J Urol Oncol > Volume 21(2); 2023 > Article
Choi, Gwak, Suh, Lim, Song, You, Jeong, Hong, Hong, Kim, and Ahn: The Predictive Value of the Preoperative Systemic Inflammatory Response Indices in Non-Organ-Confined Disease in Upper Urinary Tract Urothelial Carcinoma

Abstract

Purpose

This study aims to evaluate the systemic inflammatory response indices (SII) for the prediction of the non-organ-confined (non-OC) disease in upper urinary tract urothelial carcinoma (UTUC) patients.

Materials and Methods

From March 2010 to March 2020, patients who underwent radical nephroureterectomy (RNU) in a single tertiary center were retrospectively reviewed. Tumor location, multifocality, hydronephrosis on preoperative imaging, and preoperative SII, including C-reactive protein-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio (PLR) were used for analysis. Non-OC defined by locally advanced (pT3-4) or node-positive disease (pN1-2) in pathologic examination. Multivariable logistic regression was used for determining independent predictive markers of non-OC disease. Factors associated with locally advanced (pT3-4), and node-positive (pN1-2) disease were also analyzed.

Results

Overall, 711 UTUC patients who underwent RNU, without neoadjuvant chemotherapy, were analyzed. The average age was 68.6±9.9 years and 507 patients were male. Non-OC disease was 36.8% (262 of 711); specifically, 35.9% (255 of 711) was locally advanced and 7.2% (51 of 771) was node-positive disease. Multivariable analysis demonstrated hydronephrosis (odds ratio [OR], 1.46; 95%confidence interval [CI], 1.06-2.01; p=0.02), high PLR (OR, 1.45; 95% CI, 1.05-2.01; p=0.03), and high CAR (OR, 2.56; 95% CI, 1.79-3.66; p<0.01) were independent predictive markers non-OC disease. Hydronephrosis (p=0.01), high PLR (p=0.02), and high CAR (p<0.01) were predictive markers for locally advanced disease, and multifocal tumor (p<0.01) and high CAR (p<0.01) were predictive markers for node-positive disease.

Conclusions

CAR is a novel important factor for predicting any subtype of non-OC disease among SII. Large scale, multicenter studies should validate the clinical role of CAR.

INTRODUCTION

Upper tract urothelial carcinoma (UTUC) is a rare urologic malignancy that arises from the urothelium of the upper part of the urinary tract, from the renal calyx to the ureter [1]. Because the muscle layer of the renal pelvis and ureter is thin, UTUC has a relatively higher risk of muscle invasion. About 50% of patients with UTUC have non-organ-confined (non-OC) disease at diagnosis [2]. Current golden standard treatment of patients with UTUC consists of radical nephroureterectomy (RNU) with bladder cuffing [3]. RNU shows good oncologic outcomes and prognosis in patients with early-stage UTUC; however, it is less effective in patients with non-OC disease because these patients have lower survival rates and require additional treatment [2, 3]. The evidence from the perioperative chemotherapy versus surveillance in upper tract urothelial cancer (POUT) trial, The adjuvant chemotherapy became standard treatment [4]. However, neoadjuvant chemotherapy still has benefit from results in pathologic downstaging, and patients receiving neoadjuvant treatment plus RNU have lower disease recurrence rates than patients treated with RNU alone [5]. For this reason, the proper identification of UTUC patients at highrisk for non-OC disease before RNU is important for better patient management.
Several preoperative clinical parameters are predictive of advanced pathologic stage and lymph node metastasis in patients with UTUC [6]. These factors include patient age [7], hydronephrosis [8], tumor multifocality [9], tumor location [9], and some blood-based biomarkers [10]. Because of the similarity of immune system responses to cancer and infection [11], systemic inflammatory response indices (SII) can be accurate and cost-effective blood-based predictors of disease severity or poor prognosis. Previously identified indices include the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio. In addition, the C-reactive protein-to-albumin ratio (CAR) has recently emerged as a systemic inflammatory biomarker for oncologic outcomes and disease severity in several malignancies [12, 13]. CAR may be indicative of acute and chronic activation of the immune system, as well as chronic malnutrition, suggesting that it may provide benefit not provided by other SII [14]. The present study therefore determined whether preoperative SII, especially in CAR, can predict locally advanced or node-positive disease UTUC.

MATERIALS AND METHODS

1. Ethics Approval and Informed Consent

This study was approved by the Institutional Review Board (IRB) of Asan Medical Center. The IRB waived the requirement for informed consent due to the retrospective nature of this study. The study protocol conformed to relevant guidelines and regulations.

2. Patient Selection and Cohort Follow-up Protocols

The medical records of patients who underwent RNU due to UTUC at single tertiary referral center between March 2010 and December 2020 were retrospectively reviewed. Patients with non-UC on final pathological examination and those who received neoadjuvant chemotherapy were excluded (Fig. 1). Within 1 month prior to RNU, all included patients underwent routine laboratory testing, including complete blood count and albumin levels, as well as computed tomography (CT) scans to evaluate the urinary system. Ureteroscopy with or without biopsy was not performed routinely but was dependent on whether visible tumors were detected on CT scans. Patients underwent lymph node dissection if they were clinically suspected of having nodepositive disease on CT scans or there was a high suggestion of non-OC disease from preoperative images [3].

3. Data Collection and Statistical Analysis

Demographic and clinical characteristics were collected, including age, sex, body mass index, and comorbidities such as diabetes milieus and hypertension. Factors determined by preoperative CT scans included tumor location, tumor multifocality, and hydronephrosis. Absolute neutrophil, lymphocyte and platelet counts, and C-reactive protein and albumin concentrations were determined by preoperative laboratory tests, and the NLR, PLR, and CAR were calculated.
Continuous variables were compared by Student t-tests and categorical variables by chi-square tests. Clinical parameters, including tumor location (ureter/pelvis), tumor multifocality (yes/no), hydronephrosis (yes/no), and SII (NLR, PLR, and CAR) were used for logistic regression modeling. We defined the multifocality of tumors as co-occurrence of the tumor in more than 2 locations of the urinary tract: renal pelvis, upper ureter, mid ureter, and lower ureter based on anatomical landmarks. Each systemic inflammatory index was binary transformed by the cutoff value determined by Youden J statistics [15] from receiver operating characteristic (ROC) curve analysis predicting non-OC disease.
Using collected clinical parameters and SII, we generated univariate and multivariate logistic regression model for determine independent predictive factor for non-OC disease. Non-OC disease defined by locally advanced (pT3-4) or node-positive disease (pN1-2) in pathologic examination. Furthermore, we performed additional analysis for determine predictive factor of locally advanced disease (pT3-4) and node-positive disease (pN1-2). In the subanalysis for locally advanced disease, we generated multivariate logistic regression model for determine predictive factors of locally advanced disease (pT3-4, N=255) compared with localized disease (pT1-2 and N0, N=499). In this subanalysis, 7 patient who presented pT1-2 and N1-2 were removed from modeling. In the subanalysis for node-positive disease (pN1-2, N=51), we generated multivariate logistic regression model for determine predictive factor of node-positive disease (pN1-2, N=51) compared with node negative disease (pTanyNx, N=660). In this second analysis, no patient was excluded for analysis. Variables with p<0.1 on univariate analyses were selected for multivariate analysis. All statistical analyses were performed using Python 3.9.0 based on packages dependent on statsmodel [16]. All p-values were 2-sided, with p<0.05 indicating statistical significance.

RESULTS

1. Patient Characteristics

A review of medical records identified 711 patients who underwent RNU due to UTUC, without undergoing neoadjuvant chemotherapy, at the Asan Medical Center between March 2010 and December 2020 (Table 1). These 711 patients included 507 men (71.3%) and 204 women (28.7%), with a mean age of 68.6±9.9 years. Of these patients, 262 (36.8%) had non-OC disease, including 255 (35.9%) with locally advanced and 51 (7.2%) with node-positive disease. Moreover, 380 (53.4%) had tumors of the ureter, 122 (17.2%) had multifocal tumors, and 372 (52.3%) had hydronephrosis. These patients had a mean±standard deviation NLR, PLR, and CAR of 2.56±1.84, 139±62.5, and 0.23±0.75, respectively.

2. Comparison of Non-Organ-Confined and Localized Disease

NLR (2.86±2.01 vs. 2.39±1.72, p<0.01), PLR (152.39±75.9 vs. 131.61±51.65, p<0.01), and CAR (0.33±0.82 vs. 0.17±0.70, p=0.01) were each significantly higher in patients with non-OC than in those with localized UTUC (Table 1). The rate of hydronephrosis on CT scans was also significantly higher in patients with non-OC disease compared with localized disease (59.9% vs. 47.9%, p<0.01).

3. Optimal Cutoff Values of Systemic Inflammatory Indices

The areas under the ROC curves (AUROC) and 95% confidence interval (CI) of NLR, PLR, and CAR were 0.586 (CI, 0.543-0.629), 0.582 (CI, 0.539-0.626), and 0.620 (CI, 0.576-0.664), respectively. The optimal cutoff values calculated by Youden J statistics of AUROC for NLR, PLR, and CAR were 2.45, 143.75, and 0.09, respectively.

4. Factors Predictive of Non-Organ-Confined Disease

Univariate analyses showed that hydronephrosis (odds ratio [OR], 1.63; 95% CI, 1.20-2.22; p<0.01), high NLR (OR, 1.80; 95% CI: 1.32-2.47; p<0.01), high PLR (OR, 1.73; 95% CI, 1.27-2.37; p<0.01), and high CAR (OR, 2.88; 95% CI, 2.04-4.08; p<0.01) were significantly predictive of non-OC disease. Multivariable analyses showed that hydronephrosis (OR, 1.46; 95% CI, 1.06-2.01; p=0.02), high PLR (OR, 1.45; 95% CI, 1.05-2.01; p=0.03), and high CAR (OR, 2.56; 95% CI, 1.79-3.66; p<0.01) were independent predictive markers of non-OC UTUC (Table 2).

5. Factors Predictive of Locally Advanced Disease

Univariate analyses showed that hydronephrosis (OR, 1.67; 95% CI, 1.22-2.27; p< 0.01), high NLR (OR, 1.89; 95% CI, 1.38-2.59; p<0.01), high PLR (OR, 1.77; 95% CI, 1.29-2.42; p<0.01), and high CAR (OR, 2.79; 95% CI, 1.97-3.96; p<0.01) were significantly predictive of locally advanced disease. Multivariate model analysis found that hydronephrosis (OR, 1.50; 95% CI, 1.09-2.07; p=0.01), high PLR (OR, 1.49; 95% CI, 1.08-2.07; p=0.02), and high CAR (OR, 2.46; 95% CI, 1.72-3.52; p<0.01) were independent predictive markers of locally advanced disease (Table 3).

6. Factors Predictive of Node-Positive Disease

Univariate analyses showed that tumor location (OR, 2.00; 95% CI, 1.08-3.68; p=0.03), tumor multifocality (OR, 3.21; 95% CI, 1.75-5.88; p<0.01), high NLR (OR, 2.39; 95% CI, 1.35-4.26; p<0.01), and high CAR (OR, 3.15; 95% CI, 1.77-5.62; p<0.01) were significant predictive markers of node-positive disease. Multivariate analyses showed that multifocality (OR, 2.87; 95% CI, 1.55-5.32; p<0.01) and high CAR (OR, 2.48; 95% CI, 1.34-4.59; p<0.01) were independent predictive markers of node-positive disease (Table 3).

DISCUSSION

This study aimed to evaluate the predictive value of SII for non-OC disease in patients with UTUC. Univariate analyses showed level of SII and the preoperative hydronephrosis were significantly higher in patients with non-OC UTUC. The multivariate analysis showing that hydronephrosis, high PLR, and high CAR were independent predictive markers of non-OC disease. Moreover, sub-analyses showed that high CAR was predictive of both of locally advanced and node-positive disease.
Although POUT trial data confirmed the clinical role of adjuvant chemotherapy [4], decreased renal function after RNU affected on eligibility or regimen of adjuvant treatment [17]. Neoadjuvant chemotherapy has potential benefit than adjuvant chemotherapy, in the patients who has risks of renal function decline after RNU [18]. The prediction of the non-OC disease before RNU is clinically important, because these patients can benefit from neoadjuvant chemotherapy [19]. Many clinical, pathologic, and molecular biomarkers have been found to predict advanced stage and non-OC disease [6]. For example, older age was shown to be predictive of non-OC UTUC 7, as well as being prognostic of poor oncological outcomes. Although older age at diagnosis is associated with a higher incidence of advanced stage disease [2], the ability of age to predict non-OC UTUC remains uncertain. In this study, we also founded that patient age was not predictive factor for non-OC disease.
Recent technological advances in preoperative imaging have altered the clinical role of CT scanning for the diagnosis of UTUC and determining treatment plans [20]. A recent meta-analysis reported that the diagnostic performance of multidetector CT urography was excellent, with a pooled sensitivity of 92% (95% CI, 85%-96%) and a pooled specificity of 95% (95% CI, 88%-98%) [21]. Moreover, CT urography revealed specific tumor characteristics, including local invasiveness [22], hydronephrosis [8], and tumor location [20]. In this study, we founded hydronephrosis on CT scans was predictive factor of non-OC disease and locally advanced (pT3-4) disease.
The present study also found that tumor multifocality was predictive of node-positive disease. Tumor multifocality in pathologic specimen was associated with poor oncological outcome in organ confined UTUC [23]. Chromecki et al. [23] reported tumor multifocality, which defined by the presence of tumors in both of the ureter and pelvis, is associated with high grade tumor, tumor stage, lymph node metastasis, and survival. However, most studies defined tumor multifocality using pathologic specimens; thus, this definition is not suitable for the preoperative evaluation of disease aggressiveness. In this study, we defined the multifocality of tumors from preoperative CT scan based on anatomical landmark. Although the present study found that multifocal tumors did not predictive for locally advanced disease, it can use for prediction of node-positive disease in UTUC.
Although genetic and molecular biomarkers show potential benefit for prediction of stage or oncological outcome of UTUC [24], SII are still clinically useful because these assays are easy to perform, rapid, and inexpensive [25]. However, previous inflammatory markers such as NLR or PLR, have a potential association with cancer-related cachexia, which can have potential confounding effects [26]. Despite of other SII, CAR reflect both of systemic inflammatory responses and status of malnutrition [27], which can be both the cause and effect of tumorigenesis, and has been associated with reduced patient survival and response to treatment [28]. CAR has shown promising results in other malignancies [12, 13] and urothelial carcinomas [29]. The present study found that CAR was a better predictor of non-OC disease, both of locally advanced or node-positive UTUC than other SII.
This study had several limitations, including its retrospective design and inclusion of patients from a single center, which may have resulted in an inevitable risk of bias. Negligible number (n=44) of overlapping populations of locally advanced disease (N=499) and node-positive disease (N=51); thus, the actual role of CAR on node-positive disease should be needed to investigate in large number of node-positive patient cohort. Moreover, this study included only Asian patients, and these findings might differ in other ethnicities. Despite these limitations, to the best of our knowledge, this is the first and largest study showing that CAR is a predictive marker of non-OC UTUC, and it constantly showing that CAR was predictive of locally advanced and node-positive disease. And the minor point, we founded tumor multifocality in preoperative CT scan can be predictive factor for node-positive disease in UTUC. Large scaled, prospective, multicenter studies are needed to validate the predictive role of preoperative CAR and determine its cutoff value for patients with UTUC.

CONCLUSIONS

Hydronephrosis, high PLR, and high CAR were independent predictive markers of non-OC UTUC and of locally advanced disease, whereas high CAR and tumor multifocality were predictive of node-positive disease. Taken together, these findings show that CAR is a novel predictor of any subtype of non-OC disease. Prospective, large scale, multicenter studies are needed to validate the clinical role and determine the optimal cutoff value of CAR in patients with UTUC.

NOTES

Conflicts of Interest

The authors have nothing to disclose.

Funding/Support

This study received no specific grant from any funding agency in the public, commercial, or notfor-profit sectors.

Author Contribution

Conceptualization: JS; Data curation: SKC, CHG; Formal analysis: SKC, JS, BL; Funding : None; Methodology: JS; Project administration: SKC, JS; Visualization: None; Writing-original draft: SKC, JS; Writing-review&editing: KS, BL, CS, DY, IGJ, JHH, BH, CSK, HA.

Fig. 1.
Flow chart of patient selection for the study population. AMC, Asan Medical Center; UTUC, upper tract urothelial carcinoma.
juo-21-2-174f1.jpg
Table 1.
Baseline characteristics of the study population
Characteristic Total (n=711) Non-organ-confined disease (n=262) Localized disease (n=449) p-value
Age (yr) 68.6 (62.0-76.0) 69.4 (64.0-76.0) 68.1 (61.0-76.0) 0.08
Female sex 204 (28.7) 81 (30.9) 123 (27.4) 0.32
Body mass index (kg/m2) 25.1 (22.6-26.5) 24.4 (22.5-26.3) 25.5 (22.8-26.6) 0.27
Diabetes mellitus 170 (23.9) 65 (24.8) 105 (23.4) 0.67
Hypertension 354 (49.8) 137 (52.3) 217 (48.3) 0.31
Tumor location 380 (53.4) 143 (54.6) 237 (52.8) 0.64
Renal pelvis 363 (51.1) 125 (47.7) 238 (53.0)
Upper ureter 127 (17.9) 56 (21.4) 70 (15.6)
Mid ureter 150 (21.1) 54 (20.6) 96 (21.4)
Lower ureter 201 (28.3) 71 (27.1) 130 (29.0)
Multifocal tumor 122±17.1 51±19.5 71±15.8 0.21
Hydronephrosis 372 (52.3) 157 (59.9) 215 (47.9) <0.01*
NLR 2.56±1.85 2.86±2.01 2.39±1.72 <0.01*
PLR 139.28±62.48 152.39±75.90 131.61±51.65 <0.01*
CAR 0.23±0.75 0.33±0.82 0.17±0.70 0.01*

Values are presented as median (interquartile range), number (%), or mean±standard deviation.

NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; CAR, C-reactive protein-to-albumin ratio.

* p<0.05, statistically significant differences.

When evaluating tumor location, duplicates were allowed during counting.

Table 2.
Univariate and multivariate logistic regression analyses of factors associated with non-organ-confined disease
Variable Univariate
Multivariable
Odds ratio 95% CI p-value Odds ratio 95% CI p-value
Age, continuous 1.01 1.00-1.03 0.09 - - 0.15
Female sex, categorical 1.19 0.85-1.66 0.32 - - -
BMI, continuous 0.96 0.92-1.01 0.15 - - -
DM, categorical 1.08 0.76-1.54 0.67 - - -
Hypertension, categorical 1.17 0.86-1.59 0.31 - - -
Tumor location (ureter), categorical 1.08 0.79-1.46 0.64 - - -
Multifocal tumor, categorical 1.28 0.87-1.91 0.21 - - -
Hydronephrosis, categorical 1.63 1.20-2.22 <0.01 1.46 1.06-2.01 0.02
High NLR, categorical 1.80 1.32-2.47 <0.01 - - 0.20
High PLR, categorical 1.73 1.27-2.37 <0.01 1.45 1.05-2.01 0.03
High CAR, categorical 2.88 2.04-4.08 <0.01 2.56 1.79-3.66 <0.01

CI, confidence interval; BMI, body mass index; DM, diabetes mellitus; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; CAR, C-reactive protein-to-albumin ratio.

Table 3.
Multivariate logistic regression analyses of factors associated with locally advanced disease and node-positive disease
Variable Locally advanced disease (n=704)
Node-positive disease (n=711)
Odds ratio 95% CI p-value Odds ratio 95% CI p-value
BMI, continuous - - 0.44 - - -
Tumor location (ureter), categorical - - - - - 0.63
Multifocal tumor, categorical - - - 2.87 1.55-5.32 <0.01
Hydronephrosis, categorical 1.50 1.09-2.07 0.01 - - 0.30
High NLR, categorical - - 0.11 1.73 0.93-3.20 0.08
High PLR, categorical 1.49 1.08-2.07 0.02 - - 0.97
High CAR, categorical 2.46 1.72-3.52 <0.01 2.48 1.34-4.59 <0.01

CI, confidence interval; BMI, body mass index; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; CAR, C-reactive protein-to-albumin ratio.

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