Skip to main content
Download PDF

The application value of multi-parameter cystoscope in improving the accuracy of preoperative bladder cancer grading

BMC Urology volume 22, Article number: 111 (2022) Cite this article

Abstract

Purpose

To develop and validate a preoperative cystoscopic-based predictive model for predicting postoperative high-grade bladder cancer (BCa), which could be used to guide the surgical selection and postoperative treatment strategies.

Materials and methods

We retrospectively recruited 366 patients with cystoscopy biopsy for pathology and morphology evaluation between October 2010 and January 2021. A binary logistic regression model was used to assess the risk factors for postoperative high-grade BCa. Diagnostic performance was analyzed by plotting receiver operating characteristic curve and calculating area under the curve (AUC), sensitivity, specificity. From January 2021 to July 2021, we collected 105 BCa prospectively to validate the model's accuracy.

Results

A total of 366 individuals who underwent transurethral resection of bladder tumor (TURBT) or radical cystectomy following cystoscopy biopsy were included for analysis. 261 (71.3%) had a biopsy pathology grade that was consistent with postoperative pathology grade. We discovered five cystoscopic parameters, including tumor diameter, site, non-pedicled, high-grade biopsy pathology, morphology, were associated with high-grade BCa. The established multi-parameter logistic regression model (“JSPH” model) revealed AUC was 0.917 (P < 0.001). Sensitivity and specificity were 86.2% and 84.0%, respectively. And the consistency of pre- and post-operative high-grade pathology was improved from biopsy-based 70.5% to JSPH model-based 85.2%. In a 105-patients prospective validation cohort, the consistency of pre- and post-operative high-grade pathology was increased from 63.1 to 84.2% after incorporation into JSPH model for prediction.

Conclusion

The cystoscopic parameters based “JSPH model” is accurate at predicting postoperative pathological high-grade tumors prior to operations.

Peer Review reports

Introduction

Carcinoma of the bladder (BCa) is one of the most common malignancy in urology, with its incidence ranked tenth among malignancies worldwide [1], posing a great challenge in urologic oncology due to its propensity to recur and progress. The histologic grade is important in developing strategies for diagnosing and treating BCa [2], especially the selection of treatment options. Nowadays, transurethral resection of bladder tumor (TURBT) is commonly used for precise preoperative diagnosis. However, TURBT is not without flaws. To begin, it is debatable whether TURBT specimens generated via hot loop resection are fully reliable for histological evaluation[3]. Cheng et al. determined that in 105 matched TURBT patients, the rate of down- and upstaging at the time of RC was 3.8% and 76.2%, respectively [4]. Several studies have also shown that approximately 9–49% of bladder tumors were understaged on histopathological examination, hence increasing the risk of early recurrence and progression [5,6,7,8]. Second, TURBT may induce tumor cells to be released into the bloodstream, potentially resulting in potential tumor dissemination [9]. Third, the cost of anesthesia and surgery would impose an extra burden on financially strapped families. Fourth, as a diagnostic technique, TURBT would expose patients to unnecessary risk of anesthesia. Certain patients who are at a high risk of anesthesia may only have one surgical opportunity. As a result, an alternative precise preoperative histologic grade diagnostic approach is urgently needed.

Although cystoscopic biopsy was not advised as a compulsive process during cystoscopy, it was commonly utilized to develop treatment strategies in certain regions [10], particularly in some economically underdeveloped countries. Not only may a cystoscopic biopsy be used to determine the histologic grade preliminarily, but it can also be performed under local anaesthesia avoiding the unnecessary morbidity associated with general and spinal anaesthesia, reducing potential tumor dissemination and additional cost burden. Additionally, specifying the tumor grade aids in the treatment planning, as different tumor grades have varying risks of muscle invasion [11]. Therefore, accurate preoperative histologic grade determination is critical for therapeutic decision-making.

However, the histologic grade of current preoperative cystoscopic biopsy was frequently underestimated when compared to postoperative pathology [12], which may explain why it has not been widely used during cystoscopy. Besides, pathological grading classifications system has its own indeterminacy. Grade 2 is well recognized as a highly heterogeneous category in the WHO 1973 grading system, with reported proportion of bladder tumors classified as grade 2 ranging from 13 to 69% [13]. The use of WHO 2004 grading system is also recommended since it appears to be a more accurate predictor of tumor progression in clinical practice [14]. Nowadays severity of clinical symptoms [15], Vesical Imaging Reporting and Data System score (VI-RADS) [16] and endoscopic morphological features [17] could be helpful in determining tumor stage. Diagnostic ureteroscopic biopsy for upper urinary tract urothelial carcinoma could be helpful in prognostic analysis [18]. However, the prediction of pathological grade in cystoscopic biopsy may be related to certain factors that are still unknown.

In present study, we aimed to combine preoperative cystoscopic features and biopsy with clinical characteristics in order to accurately predict postoperative pathological grade, which could aid in early accurate diagnosis and treatment planning.

Methods

Ethics statement

The Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (China) approved this study, and all participants provided informed written consent.

Study population and general information

515 patients were included between October 2010 and January 2021, 149 patients were excluded due to incomplete pathological information on preoperative biopsy (ie, patients with unclear cystoscopic pictures; patients with no surgery within 1 month after biopsy; patients with chemotherapy or immunotherapy after biopsy). Then, the remaining 366 patients who meet the inclusion criteria were subsequently included in the study. All bladder biopsies were performed in outpatient setting. For training cohort, we retrospectively collected data on 366 patients who underwent TURBT or RC following cystoscopic biopsy for urothelial carcinoma at our centre between October 2010 and January 2021. Another 105 patients were included in the validation cohort between January 2021 and July 2021. All cases must be operated on immediately after the biopsy without any other treatment. If multiple biopsies were available for the same case, only the specimen closest to the operation is reviewed in this study. Clinical and pathological characteristics of training and validation cohort, including age, gender, smoking status, incidence frequency, tumor diameter, tumor number, tumor site and tumor morphology were respectively recorded and summarized in Table 1. We defined smokers as those who have smoked continuously or accumulatively for six months or more in their lifetime. Tumors were graded according to the WHO (2004) grading system [19], we used H as the high grade, L as the low grade, P as the low malignant potential, and G0 as the benign lesion including inflammation or atypical hyperplasia. Two urologic pathologists reviewed the tumor pathology and any discrepancies were resolved through consultation between the two pathologists. The study design is summarized in Fig. 1.

Table 1 Clinicopathological characteristics of bladder cancer patients in training and validation cohort
Full size table
Fig. 1
figure 1

Flow chart of the study. BCa = bladder cancer; TURBT = transurethral resection of bladder tumor; RC = radical cystectomy; CPC = clinical pathology coincidence; HGCPC = high-grade clinical pathology coincidence

Full size image

Cystoscopic characteristics and histological grading criterions

All patients’ cystoscopic appearances were reviewed by two experienced urologists and any discrepancies were resolved through consultation between the two urologists. At least two tumor tissues were obtained during biopsy to avoid the difficulties with pathological classification due to small tissue sample size. Clinicopathological parameters were compared both between the cystoscopic biopsy grading and TURBT or RC pathological grading.

We artificially classified cystoscopic morphology into the following five categories based on our experience: cauliflower pattern, seaweed pattern, flat nipple pattern, muscus pattern, and mixed pattern ((contained more than two forms)). Typical morphology image of each category was also provided (Fig. 2). Cauliflower morphology tumor is comparable to organic cauliflower consumption. Seaweed morphology tumor resembles coral seaweed in the ocean. A tumor with flat papillary morphology resembles the mushroom cap. Muscus morphology tumors like moss growing on a wall. The mixed sample contains more than two distinct types.

Fig. 2
figure 2

Typical morphology image of each category for bladder cancer. A Cauliflower morphology for bladder cancer. B Seaweed morphology for bladder cancer. C Flat nipple morphology for bladder cancer. D Muscus morphology for bladder cancer. E Mixture morphology for bladder cancer

Full size image

Statistical analysis

The chi-square test was used to evaluate the association of patient information and cystoscopy characteristics with the final pathology grade. A multi-parameter diagnostic model for the postoperative pathological high-grade BCa was constructed by multivariable binary logistic regression analysis. All the above-mentioned statistical calculations were performed using SPSS 26.0 software (IBM Corp., Armonk, NY, USA). Receiver operating characteristic (ROC) curve, the areas under the ROC curve (AUC), 95% confidence interval (CI), sensitivity, specificity, and multi-parameter diagnostic model was produced using Graphpad Prism 8.3.0 software (GraphPad, San Diego, CA, USA). The forest plot of the risk factors in predicting pathological high-grade BCa was performed using R software (Version 4.0.3). All reported P values are 2-sided with statistical significance considered at ≤ 0.05.

Results

Clinical characteristics and BCa pathology

We included 366 patients who underwent TURBT or RC following cystoscopic biopsy for training cohort and another 105 patients for validation cohort (Table 1). Of the training cohort, 246 (67.2%) underwent TURBT following cystoscopic biopsy while 120 (32.8%) underwent RC following cystoscopic biopsy. When clinical pathology was considered, 261 (71.3%) had a biopsy pathology grade that was consistent with postoperative pathology grade. The histologic grade of 72 (19.7%) was upgraded, the histologic grade of 33 (9.0%) was downgraded. The association between coincidence rate and clinical characteristics was shown in Table 2. Age, incidence, tumor diameter, tumor number, tumor site and tumor morphology were found to be associated with pathological grading inconsistency. Subgroup analysis of TURBT and RC group’s grading conformance was shown in Additional file 1: Tables S1, S2.

Table 2 Association between clinicopathological characteristics and consistency in pathological grading pre- and postoperatively
Full size table

Association between clinical characteristics and postoperative high-grade BCa

Among the 366 patients in training cohort, 210 patients were confirmed as high-grade tumors postoperatively. The association between clinical characteristics and risk of developing high-grade tumor was shown in Table 3. We discovered that a larger tumor diameter (> 3 cm), multiple tumor numbers (≥ 2), older age (≥ 65 years-old), non-pedicled tumor, multiple tumor sites (≥ 2 sties) and tumor morphology were all associated with a high-grade tumor. The risk factors of developing high-grade tumor in TURBT and RC group were stratified analyzed in Additional file 1: Tables S3, S4.

Table 3 Correlations between the postoperative pathological grading and clinicopathological characteristics in BCa patients
Full size table

Univariate and multivariate regression analysis between clinical characteristics and high-grade BCa

Among the 210 patients with postoperative high-grade tumor in training cohort, only 70.5% (148/210) cases had the same pathological grade at biopsy. Univariate analysis revealed a correlation between postoperative high-grade BCa and age, tumor diameter, tumor number, tumor site, pedicled and morphology. On multivariate analysis, postoperative high-grade BCa was associated with larger tumor diameter (HR 3.056, P = 0.002), multiple tumor site (HR 2.927, P = 0.001), non-pedicled (HR 4.280, P < 0.001), seaweed morphology (HR 0.093, P = 0.029), muscus morphology (HR 0.113, P = 0.001), and biopsy-confirmed high-grade tumor (HR 36.745, P < 0.001) (Table 4).

Table 4 Univariate and multivariate binary logistic regression analysis of clinicopathological characteristics in predicting high-grade BCa
Full size table

Establishment of a multi-parameter diagnostic model for predicting postoperative high-grade BCa

The multi-parameter diagnostic model of the five parameters for predicting postoperative high-grade BCa was constructed by multivariate regression analysis. We designated the predictive model as “JSPH model” and the equation of JSPH model was as follows: In [p/(1 − p)] = − 2.675 + 1.117 × tumor diameter + 1.074 × tumor site + 1.454 × non-pedicled − 2.371 × seaweed morphology − 2.082 × muscus morphology − 0.665 × flat nipple morphology + 0.736 × mixture morphology + 3.604 × high-grade biopsy pathology + 0.701 × low-grade biopsy pathology + 0.737 × low-malignant potential biopsy pathology (Fig. 3).

Fig. 3
figure 3

Forest plot of binary logistic regression analysis in BCa and ROC curve analysis for the “JSPH model” diagnostic performance

Full size image

Diagnostic performance of the JSPH model for high-grade BCa prediction

The diagnostic performance of the JSPH model for high-grade BCa conferred 86.2% sensitivity and 84.0% specificity. The AUC for the diagnostic performance of JSPH model was 0.917 (95% CI 0.889–0.945, P < 0.001), ROC curve analysis for the JSPH model diagnostic performance was summarized in Fig. 3. The JSPH model’s cut-off value for high-grade postoperative pathology was 0.5, which was defined by the binary logistic regression model. The JSPH model demonstrated high accuracy in detecting postoperative high-grade pathology in the training cohort, with a consistency rate of 85.2%.

Prospective validation of JSPH model

After establishing the model, we prospectively collected 105 BCa cases for validation (Additional file 1: Table S5). In validation cohort, 89 (84.8%) patients underwent TURBT following cystoscopic biopsy and 16 (15.2%) patients underwent RC following cystoscopic biopsy. 62.9% (66/105) of these patients had biopsy pathology grade that were consistent with postoperative pathology grades (Additional file 1: Table S6). The association between clinical characteristics and risk of developing high-grade tumor in prospective cohort was shown in Additional file 1: Table S7.

Among the 105 patients, 57 had high-grade postoperative pathology. However, only 63.1% (36/57) cases exhibit consistency between biopsy pathology and postoperative pathology. After incorporation into our JSPH model for validation, the accuracy of predicting postoperative high-grade pathology was 84.2% (48/57).

Discussion

We established a model which could enhance the diagnostic accuracy of high-grade tumor (85.2%) using cystoscopic biopsy prior to surgery. In a prospective validation group, the model also demonstrated a high level of accuracy of 84.2%. Tumor diameter, tumor site, non-pedicled, high-grade biopsy pathology, seaweed and muscus morphology were all independent risk factors for high-grade BCa.

There was a significant underestimation of pathology grade in preoperative cystoscopic biopsy. In our study, the pathological coincidence rate between preoperative cystoscopic biopsy and postoperative pathology was only 71.3% (261/366). Only 148 (70.5%) of the 210 postoperative high-grade cases were consistent with preoperative cystoscopic biopsy pathology. Possible explanations include the small size of biopsy tissues and the difficulty of biopsy due to the patient's low tolerance for cystoscopy. Besides, the biopsy tissue could not accurately reflect the entire tumor’s characteristics due to the tumor’s intrinsic heterogeneity. In order to overcome the cystoscopic defect in biopsy pathology, it had been reported that imaging methods such as CT (computed tomography) prediction, MRI (magnetic resonance imaging) and Micro Ultrasound were used to predict and improve the accuracy of grading or staging classification [20,21,22]. However, the prediction models built on imbalanced data are prone to overrepresent the majority type (high-grade) and underrepresent the minority type (low-grade), resulting in performing bias.

The morphology of bladder tumors varies significantly, and systematic definitions of the morphology have been lacking. Currently, it had been reported that the morphology of bladder tumors was associated with postoperative pathological stage [17]. In our study, we defined five distinct morphology subtypes and discovered that more than 70% of bladder tumors had cauliflower morphology. However, differentiation differences led to the development of various morphologies. Seaweed patterns were generally shallow, rarely infiltrated, and better differentiated than cauliflower patterns. Besides, seaweed and muscus morphologies had a higher rate of low-grade tumor while mixture morphology had a higher rate of high-grade tumor. However, mixture morphology was usually infiltrative, non-pedicle with a broad base and performed as high-grade tumor, which could reflect the tumor intrinsic heterogeneity. According to EORTC risk stratification, bladder tumors with a maximum diameter larger than 3 cm were deemed a risk factor for tumor progression [23], which could correspond to our “high-grade” tumors. Patients with pedicled bladder masses were considered to have a VI-RADS score ≤ 2 in clinical [24]. Del Giudice et al. [25] also reported that VI-RADS score ≤ 2 was deemed as a clinical predictor of NMIBC. Regarding pedicled tumors, they usually grew slowly and exhibited little aggressive behavior. High-grade bladder tumor tended to have more genetic alterations and a higher mutational load [26], which may predispose them to multiple and multisite tumor.

It was of great importance to confirm the pathological grade before operation. According to the criteria of tumor risk stratification in the EAU NMIBC guidelines [27], although some high-grade tumor could be intermediate risk based on additional risk factors, the majority of high-grade NMIBC are high risk tumors. Urine cytology (UC) has a high sensitivity for G3 and high-grade tumours (84%), however, the low sensitivity in G1/LG tumours (16%) has limited its clinical application [27]. Our model has demonstraed a high degree of specificity and sensitivity in distinguishing high grade BCa from low grade. In our prospective validation group, the pathological coincidence rate between preoperative cystoscopic biopsy and postoperative pathology was only 62.9% (66/105). Only 63.2% (36/57) postoperative high-grade cases were consistent with preoperative cystoscopic biopsy pathology. The pathological coincidence rate increased form 63.2% (36/57) to 84.2% (48/57), when our prediction model was used. Besides, the majority of MIBC cases could be classified as high-grade, because low-grade tumors and papillary urothelial neoplasms of low malignant potential were uneasy to infiltrate the basal membrane [11]. Therefore, more attention should be directed to the muscular layer examination and random biopsy during TURBT in patients with a high-grade pathology predicted by the model.

Although diagnostic TURBT was recommended prior to RC in accordance with European Association of Urology Guidelines [28], we might consider RC directly in patients with ≥ cT2 stage and predicted high grade pathology. The current multiparametric MRI (mp-MRI) based VI-RADS is capable of accurately predict tumor stage. However, there was still some misjudgment in assessing the muscular invasion of bladder tumor. It was reported that the cases scored as VI-RADS 4 had MIBC in the range of 89.7% to 100%, whereas case scored VI-RADS 3 had MIBC in the range of 45%–66.7% [16, 25, 29]. Therefore, it will be more beneficial to combine preoperative grade prediction and stage prediction when guiding the selection of treatment plan. For instance, patients with a VI-RADS score of 3 or 4 and a low-grade bladder cancer biopsy should exercise caution when selecting a treatment plan. If the predicted pathology remains low grade, we have a higher chance of preserving the bladder due to the low risk of invasion (Additional file 2: Fig. S1). If, on the other hand, the predicted pathology was high grade, we could avoid the potential influence of preoperative low-grade biopsy on treatment strategy selection, allowing us to consider RC directly (Additional file 3: Fig. S2).

Along with the VI-RADS score, the cystoscopic Likert score may also be useful in predicting tumor stage of bladder cancer, but its accuracy of was still not high enough. A total of 42 participants classified as Likert 3–5 underwent TURBT or cystectomy with pathological staging available, confirming MIBC in 13 [17]. Notably, when Likert ≥ 3 is present in combination with a confirmed high-grade tumor, it may indicate an increased risk of muscular infiltration. On the one hand, preoperative biopsy-induced underestimation could be avoided. On the other hand, cystoscope could be used exclusively to perform an accurate preoperative evaluation of tumor stage and grade, which aided in the formulation of a treatment plan. In addition, molecular markers have been explored in the diagnosis, prognosis prediction and recurrence monitoring of bladder cancer [30,31,32]. And the possibility for precise preoperative staging and grading of bladder cancer has been demotrated by cystoscopic Likert score and our cystoscopic predictive model. Thus, the implementing of cytology information with potential markers could benefit in future noninvasive precise diagnosis in bladder cancer.

Apart from the upgraded cases, we also focus on some degraded cases. In our training cohort, only 9.0% (33/336) of patients were pathological downgraded. Additionally, 72.7% (24/33) of them were downgraded following the operation due to obvious burns and deformations of pathological tissues during the operation, resulting in the absence of postoperative pathological conditions. Even if there is no postoperative pathology in these patients, the postoperative intravesical infusion plan must be based on the biopsy pathology, emphasizing the critical importance of pathological accuracy in biopsy.

Regarding limitations, this study is a single-center investigation that lacks external validation from other institutions. Besides, the tumor morphology description is a subjective judgment that may introduce bias. A larger prospective study is required to confirm our preliminary results.

Conclusion

To our knowledge, we firstly present a model that could accurately predict postoperative pathological high-grade tumor prior to operations. We also use the validation cohort to verify the model’s accuracy. Our predictive model may be used to alternate TURBT to guide the surgical selection and potentially reduce anesthesia and operation risks, as well as the economic burden on patients, associated with diagnostic TURBT.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

BCa:

Bladder cancer

TURBT:

Transurethral resection of bladder tumor

RC:

Radical cystectomy

NMIBC:

Non-muscle-invasive bladder cancer

TMT:

Trimodal therapy

WHO:

World Health Organization

BCG:

Bacillus Calmette-Guérin

AUC:

Area under the curve

ROC:

Receiver operating characteristics

CT:

Computed tomography

MRI:

Magnetic resonance imaging

VI-RADS:

Vesical Imaging Reporting and Data System

EAU:

European Association of Urology

CI:

Confidence interval

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  Google Scholar 

  2. Pellucchi F, Freschi M, Ibrahim B, Rocchini L, Maccagnano C, Briganti A, et al. Clinical reliability of the 2004 WHO histological classification system compared with the 1973 WHO system for Ta primary bladder tumors. J Urol. 2011;186(6):2194–9.

    Article  Google Scholar 

  3. Poletajew S, Fus L, Waledziak M, Pomada P, Ciechanska J, Wasiutynski A, et al. Comparison of pathological staging and grading of urothelial bladder carcinoma in post-transurethral resection and post-radical cystectomy specimens. Pol J Pathol. 2014;65(4):305–12.

    Article  CAS  Google Scholar 

  4. Cheng L, Neumann RM, Weaver AL, Cheville JC, Leibovich BC, Ramnani DM, et al. Grading and staging of bladder carcinoma in transurethral resection specimens. Correlation with 105 matched cystectomy specimens. Am J Clin Pathol. 2000;113(2):275–9.

    Article  CAS  Google Scholar 

  5. Mersdorf A, Brauers A, Wolff JM, Schneider V, Jaske G. Second transurethral resection for superficial bladder cancer: A must? 1998.

  6. Vgeli TA, Grimm MO, Ackermann RJTJou. Prospective study for quality control of TURB of bladder tumors by routine 2nd TURB (ReTURB). 1998;159.

  7. Herr HW. The value of a second transurethral resection in evaluating patients with bladder tumors. J Urol. 1999;162(1):74–6.

    Article  CAS  Google Scholar 

  8. Brauers A, Buettner R, Jakse G. Second resection and prognosis of primary high risk superficial bladder cancer: is cystectomy often too early? J Urol. 2001;165(3):808–10.

    Article  CAS  Google Scholar 

  9. Engilbertsson H, Aaltonen KE, Bjornsson S, Kristmundsson T, Patschan O, Ryden L, et al. Transurethral bladder tumor resection can cause seeding of cancer cells into the bloodstream. J Urol. 2015;193(1):53–7.

    Article  Google Scholar 

  10. Beaghler M, Grasso M 3rd. Flexible cystoscopic bladder biopsies: a technique for outpatient evaluation of the lower urinary tract urothelium. Urology. 1994;44(5):756–9.

    Article  CAS  Google Scholar 

  11. Amin MB, McKenney JK, Paner GP, Hansel DE, Grignon DJ, Montironi R, et al. ICUD-EAU International Consultation on Bladder Cancer 2012: Pathology. Eur Urol. 2013;63(1):16–35.

    Article  Google Scholar 

  12. Chang BS, Kim HL, Yang XJ, Steinberg GD. Correlation between biopsy and radical cystectomy in assessing grade and depth of invasion in bladder urothelial carcinoma. Urology. 2001;57(6):1063–6; discussion 66–7.

  13. Soukup V, Capoun O, Cohen D, Hernandez V, Babjuk M, Burger M, et al. Prognostic performance and reproducibility of the 1973 and 2004/2016 world health organization grading classification systems in non-muscle-invasive bladder cancer: a European Association of Urology Non-muscle Invasive Bladder Cancer Guidelines Panel Systematic Review. Eur Urol. 2017;72(5):801–13.

    Article  Google Scholar 

  14. Cao D, Vollmer RT, Luly J, Jain S, Roytman TM, Ferris CW, et al. Comparison of 2004 and 1973 World Health Organization grading systems and their relationship to pathologic staging for predicting long-term prognosis in patients with urothelial carcinoma. Urology. 2010;76(3):593–9.

    Article  Google Scholar 

  15. de Jong JJ, Liu Y, Boorjian SA, Bivalacqua TJ, Porten SP, Wheeler T, et al. A genomic classifier for predicting clinically aggressive luminal bladder tumors with higher rates of pathological up staging. J Urol. 2020;204(2):239–46.

    Article  Google Scholar 

  16. Wang H, Luo C, Zhang F, Guan J, Li S, Yao H, et al. Multiparametric MRI for bladder cancer: validation of VI-RADS for the detection of detrusor muscle invasion. Radiology. 2019;291(3):668–74.

    Article  Google Scholar 

  17. Bryan RT, Liu W, Pirrie SJ, Amir R, Gallagher J, Hughes AI, et al. Comparing an imaging-guided pathway with the standard pathway for staging muscle-invasive bladder cancer: preliminary data from the bladderpath study. Eur Urol. 2021;80(1):12–5.

    Article  Google Scholar 

  18. Chen H, Wang M, Weng T, Wei Y, Yang L, Ren K, et al. Prognostic analysis of diagnostic ureteroscopic biopsy for intravesical recurrence of upper urinary tract urothelial carcinoma. Urol Int. 2022;106(2):186–94.

    Article  CAS  Google Scholar 

  19. Varma M, Delahunt B, van der Kwast T. Grading noninvasive bladder cancer: World Health Organisation 1973 or 2004 may be the wrong question. Eur Urol. 2019;76(4):413–5.

    Article  Google Scholar 

  20. Zhang GM, Sun H, Shi B, Jin ZY, Xue HD. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY). 2017;42(2):561–8.

    Article  Google Scholar 

  21. Zheng Z, Xu F, Gu Z, Yan Y, Xu T, Liu S, et al. Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading. Abdom Radiol (NY). 2021;46(9):4311–23.

    Article  Google Scholar 

  22. Diana P, Lughezzani G, Saita A, Uleri A, Frego N, Contieri R, et al. Head-to-Head comparison between high-resolution microultrasound imaging and multiparametric mri in detecting and local staging of bladder cancer: the BUS-MISS protocol. Bladder Cancer. 2022;8(2):119–27.

  23. Sylvester RJ, Rodriguez O, Hernandez V, Turturica D, Bauerova L, Bruins HM, et al. European Association of Urology (EAU) Prognostic Factor Risk Groups for Non-muscle-invasive Bladder Cancer (NMIBC) Incorporating the WHO 2004/2016 and WHO 1973 classification systems for grade: an update from the EAU NMIBC guidelines panel. Eur Urol. 2021;79(4):480–8.

    Article  Google Scholar 

  24. Panebianco V, Narumi Y, Altun E, Bochner BH, Efstathiou JA, Hafeez S, et al. Multiparametric magnetic resonance imaging for bladder cancer: development of VI-RADS (vesical imaging-reporting and data system). Eur Urol. 2018;74(3):294–306.

    Article  Google Scholar 

  25. Del Giudice F, Barchetti G, De Berardinis E, Pecoraro M, Salvo V, Simone G, et al. Prospective assessment of vesical imaging reporting and data system (VI-RADS) and its clinical impact on the management of high-risk non-muscle-invasive bladder cancer patients candidate for repeated transurethral resection. Eur Urol. 2020;77(1):101–9.

    Article  Google Scholar 

  26. Pietzak EJ, Bagrodia A, Cha EK, Drill EN, Iyer G, Isharwal S, et al. Next-generation sequencing of nonmuscle invasive bladder cancer reveals potential biomarkers and rational therapeutic targets. Eur Urol. 2017;72(6):952–9.

    Article  CAS  Google Scholar 

  27. Babjuk M, Burger M, Capoun O, Cohen D, Comperat EM, Dominguez Escrig JL, et al. European association of urology guidelines on non-muscle-invasive bladder cancer (Ta, T1, and Carcinoma in Situ). Eur Urol. 2022;81(1):75–94.

    Article  Google Scholar 

  28. Witjes JA, Bruins HM, Cathomas R, Comperat EM, Cowan NC, Gakis G, et al. European Association of urology guidelines on muscle-invasive and metastatic bladder cancer: summary of the 2020 guidelines. Eur Urol. 2021;79(1):82–104.

    Article  CAS  Google Scholar 

  29. Ueno Y, Takeuchi M, Tamada T, Sofue K, Takahashi S, Kamishima Y, et al. Diagnostic accuracy and interobserver agreement for the vesical imaging-reporting and data system for muscle-invasive bladder cancer: a multireader validation study. Eur Urol. 2019;76(1):54–6.

    Article  Google Scholar 

  30. Soria F, Krabbe LM, Todenhofer T, Dobruch J, Mitra AP, Inman BA, et al. Molecular markers in bladder cancer. World J Urol. 2019;37(1):31–40.

    Article  Google Scholar 

  31. Afferi L, Moschini M, Cumberbatch MG, Catto JW, Scarpa RM, Porpiglia F, et al. Biomarkers predicting oncological outcomes of high-risk non-muscle-invasive bladder cancer. Minerva Urol Nefrol. 2020;72(3):265–78.

    Article  Google Scholar 

  32. Fasulo V, Paciotti M, Lazzeri M, Contieri R, Casale P, Saita A, et al. Xpert Bladder Cancer Monitor May Avoid Cystoscopies in Patients Under “Active Surveillance” for Recurrent Bladder Cancer (BIAS Project): Longitudinal Cohort Study. Front Oncol. 2022;12:832835.

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to all departments that contributed to this study.

Funding

This research was funded by grants from the National Natural Science Foundation of China (grantsNo.82072832,81772711), the “333” project of Jiangsu Province (LGY2016002), Jiangsu Province’s Key Provincial Talents Program (ZDRCA2016006), Scientific research project of Gusu College of Nanjing Medical University (GSKY20210222) and Suzhou Gusu Health talents training project (GSWS2020061).

Author information

Author notes

  1. Qikai Wu, Lingkai Cai and Baorui Yuan have contributed equally to this work

Authors and Affiliations

  1. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People’s Republic of China

    Qikai Wu, Lingkai Cai, Baorui Yuan, Qiang Cao, Juntao Zhuang, Dexiang Feng, Jun Tao, Pengchao Li, Xiao Yang & Qiang Lu

  2. Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People’s Republic of China

    Meiling Bao & Zhen Wang

  3. Department of Urology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215008, People’s Republic of China

    Qiang Shao

Authors
  1. Qikai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingkai Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baorui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juntao Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Meiling Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dexiang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jun Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pengchao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Qiang Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Xiao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Qiang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QW, XY, LC, BY wrote the main manuscript text. MB, ZW prepared Figs. 1–3. QC, JZ, DF prepared Tables 1–4. JT, PL prepared the Supplementary data. QS, QL Review and edit the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qiang Shao, Xiao Yang or Qiang Lu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations. This was a retrospective study that received approval by the Hospital Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2021-SR-277). A written informed consent was obtained from all subjects.

Consent for publication

Not applicable.

Competing interest

The authors have no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. Supplementary Table 1. Association between clinicopathological characteristics and consistency in pathological grading pre- and postoperatively (TURBT group). Supplementary Table 2. Association between clinicopathological characteristics and consistency in pathological grading pre- and postoperatively (RC group). Supplementary Table 3. Correlations between the postoperative pathological grading of TURBT and clinicopathological features in BCa patients. Supplementary Table 4. Correlations between the postoperative pathological grading of RC and clinicopathological features in BCa patients. Supplementary Table 5. Clinicopathological characteristics of bladder cancer patients in validation cohort. Supplementary Table 6. Association between clinicopathological characteristics and consistency in pathological grading pre- and postoperatively (validation cohort). Supplementary Table 7. Correlations between the postoperative pathological grading and clinicopathological characteristics in BCa patients (validation cohort).

Additional file 2

. Fig. S1. Cystoscopic and MRI scans of a patient with low-grade BCa on cystoscopic biopsy, low-grade BCa predicted by the JSPH model, and low-grade T1 BCa pathology on TURBT.

Additional file 3

. Fig. S2. Cystoscopic and MRI scans of a patient with low-grade BCa on cystoscopic biopsy, high-grade BCa predicted by the JSPH model, and high-grade T2 BCa pathology on TURBT.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Q., Cai, L., Yuan, B. et al. The application value of multi-parameter cystoscope in improving the accuracy of preoperative bladder cancer grading. BMC Urol 22, 111 (2022). https://doi.org/10.1186/s12894-022-01054-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12894-022-01054-z

Keywords

  • Bladder cancer
  • Cystoscopic biopsy
  • Pathological grade
  • Predictive model
  • High grade

BMC Urology

ISSN: 1471-2490

Contact us