A preoperative nomogram predicting the pseudocapsule status in localized renal cell carcinoma.

BACKGROUND: Tumor enucleation (TE) surgery for localized renal cell carcinoma (RCC) relies on a complete peritumoral pseudocapsule (PC). Study objective was to develop a preoperative model to predict PC status.
METHODS: The prediction model was developed in a cohort that consisted of 170 patients with localized RCC, and data was gathered from 2010 to 2015. Multivariable logistic regression analysis and R were used to generate this prediction model. The statistical performance was assessed with respect to the calibration, discrimination, and clinical usefulness.
RESULTS: The prediction model incorporated the systemic inflammatory markers [neutrophil-lymphocyte ratio (NLR); albumin-globulin ratio (AGR)], CT imaging features (tumor size and necrosis), and clinical risk factors (BMI). The model showed good discrimination, with a C-index of 0.85 (0.78-0.91), and good calibration (P=0.60). The sensitivity and specificity were 62% and 94% respectively. Decision curves and clinical impact curve demonstrated that the current model was clinically useful.
CONCLUSIONS: We constructed a model that incorporated both the systematic inflammatory markers and clinical risk factors. It can be conveniently used to preoperatively predict the individualized risk of PC invasion and identify the best candidates to receive TE surgery. 2020 Translational Andrology and Urology. All rights reserved.

Introduction

Nephron sparing surgery (NSS) is recommended as the first choice for patients with localized renal cell carcinoma (RCC) (1). Standardized NSS excises renal cancers with a layer of normal renal parenchyma to achieve negative surgical margin. But the excision of normal renal parenchyma may cause damage to renal function (2). To optimize the renal function preservation, tumor enucleation (TE) technique has been developed by excising the tumor without normal parenchyma along a natural cleavage between the peritumoral pseudocapsule (PC) and healthy parenchyma (3-5). Recently, Dell’Atti et al. developed an unclamped sutureless laparoscopic simple enucleation technique for renal tumors with low nephrometry score (6). After analyzing the complication rates, functional and oncological outcomes, we found that this innovative technique was a rational and safe approach. However, other studies reported that TE surgery increased recurrence and death due to higher positive surgical margin rate in comparison with standardized NSS, especially for patients without PC (7,8).

Peritumoral PC is a layer of fibrous connective tissue surrounding the cancer. A complete PC can prevent tumor cells from infiltrating adjacent renal parenchyma and facilitate the smooth implementation of TE surgery (9). In contrast, patients with disrupted PC or infiltrated parenchyma are recommended to receive standardized NSS to avoid positive surgical margin. Notably, the PC status is the most critical indication of TE surgery. Previous researches revealed that PC status was related to several pathological features, such as tumor grade and histological subtype (10). However, these pathological characters are difficult to identify before surgery. Hence, it is urgent to identify preoperative predictors of PC status.

It has been well summarized that systemic inflammatory markers, such as neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR) and albumin-globulin ratio (AGR), are associated with pathological characters and prognosis of multiple cancers, such as renal cancer, gastric cancer, bladder cancer and prostate cancer (11-15). Importantly, these markers can be simply and economically assessed before surgery. However, there is no study to explore the associations between these inflammatory markers and PC status. Hence, we performed this retrospective study to construct a preoperative prediction model for PC status by incorporating systematic inflammatory and clinical characters.

Methods

Study population and clinical data

This study obtained approval from our institutional ethical review board (Ethical approval ID: 201912530). The informed consent was waived for this study. A total of 250 consecutive patients with localized RCC were analyzed from 2010 to 2015. All patients received laparoscopic NSS which excised a layer of normal renal parenchyma with a thickness of 5 mm. Conversion to open surgery was not necessary in any patient. All surgical procedures were completed by skillful surgeons with more than five years of laparoscopic surgical experience. The criteria and flow diagram of patient inclusion were shown in . Clinical data were collected from archived records, including hematological parameters, age, gender, body mass index (BMI) and smoking history, and some systemic comorbidities (chronic renal disease, hypertension, and coronary heart disease). Patients would be excluded if they had diseases which affected hematological parameters, such as cold, fever, pneumonia and urinary tract infection. Blood samples were obtained and examined within three days prior to surgery. The normal references of hematological parameters were provided in . We calculated four systemic inflammatory indexes including NLR, PLR, LMR and AGR. All patients received computer tomography (CT) examination. Several CT imaging features, such as tumor size, necrosis on CT scan, tumor shape and enhancement pattern, were described with reference to a previous report () (16). Of particular note was the necrosis on CT scan that was defined as a low-density region without contrast enhancement during the cortex or parenchyma phases.

Figure S1

Flow diagram of patient inclusion. RCC, renal cell carcinoma.

Table S1

The normal references of hematological parameters

Hematological parameters	Normal reference
Neutrophile granulocyte (×109/L)	1.8–6.3
Lymphocyte (×109/L)	1.1–3.2
Monocyte (×109/L)	0.1–0.6
Blood platelet (×109/L)	125–135
Albumin (g/L)	40–55
Globulin (g/L)	20–40

Figure S2

Imaging features on CT scan. (A) Necrosis; (B) heterogeneous enhancement pattern; (C) irregular tumor shape; (D) regular tumor shape.

Pathological characters

Two independent pathologists re-evaluated all histological sections. Tumor grade was evaluated based on Fuhrman grade criteria and was divided into high grade (G3–4) and low grade (G1–2). Histological subtype was assessed according to the World Health Organization 2016 classification. Because all eligible patients were diagnosed with localized RCC, the tumor stage was not further evaluated. Four typical PC status were shown in . Complete PC: intact PC without disconnection or neoplastic infiltration; PC absence: PC was not visible at any point in all slides, and the cancer cell directly adjoined the renal parenchyma; PC infiltration: cancer cell infiltrated the PC, but not exceeded it; Parenchyma infiltration: cancer cell infiltrated peritumoral renal parenchyma. Here, we defined PC absence, PC infiltration and parenchyma infiltration as “PC invasion”.

Figure 1

PC status. (A) Complete PC: PC was intact and free from invasion; (B) Infiltrated PC: neoplastic infiltration occurred in the PC but not exceeded it; (C) Parenchyma infiltration: neoplastic infiltration occurred in the peritumoral parenchyma; (D) PC absence: PC was not visible at any point in all slides and the cancer cell directly contacted with the renal parenchyma. PC, pseudocapsule.

Statistical analysis

Pearson’s chi-squared test was used to evaluate associations between categorical variables. Independent-samples T test was applied to analyzed continuous variables. The optimal cut-off values of systemic inflammatory markers were calculated by using receiver operating characteristic (ROC) curves and Youden index. We identified the risk factors of PC invasion using univariate analysis. Factors which were significant or nearly significant in univariable analysis were reconsidered for further forward stepwise multivariate logistic regression analysis. Then we constructed a graphic nomogram by using R studio (rms package). The discrimination ability of the nomogram was measured by C-index calculated from the area under ROC curves. A calibration curve was plotted to further validate the statistical performance by using Hosmer-Lemeshow test. Decision curve analysis was performed to determine the clinical usefulness by calculating the net benefits at different threshold probabilities. On this basis, we further plotted the clinical impact curve of the nomogram. Finally, we performed survival analysis by using Cox proportional hazard regression analyses. All statistical analyses were performed using SPSS version22 (IBM, Armonk, NY, USA) and R, and all tests were two-sided with a significance level of 0.05.

Results

Clinicopathological characteristics

Eventually, 170 eligible patients were enrolled with a mean age of 52.56 years. PC invasion occurred in 70 (41.2%) tumors. All surgical margins were negative which were confirmed by an experienced pathologist. Necrosis on CT scan occurred in 60 (35.3%) tumors. One hundred thirty-five (79.4%) tumors were diagnosed as clear cell RCC. Median (IQR) of NLR, PLR, LMR and AGR were 2.41 (1.66–3.46), 119.64 (87.34–160.83), 3.75 (2.80–4.80) and 1.60 (1.50–1.80), respectively. Other detailed clinicopathological data were shown in .

Table 1

Clinicopathological characteristics

Variables	Number
Age, years, mean ± SD	52.56±12.04
RENAL score, median [range]	4.1 [2–6]
Gender, n (%)
Female	50 (29.4)
Male	120 (70.6)
BMI, n (%)
≤28 kg/m2	109 (64.1)
>28 kg/m2	61 (35.9)
Smoking history, n (%)
No	97 (57.1)
Yes	73 (42.9)
Chronic renal disease, n (%)
No	165 (97.1)
Yes	5 (2.9)
Hypertension, n (%)
No	153 (90.0)
Yes	17 (10.0)
Coronary heart disease, n (%)
No	160 (94.1)
Yes	10 (5.9)
Imaging findings on CT scan
Tumor size, n (%)
≤4 cm	122 (71.8)
>4 cm	48 (28.2)
Necrosis, n (%)
Absent	110 (64.7)
Yes	60 (35.3)
Tumor shape, n (%)
Regular	122 (71.8)
Irregular	48 (28.2)
Enhancement pattern, n (%)
Homogeneous	86 (50.6)
Heterogeneous	84 (49.4)
Inflammatory markers, median (IQR)
NLR	2.41 (1.66–3.46)
LMR	3.75 (2.80–4.80)
PLR	119.64 (87.34–160.83)
AGR	1.60 (1.50–1.80)
Histological subtype, n (%)
ccRCC	135 (79.4)
chrRCC	10 (5.9)
ONC	8 (4.7)
papRCC	17 (10.0)
Fuhrman grade, n (%)
G1–2	131 (77.1)
G3–4	39 (22.9)
PC invasion, n (%)
Absent	100 (58.8)
Present	70 (41.2)

ccRCC, clear cell renal cell carcinoma; chrRCC, chromophobe RCC; papRCC, papillary RCC; ONC, oncocytoma; PC, pseudocapsule; BMI, body mass index; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; AGR, albumin-globulin ratio; CT, computed Tomography; SD, standard deviation.

Associations between systemic inflammatory markers with clinicopathological features

The optimal cutoffs of NLR, PLR, LMR and AGR were 3.13, 113.51, 2.41, 1.35, respectively. We found that higher NLR was significantly associated with higher BMI (P=0.007) and higher necrosis rates on CT scan (P<0.001). Lower AGR was significantly related to older age (P=0.005). Lower LMR was related to higher necrosis rates on CT scan (P=0.042). All systemic markers were positively related to PC invasion. However, none of them was related to tumor grade, histological subtype, gender, tumor shape or enhancement pattern. These data were shown in .

Table S2

Associations between systematic inflammatory markers with clinicopathological characters

Variables	NLR				PLR				AGR				LMR
	<3.13	≥3.13	P value		<113.51	≥113.51	P value		≥1.35	<1.35	P value		≥2.41	<2.41	P value
Age (years)			0.334				0.499				0.005				0.084
<60	88	34			54	68			99	23			105	17
≥60	31	17			24	24			29	19			36	12
Gender			0.142				0.301				0.801				0.258
Female	39	11			26	24			37	13			44	6
Male	80	40			52	68			91	29			97	23
BMI			0.007				0.109				0.145				0.127
≤28	84	25			55	54			86	23			94	15
>28	35	26			23	38			42	19			47	14
Smoking history			0.478				0.642				0.729				0.144
No	70	27			46	51			74	23			84	13
Yes	49	24			32	41			54	19			57	16
Imaging findings
Tumor size			0.087				0.089				0.956				0.713
≤4 cm	90	32			51	71			92	30			102	20
>4 cm	29	19			27	21			36	12			39	9
Necrosis			<0.001				0.622				0.120				0.042
Absent	89	21			52	58			87	23			96	14
Present	30	30			26	34			41	19			45	15
Tumor shape			0.372				0.726				0.215				0.149
Regular	83	39			57	65			95	27			98	24
Irregular	36	12			21	27			33	15			43	5
Enhancement pattern			0.547				0.434				0.657				0.588
Homogeneous	62	24			42	44			66	20			70	16
Heterogeneous	57	27			36	48			62	22			71	13
Histological subtype			0.469				0.137				0.816				0.597
ccRCC	96	39			67	68			103	32			110	25
chrRCC	5	5			3	7			7	3			8	2
ONC	5	3			1	7			5	3			7	1
papRCC	13	4			7	10			13	4			16	1
Fuhrman grade			0.360				0.743				0.155				0.514
G1–2	94	37			61	70			102	29			110	21
G3–4	25	14			17	22			26	13			31	8
PC invasion			<0.001				0.002				<0.001				<0.001
Absent	89	11			56	44			86	14			92	8
Present	30	40			22	48			42	28			49	21

ccRCC, clear cell renal cell carcinoma; chrRCC, chromophobe RCC; papRCC, papillary RCC; ONC, oncocytoma; PC, pseudocapsule; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; AGR, albumin-globulin ratio; CT, computed Tomography; SD, standard deviation; BMI, body mass index.

Risk factors of PC invasion

Univariable analyses revealed that larger BMI (P<0.001), higher tumor grade (P<0.001), larger tumor size (P=0.002), the presence of tumor necrosis on CT scan (P<0.001), higher NLR (P<0.001), higher PLR (P=0.002), lower LMR (P<0.001) and lower AGR (P<0.001) were related to PC invasion (). Then we performed further multivariable logistic analyses to adjust all potential covariant. We demonstrated that larger BMI [odds ratio (OR) 4.81; 95% confidence interval (CI): 2.01–11.53; P<0.001], higher tumor grade (OR 8.23; 95% CI: 2.83–23.94; P<0.001), larger tumor size (OR 3.13; 95% CI: 1.19–8.25; P=0.021), the presence of tumor necrosis (OR 2.55; 95% CI: 1.03–6.30; P=0.043), higher NLR (OR 6.17; 95% CI: 1.96–19.46; P=0.002), and lower AGR (OR 3.21; 95% CI: 1.14–9.05; P=0.027) were independent risk factors of PC invasion.

Table 2

Logistic regression analyses for predictors of PC invasion

Variables	Univariable analyses			Multivariable analyses
	OR (95% CI)	P value		OR (95% CI)	P value
Age (years)
<60	Reference
≥60	1.47 (0.75–2.88)	0.263
Gender
Female	Reference
Male	0.67 (0.35–1.31)	0.243
BMI
≤28 kg/m2	Reference			Reference
>28 kg/m2	4.46 (2.28–8.70)	<0.001		4.81 (2.01–11.53)	<0.001
Smoking history
No	Reference
Yes	0.90 (0.49–1.67)	0.739
Histological subtype
ccRCC	Reference
chrRCC	0.95 (0.34–2.66)	0.926
ONC	2.14 (0.44–10.53)	0.348
papRCC	0.86 (0.15–4.82)	0.861
Fuhrman grade
G1–2	Reference			Reference
G3–4	4.61 (2.13–9.96)	<0.001		8.23 (2.83–23.94)	<0.001
Imaging findings
Tumor size
≤4 cm	Reference			Reference
>4 cm	3.01 (1.51–6.01)	0.002		3.13 (1.19–8.25)	0.021
Necrosis
Absent	Reference			Reference
Present	4.21 (2.16–8.20)	<0.001		2.55 (1.03–6.30)	0.043
Tumor shape
Regular	Reference
Irregular	1.03 (0.52–2.23)	0.935
Enhancement pattern
Homogeneous	Reference
Heterogeneous	1.26 (0.68–2.33)	0.452
Inflammatory markers
NLR
<3.13	Reference			Reference
≥3.13	10.79 (4.92–23.66)	<0.001		6.17 (1.96–19.46)	0.002
PLR
<113.51	Reference			Reference
≥113.51	2.78 (1.46–5.27)	0.002		1.94 (0.72–5.26)	0.190
LMR
≥2.41	Reference			Reference
<2.41	4.93 (2.03–11.94)	<0.001		1.18 (0.31–4.54)	0.808
AGR
≥1.35	Reference			Reference
<1.35	4.10 (1.95–8.58)	<0.001		3.21 (1.14–9.05)	0.027

ccRCC, clear cell renal cell carcinoma; chrRCC, chromophobe RCC; papRCC, papillary RCC; ONC, oncocytoma; PC, pseudocapsule; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; AGR, albumin-globulin ratio; CT, computed Tomography; BMI, body mass index.

Development of an individualized nomogram to predict PC status

Three prediction models were constructed based on these independent risk factors. Model 1 incorporated tumor size, BMI and necrosis on CT scan (). On this basis, we added two systemic inflammatory markers (NLR and AGR) to construct the Model 2 (). In addition, we constructed the Model 3 by incorporating tumor grade on the basis of Model 2 (). The results of discrimination curves demonstrated that the C-index of Model 2 was higher than that of Model 1 (0.85 vs. 0.78) (). The decision curves further indicated that Model 2 was superior to Model 1 in predicting PC invasion (). Although the ability of discrimination and clinical decision of Model 3 were slightly superior to Model 2, tumor grade was detected postoperatively. Therefore, we selected Model 2 as the final prediction model.

Figure 2

Nomograms predicting PC invasion in patients with localized RCC. (A) Model 1: clinical model incorporating BMI, tumor size and tumor necrosis on CT scans; (B) Model 1 + inflammatory markers (NLR and AGR); (C) Model 2 + pathological tumor grade. PC, pseudocapsule; RCC, renal cell carcinoma; NLR, neutrophil-lymphocyte ratio; AGR, albumin-globulin ratio; BMI, body mass index.

Figure 3

Statistical performance and clinical usefulness of the nomograms. (A) Discrimination ability of the nomograms measured by ROC curves; (B) decision curves calculating the net benefits at different threshold probabilities; (C) calibration ability of Model 2 measured by Hosmer-Lemeshow test; (D) clinical impact curve of Model 2.

Statistical performance and clinical use of Model 2

The Model 2 included NLR, AGR, tumor size, BMI and tumor necrosis on CT scan. We implemented ROC curves to measure the predictive value of this final multivariable model. The C-index of this model was 0.85 (95% CI, 0.78–0.91). The sensitivity was 62% and specificity was 94%. The calibration curve for the risk of PC invasion demonstrated good agreement between prediction and observation (). The Hosmer-Lemeshow test yielded a nonsignificant statistic (P=0.602), which indicated that Model 2 was well fitting. The decision curve demonstrated that if the threshold probability of a patient or doctor is 10%, using Model 2 to predict PC invasion added more benefit than either the treat-all-patients or the treat-none scheme. Finally, its’ clinical value was further validated with the clinical impact curve ().

Prognostic factors of overall survival

Median follow-up time was 70 (IQR, 63–80) months. Thirty-three patients died during the follow-up period. The 3- and 5-year overall survival rates were 90% and 81% respectively. showed the results of survival analyses. After we adjusted all covariates in the multivariable cox regression analyses, we found that older age [hazard ratio (HR) 5.56; 95% CI: 2.32–13.13; P<0.001], larger BMI (HR 16.25; 95% CI: 6.14–43.01; P<0.001), larger tumor size (HR 12.75; 95% CI: 4.76–34.12; P<0.001), the presence of tumor necrosis (HR 2.90; 95% CI: 1.34–6.28; P=0.007), higher NLR (HR 28.25; 95% CI: 7.44–107.25; P<0.001), higher tumor grade (HR 18.90; 95% CI: 6.78–52.64; P<0.001) and PC invasion (HR 31.49; 95% CI: 7.94–124.84; P<0.001) were independent adverse prognostic factors of overall survival.

Table 3

Cox regression analyses for prognostic factors of overall survival

Variables	Univariable analysis			Multivariable analysis
	HR (95% CI)	P value		HR (95% CI)	P value
Age (≥60 vs. <60 years)	2.51 (1.26–4.98)	0.009		5.56 (2.32–13.13)	<0.001
Gender (male vs. female)	1.94 (0.80–4.70)	0.142
BMI (>28 vs. ≤28 kg/m2)	4.82 (2.29–10.14)	<0.001		16.25 (6.14–43.01)	<0.001
Smoking history (yes vs. no)	1.69 (0.85–3.34)	0.136
Chronic renal disease (yes vs. no)	0.87 (0.11–6.93)	0.867
Hypertension (yes vs. no)	2.29 (0.94–5.64)	0.069
Coronary heart disease (yes vs. no)	0.96 (0.21–4.43)	0.960
Tumor size (>4 vs. ≤4 cm)	4.18 (2.09–8.35)	<0.001		12.75 (4.76–34.12)	<0.001
Necrosis (present vs. absent)	4.24 (2.06–8.76)	<0.001		2.90 (1.34–6.28)	0.007
Tumor shape (irregular vs. regular)	0.68 (0.30–1.58)	0.372
Enhancement pattern (heterogeneous vs. homogeneous)	0.55 (0.27–1.11)	0.094
NLR (≥3.13 vs. <3.13)	7.86 (3.65–16.94)	<0.001		28.25 (7.44–107.25)	<0.001
PLR (≥113.51 vs. <113.51)	1.17 (0.58–2.32)	0.665
LMR (<2.41 vs. ≥2.41)	2.80 (1.36–5.78)	0.005		0.82 (0.32–2.13)	0.696
AGR (<1.35 vs. ≥1.35)	2.95 (1.49–5.87)	0.002		2.24 (0.88–5.70)	0.091
PC invasion (present vs. absent)	2.45 (1.23–4.96)	0.010		31.49 (7.94–124.84)	<0.001
Fuhrman grade (G3–4 vs. G1–2)	3.51 (1.76–6.97)	<0.001		18.90 (6.78–52.64)	<0.001
Histological subtype (ccRCC vs. non ccRCC)	1.18 (0.53–2.62)	0.684

ccRCC, clear cell renal cell carcinoma; NLR, neutrophil-lymphocyte ratio; PLR, plate-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; AGR, albumin-globulin ratio; BMI, body mass index.

Discussion

Whether it is necessary to excise a layer of renal parenchyma around the cancer during NSS remains controversial. Some evidences support that preserving all non-neoplastic renal parenchyma via TE procedure achieves comparable oncological control and long-term survival with better renal function recovery when it compares with standard NSS (2,3,17,18). However, some researchers suggest that TE surgery may cause poorer prognosis (7,8). They think that excising a layer of healthy parenchyma around the cancer via standard NSS procedure is crucial for better survival by achieving negative surgical margin. Given these controversies over the oncological control of TE procedure, urologists should be carefully selecting ideal patients for TE surgery.

Cho et al. performed a prospective multicenter study (10). They found that 58.4% renal tumors were completely surrounded by a continuous PC. The larger tumor size (>4 cm) and non-clear cell subtype were risk factors for PC invasion. Therefore, they hold the view that the TE surgery should be performed with extreme care. Some other studies also explored the risk factors of PC invasion. Overall, there were several potential risk factors such as tumor stage, grade, tumor size, necrosis, and histological subtype (10,19,20). However, most of these predictors were confirmed postoperatively. We should identify some preoperative predictors for PC invasion, which can help urologists chose the appropriate surgical approach. Therefore, we generated a systemic inflammatory marker–based nomogram for the preoperative prediction of PC invasion in patients with localized RCC. This nomogram incorporated three parts of the inflammatory markers (NLR and AGR), BMI and CT imaging features (tumor size and tumor necrosis). All items successfully stratified tumors according to their risk of PC invasion. A high C-index of 0.85 and a well-fitting calibration curve indicated that this nomogram was robust. To further justify its clinical usefulness, we assessed whether nomogram-assisted decisions would improve patient outcomes by performing decision curve analysis. The decision curves suggested that if the threshold probability was 10%, using this inflammatory nomogram to predict PC invasion added more benefit than either the treat-all-patients or the treat-none scheme. Above all, the current easy-to-use nomogram facilitated urologists to identify the best candidates to receive TE surgery.

The accuracy of magnetic resonance imaging (MRI) to identify PC invasion in renal tumors is higher than that of CT (21). However, in clinical practice, CT is the first and most accurate method for diagnosing renal cancer. In addition, the cost of MRI is more expensive than CT and the examination time is longer. Overall, CT is of higher value than MRI for early diagnosis and clinical decision making, which makes most doctors and patients prefer CT examination. Therefore, all patients in our study received CT examination. We demonstrated that several imaging features on CT scans, such as tumor size and necrosis, were able to help urologists preliminarily predict PC status. Similarly, Wei et al. found that CT could differentiate the tumor grade, PC status, and tumor necrosis of renal cancer. However, neither CT nor MRI can detect the latent micro PC invasion. To increase the predictive accuracy and identify micro PC invasion, we combined these imaging features with pre-treatment systemic inflammatory markers.

Boissier et al. summarized that NLR was a strong prognostic factor of renal cancer and NLR could improve the statistical performance of predictive nomograms used in renal cancer (22). Viers et al. demonstrated that NLR could facilitate urologists to distinguish benign and malignant renal masses (23). An elevated NLR predicted advanced RCC pathology, such as higher-grade and more aggressive histologic subtypes. Similarly, Chen et al. found that preoperative lower AGR was associated with poorer prognosis and more advanced pathology, such as tumor necrosis and tumor size (24). In addition, they demonstrated that AGR could improve the accuracy of prognostic nomogram for over survival. So far, no previous study explores the role of NLR and AGR in predicting PC status. Here, we found that NLR and AGR were independent risk factors of PC invasion. The addition of NLR and AGR to the nomogram along with other risk factors (BMI, imaging features) significantly improved the statistical performance of the nomogram and increased its clinical net benefit.

We recognized several limitations of our study. Firstly, this study was retrospective, single-central and non-randomized; Secondly, all included tumors were localized RCC, which may result in selection bias. Thirdly, some preoperative hematological parameters were collected from other medical centers, which may cause measurement bias. The hematological parameters might be affected by some latent sickness. Fourthly, we did not analyze the biological mechanism under the association between these systemic inflammatory markers and PC invasion.

Conclusions

We constructed a nomogram that incorporated both the systematic inflammatory markers and clinical risk factors. It can be conveniently used to preoperatively predict the individualized risk of PC invasion and identify the best candidates to receive TE surgery.

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