Retrospective study of Diameter-axial-polar nephrometry score in predicting high pathological grade of clear cell renal cell carcinoma and papillary renal cell carcinoma

doi:10.21203/rs.3.rs-3344855/v1

Purpose: To explore whether Diameter-axial-polar nephrometry score has the ability to predict high pathological grade of clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC).

Patients and Methods: We included 430 patients who underwent robotic or laparoscopic partial nephrectomy (PN) or radical nephrectomy (RN) at our center from 1 January, 2021 to 31 March, 2023. Tumor features was recorded using Diameter-axial-polar nephrometry score (DAP score). Logistic regression was performed to analyse the relationship between DAP score and other clinical data with the high pathological grade of ccRCC and pRCC.

Results: In univariate analysis, male (p=0.006), older(p=0.008), BMI≤ 25 kg/m² (p=0.036), tumor size (p<0.001). the age-related comorbidity index (ACCI) (p =0.010) and DAP score (p <0.001) was significantly correlated with high pathological grade. Multivariate analysis showed that gender, BMI≤ 25 kg/m² and DAP score were independent risk factors of high pathological grade of ccRCC and pRCC. The areas under the curve (AUC) of Multivariate logistic regression was 0.774 (95%CI 0.725-0.823).

Conclusion: We confirmed that DAP score is independent risk factor of high pathological grade of ccRCC and pRCC and that a multivariate model incorporating gender and BMI≤ 25 kg/m² may help urologist provide treatment recommendations.

Diameter-axial-polar nephrometry score

nephrectomy

clear cell renal cell carcinoma

papillary renal cell carcinoma

pathological grade

There are approximately 430000 new cases of kidney cancer and approximately 170000 deaths worldwide every year[1]. Renal cell carcinoma (RCC) is the most common malignant tumor of the kidney. In recent years, the widespread use of imaging technology has resulted in the increase in the detection rate of RCC[2]. The 3 most common histologic subtypes of RCC include ccRCC, pRCC, and chromophobe RCC, accounting for 75–85%, 10–15% and 5–10% of all renal cancers, respectively[3, 4]. The four-tiered WHO/ISUP grading system recommended by the WHO has been validated for clear cell RCC and papillary RCC[5, 6]. Moreover, studies have reported a correlation between pathological grade of clear RCC and papillary RCC and adverse clinical outcomes[7–9]. Therefore, early prediction of the pathological grade of renal cell carcinoma is crucial. Various nephrometry scoring systems such as the R.E.N.A.L. nephrometry score (RNS), contact surface area and centrality index were in use to predict pathological grade in renal cell carcinoma[10–14]. However, the measurement variability and methodological complexity of the renal measurement systems described above increase the difficulty of translating measurement information into practice. In 2012, a study by Simmons et al. described DAP score[15], which integrates the optimization properties of the R.E.N.A.L. and C-index scoring systems. At present, the relevant research on DAP score is lacking. Therefore, our study aimed to explore whether DAP score has the ability to predict high pathological grade of ccRCC and pRCC.

Patients

We retrospectively collected the data of patients who underwent robotic or laparoscopic partial nephrectomy (PN) or radical nephrectomy (RN) at our center from 1 January, 2021 to 31 March, 2023. Inclusion criteria included: 1) available CT or MRI images, 2) pathologic results showed clear cell renal cell carcinoma or papillary renal cell carcinoma. Exclusion criteria included: 1) individuals with unilateral multiple renal tumors or bilateral renal tumors, 2) multiple surgeries undergone concurrently, 3) incomplete clinical or radiological data. This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University. Since this study was retrospective, informed consent from patients was not needed. Finally, 430 patients were included in the study. The CT or MRI images of all patients were evaluated by our experienced urologist using Diameter-axial-polar nephrometry score.

Data collection

Gender, age, ACCI[16], clinical symptom, BMI, tumor location, tumor size, DAP score and pathology grades were recorded. Patients with hematuria, low back pain, or abdominal mass were regarded as patients with clinical symptoms, while patients with no obvious discomfort, whose renal tumor were just found during physical examination, were regarded as clinically asymptomatic patients. BMI ≤ 25 kg/m² is defined as normal weight. BMI ≥ 25 kg/m² is defined as overweight. The DAP score is the sum of the Diameter (diameter of tumor), Axial (distance from the center of the kidney axis to the closest tumor edge) and Polar (distance from the kidney equatorial plane to the tumor edge) scores. The 2016 WHO Classification of Tumors of the Urinary System and Male Genital Organs was applied to evaluate the pathological grades of each patient's pathological specimen[5, 6]. WHO/ISUP grades I–II are classified as low-level group (LLG), and WHO/ISUP grades III–IV are classified as high-level group (HLG).

Statistical analysis

IBM SPSS Statistics for Windows, version 27.0 (IBM Corporation), and R software (version 4.3.1; https://r-project.org/) were used to analyze the included data. All levels of statistical significance stated were two-sided, with P < 0.05 were considered statistically significant. The normality of continuous variables was tested by using Kolmogorov-Smirnov test. Normally distributed continuous variables were reported as the mean ± standard deviation, whereas non-normally distributed continuous variables and ranked variables were presented as the median [interquartile range]. As a count, categorical variables are reported (percentage). Two independent samples t test was used to compare the means of continuous normally distributed variables. Non-normally continuous distributed variables were compared by the Mann–Whitney U test. For comparing categorical variables, the chi-squared test were employed. In univariate analysis, we included variables with P < 0.05 as candidate variables in multivariate logistic regression, and then conducted risk factor analysis using stepwise regression method. According to the results of the logistic regression analysis, we constructed the nomogram. In order to evaluate the prediction performance of the model, we drew the ROC curve and calculated the AUC value. For internal validation, a ten-fold cross validation were repeated sampling for 2000 times to calculated the mean AUC value. The validity of the nomogram was assessed by the calibration curve in conjunction with the Hosmer–Lemeshow test (P > 0.05 indicates good calibration), DCA curve, and clinical impact curve.

Patient characteristics and outcome

A total of 430 eligible patients were enrolled in this study. The characteristics of the cohort are summarized in Table 1. The cohort included 262 males (60.93%) and 168 females (39.07%) with a mean age of 56.67 years at the time of operation and median age-related comorbidity index of 1. Most patients were asymptomatic (268, 62.33%). Patients who was not overweight (270 ,62.79%) accounted for the majority of cohort. There were 228 patients (53.02%) whose tumor was on the left side, 202 patients (46.98%) whose tumor was on the right side, and the median tumor size was 4.10 cm. the cohort included 273 PNs (63.49%) and 157 RNs (36.51%). The median DAP score of tumors was 7. In regard to pathology group, 331 patients (76.98%) were included in LLG and 99 patients (23.02%) were included in HLG.

Univariate and multivariate analysis

In the univariate analysis, patients in HLG were more likely to be male (p = 0.006), older(p = 0.008), BMI ≤ 25 kg/m² (p = 0.036), and had larger tumor size (p < 0.001). ACCI (p = 0.010) and DAP score (p < 0.001) of patients in HLG were significantly larger than that of patients in LLG (Table 2).

The above variables were entered into multivariate logistic regression analysis (Fig. 1). After stepwise regression, the three variables Gender (OR 2.61, 95%CI 1.53–4.58), Overweight (OR 0.49, 95%CI 0.28–0.83) and DAP score (OR 2.13, 95%CI 1.73–2.67) were identified as independent predictors of HLG (Table 3)

Development and validation of a nomogram

we constructed a nomogram consisting of three independent risk factors (Fig. 2). To evaluate the prediction performance of the nomogram, we plotted ROC curves of predicted probabilities (Fig. 3a). The AUC was 0.774 (95%CI 0.725–0.823) and the cutoff value was 0.182 with a sensitivity of 0.848 and specificity of 0.562, indicating that the model has a good ability to distinguish patients in HLG from patients in LLG. the ten-fold cross validation method with repeated sampling 2000 times was used for internal verification of the model, and the mean AUC value was 0.734, indicating that the nomogram has relatively good reproducibility. The calibration curve is shown in Fig. 3b and the p value of the Hosmer–Lemeshow test was 0.9216, indicating good calibration in our prediction model. According to the DCA curve of the model (Fig. 3c), the net benefit range for baseline model, which only included two variables (gender and overweight), was smaller than the range of full model, indicating the predictive ability of DAP score owned powerful predictive ability. The clinical impact curve (CIC)on the ground of the DCA curve was further plotted to evaluate the clinical effects of the nomogram (Fig. 3d). CIC showed that the nomogram had a superior net benefit within the wide ranges of threshold probabilities.

The present study elicited that high pathological grade of ccRCC and pRCC is related to the anatomical characteristics of tumors. We found that the DAP score is an effective predictor of high pathological grade of ccRCC and pRCC. We also introduced gender and overweight into the nomogram which showed excellent performance.

The DAP score focuses on 3 features: tumor diameter, axial distance and polar distance, which means that larger DAP score indicates the larger tumor size and the higher centrality of the tumors. Previous studies have shown that tumor size is associated with high pathological grade [17–20]. For each 1 cm increase in size of a ccRCC, the odds of high pathological grade increased by 25%[18]. Besides, as the centrality increases, the likelihood of tumors invading renal sinus will increase. And the invasion of the tissues in the renal sinus by tumors is related to high pathological grade which had been validated in previous studies[21–26]. A possible mechanism of this correlation is that in order to escape the unfavorable environment of high osmotic concentration and low oxygen tension within inner renal medulla[27], tumors may tend to invade the renal sinus. The many thin-walled veins and lymphatic vessels in the renal sinus provide adequate blood supply and oxygen to promote tumor survival and progression[13]. Moreover, we found that the AUC of our model is slightly higher than that in a previous study reported by Kutikov et al. about predicting pathological grade by RNS. We believe that this difference in predictive performance may be related to the different pathological grading systems selected in the two studies. Previously, Fuhrman grade is used to determine the pathological grading of removed tumors. But the three parameters for determining the Fuhrman grade (size, shape and prominence of nuclear) may sometimes be inconsistent and subjectively biased, resulting in unclear grading and poor repeatability in clinical practice[28]. WHO/ISUP grade had been improved to address these problems and improve the accuracy of grading, which may make our model more effective.

Currently, preoperative pathologic evaluation of renal tumors is mainly achieved by percutaneous renal tumor biopsy (PRTB), the safety and efficacy of which have been validated in previous studies[29–31]. However, PRTB may require repeated puncture to obtain diagnostic results, which may cause patient non-cooperation and lead to associated complications, such as bleeding, infection, and arteriovenous fistulae. Compared to RPBT, using DAP score is simpler and more accessible. DAP score is not based on complex computerized analyses, but rather on manual measurements using basic image-viewing software. In addition, the interpretation of the results of DAP score is relatively simple, thus being more intuitive in clinical practice.

Similarly, the results of this study also showed that male and BMI ≤ 25 kg/m2 were risk factors for high pathological grading which match those results observed in earlier studies[32–36]. Possible explanation for the reduced risk of high pathological grade of ccRCC and pRCC in overweight patients is that they undergo imaging more frequently for various conditions and are more likely to have early detection of renal tumours with low pathological grade. Furthermore, the expression of fatty acid synthase, which is associated with RCC aggressiveness, was downregulated in patients with high BMI[37, 38]. This could also be the cause of the lower pathological grade in these patients.

The limitations of this study are as follows. First, the sample size of this study was relatively small and the data were obtained from a single medical centre in China. Data from other countries will are needed to further validate our results. The prediction model in this study can only be used to distinguish between high and low pathological grade of ccRCC and pRCC, and does not distinguish between the four grades of the WHO grading system.

We confirmed that DAP score is significant for preoperative prediction of predicting pathological grade of ccRCC and pRCC. Male, BMI≤ 25 kg/m² and larger DAP score are independent risk factor of high pathological grade of ccRCC and pRCC. The Objectively quantified prediction probabilities of nomogram will help clinicians provide more appropriate treatment recommendations for each patient

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript..

Competing Interest

The authors have no relevant financial or non-financial interests to disclose.

Author Contribution

Conception and design: Ziwen Liu

Acquisition of data: Ziwen Liu, Hao Huang

Analysis and interpretation of data: Hao Huang

Drafting of the manuscript and statistical analysis: Ziwen Liu, Hao Huang

Critical revision: Ting Sun

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval

This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University.

Consent to participate

Since this study was retrospective, informed consent from patients was not needed.

Consent to publish

Our manuscript does not contain any form of personal data

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Table 1 The characteristics of the cohort
Variables	Value
Gender (%)
Female	168 (39.07)
Male	262 (60.93)
Age (year)	56.67±11.42
ACCI	1.00 [1.00, 3.00]
Clinical symptoms (%)
No	268 (62.33)
Yes	162 (37.67)
Overweight
No	270 (62.79)
Yes	160 (37.21)
Tumor location (%)
Left	228 (53.02)
Right	202 (46.98)
Tumor size (diameter, cm)	4.10 [3.10, 5.80]
DAP score	7.00 [6.00, 8.00]
Operative methods (%)
PN	273 (63.49)
RN	157 (36.51)
Pathology (%)
ccRCC	405 (94.19)
pRCC	25 (5.81)
Pathology group (%)
LLG	331(76.98)
HLG	99(23.02)

Table 2 univariate analysis of variables associated with HLG
Variables	LLG	HLG	P
Number of patients (%)	331(76.98)	99(23.02)
Gender (%)			0.006
Female	141 (42.60)	27 (27.27)
Male	190 (57.40)	72 (72.73)
Age (year)	55.88 ±11.19	59.33 ±11.82	0.008
ACCI	1.00 [1.00, 2.00]	2.00 [1.00, 3.00]	0.010

Table 3 multivariate analysis of variables associated with HLG

No competing interests reported.

Retrospective study of Diameter-axial-polar nephrometry score in predicting high pathological grade of clear cell renal cell carcinoma and papillary renal cell carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Patients and Methods

Patients

Data collection

Statistical analysis

Results

Patient characteristics and outcome

Univariate and multivariate analysis

Development and validation of a nomogram

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Status:

Version 1