univariate Cox regression analyses for six variables for patients in the training cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which showed that clinical T-stage (T1: HR\u0026thinsp;=\u0026thinsp;2.78, 95%CI: 1.85\u0026ndash;4.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prostate volume (HR\u0026thinsp;=\u0026thinsp;2.72, 95%CI: 1.78\u0026ndash;4.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor number (HR\u0026thinsp;=\u0026thinsp;3.61, 95%CI: 2.40\u0026ndash;5.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor size (HR\u0026thinsp;=\u0026thinsp;2.20, 95%CI: 1.48\u0026ndash;3.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), pathologic grade (HR\u0026thinsp;=\u0026thinsp;3.03, 95%CI: 2.10\u0026ndash;4.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and age (HR\u0026thinsp;=\u0026thinsp;1.52, 95%CI: 1.02\u0026ndash;2.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were associated with recurrence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses of factors associated with RFS in the training cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.52 (1.02\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.10 (0.73\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.61 (2.40\u0026ndash;5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.08 (2.02\u0026ndash;4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePUNLMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.03 (2.10\u0026ndash;4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.30 (1.49\u0026ndash;3.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;3 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.20 (1.48\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.56 (1.03\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProstate volume\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;30 ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.72 (1.78\u0026ndash;4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.03 (1.96\u0026ndash;4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.78 (1.85\u0026ndash;4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.68 (1.05\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; PUNLMP, papillary urothelial neoplasms of low malignant potential; RFS, recurrence-free survival\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, we conducted a multivariate analysis to further identify potential risk factors for recurrence in NMIBC patients. Multifactorial Cox regression analysis revealed independent risk factors including pathological grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor size (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), T-stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), prostate volume (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and tumor number (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between prostate volume and recurrence in NMIBC patients\u003c/h2\u003e \u003cp\u003eOur dataset comprised 555 patients divided into either a recurrence-free or a recurrence group. Among those in the non-recurrence group, 12.4% (50/403) of patients exhibited PV exceeding 30 ml, contrasting with 33.6% (51/152) of patients in the recurrence group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To assess the impact of PV on NMIBC recurrence, we employed the Kaplan-Meier survival analyses. The mean RFS for patients with a large PV was 23.129\u0026thinsp;\u0026plusmn;\u0026thinsp;1.478 months, significantly inferior to that of patients with a small PV (30.912\u0026thinsp;\u0026plusmn;\u0026thinsp;0.580 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This suggests that PV constitutes a recurrence factor affecting male NMIBC patients. Multifactorial Cox regression analysis demonstrated that PV remained an independent risk factor for RFS after adjustment for clinicopathologic variables (HR\u0026thinsp;=\u0026thinsp;3.03; 95%CI, 1.96\u0026ndash;4.68; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of nomograms\u003c/h2\u003e \u003cp\u003eWe further constructed two nomograms based on the results of the univariate and multivariate Cox regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nomogram 1 includes six prognostic factors (age at diagnosis, number of tumors, prostate volume, tumor size, pathological grade, and clinical stage), and although age at diagnosis was not statistically significant in the training cohort, it was included in the model because of its clinical significance. In comparison to nomogram 1, Nomogram 2 only includes five variables, excluding prostate volume. Nomogram 2 serves as a control to investigate the impact of prostate volume on the model.\u003c/p\u003e \u003cp\u003eIn nomogram 1, given the values of the 6 prognostic factors for patients, we can graphically calculate the predicted probabilities of recurrence-free within 1, 2, and 3 years after surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, using the values of the 5 prognostic factors in nomogram 2, we can estimate the probabilities of recurrence-free at the 3 time points mentioned above (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The nomogram 1 indicated the most significant contribution from pathological grade, followed by tumor number, prostate volume, clinical stage, tumor size, and age at diagnosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eValidation and comparison of two nomograms\u003c/h2\u003e \u003cp\u003eIn the training cohort, the C-index of the model incorporating PV was 0.781 (95%CI 0.740\u0026ndash;0.820), surpassing the C-index of the model excluding PV, which was 0.766 (95%CI 0.724\u0026ndash;0.808) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Calibration curves demonstrated excellent performance across all cohorts, as evidenced by the overlap of 1-, 2-, and 3-year calibration curves of the training and external validation cohorts with standard curves (diagonal lines with a slope of 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further compare the two nomograms, ROC curves and DCA curves were plotted. As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Model 1 exhibited a higher AUC in both the training and external validation cohorts, indicating superior accuracy compared to Model 2. Moreover, the DCA curves for Model 1 showed greater net benefit over a larger range of threshold probabilities than Model 2, suggesting enhanced clinical utility of this nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This underscores that the inclusion of the PV variable in the nomogram significantly improves the accuracy and clinical applicability of the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRisk classification model based on nomogram\u003c/h2\u003e \u003cp\u003eThe cutoff value for the risk score is set at 194.306. Subsequently, patients in the training cohort with risk scores\u0026thinsp;\u0026le;\u0026thinsp;194.306 are classified into the low-risk group (n\u0026thinsp;=\u0026thinsp;308), while those with risk scores\u0026thinsp;\u0026gt;\u0026thinsp;194.306 are classified into the high-risk group (n\u0026thinsp;=\u0026thinsp;65).\u003c/p\u003e \u003cp\u003eThe performance of our risk classification model was then assessed by plotting the Kaplan-Meier survival curves for the training cohort, the external validation cohort, and the combined cohort, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In brief, the 3-year recurrence rates for the low-risk group were 16% in the training cohort, 22% in the external validation cohort, and 18% in the combined cohort. In contrast, the 3-year recurrence rate was 62%, 56%, and 60% in the high-risk groups in the three aforementioned cohorts. What\u0026rsquo;s more, the 1-year, and 2-year recurrence rates showed similar results. The significant RFS differences between the high-risk group and the low-risk group demonstrate the precise accuracy of our model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are numerous factors influencing the prognosis of NMIBC patients, such as advanced age, female gender, tumor size, multifocality, clinical staging, and tumor grading. Our primary focus is to explore the impact of PV on the recurrence of NMIBC patients.\u003c/p\u003e \u003cp\u003eLUTS are common in adult men[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], LUTSs caused by BPH promote the accumulation and concentration of carcinogens involving chemical substances such as polycyclic aromatic hydrocarbons and 2,4-diamino toluene in the bladder, extending the contact time with the bladder mucosa, which results in the induction of BC[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The obstruction of urine flow, accompanied by increased residual urine, also raises the likelihood of urinary tract infections. Inflammation can stimulate the proliferation or metaplasia of transitional epithelial cells in the bladder. The bladder epithelial cells undergoing proliferation or metaplasia, when exposed to carcinogenic substances in concentrated urine, gradually develop tumors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study utilized multi-institutional data to validate the correlation between PV and recurrence in NMIBC patients. Our study findings indicate an increased susceptibility to NMIBC recurrence in patients with PV exceeding 30 ml. What\u0026rsquo;s more, Kaplan-Meier survival curves demonstrate a notably diminished RFS for patients with larger PV. Multifactorial Cox regression analysis reveals a negative correlation between increasing PV and decreasing RFS. However, the specific mechanism remains unclear. We hypothesize that patients with BPH exhibit larger PV, and larger PV could be associated with LUTS, potentially leading to the accumulation and concentration of carcinogens in urine. Consequently, this process may increase the risk of NMIBC recurrence. Further studies are needed to elucidate the precise underlying mechanism.\u003c/p\u003e \u003cp\u003eIn recent years, several nomograms have been proposed to assist doctors in making treatment decisions for NMIBC patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], a review by Kluth et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]identified five NMIBC nomograms. Unfortunately, only one of these nomograms underwent external validation and is applicable to the Japanese population[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In comparison to the aforementioned nomograms, our models are based on a Chinese cohort from multiple medical institutions, encompassing a larger patient sample of 555 cases. We introduced two nomograms and identified that PV plays a significant role in predicting NMIBC recurrence after comparing their performances. Additionally, we implemented an external validation cohort to validate the utility of the models. Moreover, establishing a predictive model for the recurrence of NMIBC patients, incorporating factors such as PV, will aid in counseling patients and making treatment decisions.\u003c/p\u003e \u003cp\u003eTo make the model more clinically applicable, we introduced a risk classification model. Calculating individual risk scores using the established nomogram, we determined a cutoff value and divided patients into low-risk and high-risk groups. Kaplan-Meier curves exhibit clear differentiation between the two groups. In clinical practice, grouping by risk classification is preferable to simply calculating risk probabilities. This approach provides valuable information for tailoring subsequent treatment regimens to individual patients.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations in our study. Although we established the relationship between PV and NMIBC patient recurrence, the underlying mechanisms remain unclear. Another notable constraint is the potential sensitivity of our results to selection bias, given the essentially retrospective and non-randomized nature of this study. Additionally, the follow-up time for the included patients in this study may also be relatively long. Nonetheless, our research indicates that PV is an independent recurrence factor for NMIBC. Elevated PV correlates with poorer RFS, and the reduction of PV may play a crucial role in preventing the recurrence of NMIBC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe included a large multi-institutional cohort of the Chinese population to discover the prognostic value of PV on NMIBC recurrence. We further established as well as validated a more accurate nomogram for predicting recurrence probability in patients with NMIBC by including PV.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebladder cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTURBT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etransurethral resection of bladder tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMIBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-muscle invasive bladder cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emuscle-invasive bladder cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBPH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebenign prostatic hyperplasia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLUTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elower urinary tract symptoms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprostate volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econcordance index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operator curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLHT conceived and designed the study. HDC, LMY was involved in writing the initial draft of the manuscript. HDC, LP, LYQ, LMY, LH, WWB, MCX, and CLJ were involved in data collection. LHT, WYC, QL, and SZQ were involved in manuscript drafting and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by project from National Nature Science Foundation of China (81972371); Shanghai Songjiang District Medical and Health Science and Technology Research Project (21SJKJGG97).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Shanghai General Hospital Institutional Review Board approved this retrospective study. The requirement for informed content was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Int J Clin Oncol. 2010;15:271\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10147-010-0049-6\u003c/span\u003e\u003cspan address=\"10.1007/s10147-010-0049-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prostate volume, Non-muscle invasive bladder cancer, Nomogram, Recurrence","lastPublishedDoi":"10.21203/rs.3.rs-4728588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4728588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eWe conducted an assessment to investigate the impact of prostate volume on the recurrence of patients with non-muscle invasive bladder cancer (NMIBC). Subsequently, we developed and validated nomograms to accurately evaluate recurrence in NMIBC patients. Additionally, we examined the potential improvement in predictive capability achieved by introducing prostate volume as a variable in the model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective analysis, enrolling 555 eligible patients from seven independent medical institutions across China. We first evaluate recurrence-free survival outcomes in patients with varying prostate volumes. Subsequently, we divided patients into a training cohort and an external validation cohort. Univariate and multivariate Cox regression analyses were conducted within the training cohort. Accordingly, two nomogram models with and without prostate volumes were developed. Their performance was compared by concordance index, calibration curves, receiver operating characteristics curves, and decision curve analysis. Furthermore, a risk classification model utilizing the nomogram incorporating prostate volume was developed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 3-year recurrence-free survival was markedly lower in patients with large prostate volumes (\u0026gt;\u0026thinsp;30 ml) compared to those with relatively small prostate volumes (\u0026lt;\u0026thinsp;30 ml) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUC for the model incorporating prostate volume at 3 years in the training cohort and external validation cohort was 0.803 and 0.776, surpassing the AUC for the model excluding prostate volume at the corresponding intervals, which was 0.787 and 0.767. The 1- and 2-year AUC for the two models also exhibited similar differences. The decision curve analysis results demonstrated the significant superiority of the nomogram incorporating prostate volume over the one without it.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur investigation revealed that prostate volume significantly influences recurrence in patients with NMIBC. We successfully developed a more accurate nomogram by introducing prostate volume as a variable and provided new insights to further guide clinical management and individualized treatment of NMIBC patients.\u003c/p\u003e","manuscriptTitle":"Prognostic value of prostate volume and nomograms for predicting recurrence in patients with non-muscle invasive bladder cancer: a multi-institutional study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:38:27","doi":"10.21203/rs.3.rs-4728588/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-17T10:17:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T08:33:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-15T08:31:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-07-12T07:13:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"234cb956-eef3-4946-b287-32025d185ea5","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:04:50+00:00","versionOfRecord":{"articleIdentity":"rs-4728588","link":"https://doi.org/10.1186/s12885-025-15028-5","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-11-05 15:57:38","publishedOnDateReadable":"November 5th, 2025"},"versionCreatedAt":"2024-08-11 12:38:27","video":"","vorDoi":"10.1186/s12885-025-15028-5","vorDoiUrl":"https://doi.org/10.1186/s12885-025-15028-5","workflowStages":[]},"version":"v1","identity":"rs-4728588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4728588","identity":"rs-4728588","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}