Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study

Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study Yifan Zhao, Shian Qian, Xianchuang Li, Hengxi Jin, Xiaojun Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872940/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To investigate the predictive value of pre-operative prognostic nutritional index (PNI) in biochemical recurrence (BCR) in patients with robot-assistedlaparoscopic radical prostatectomy (RARP) and to establish a BCR risk score model based on PNI. Methods: The clinical data of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University were retrospectively analyzed. The endpoint of observation was BCR. The area under the receiver operating characteristic (ROC) curve was evaluated to determine the optimal cutoff value for PNI. Kaplan-Meier analysis and Cox regression analysis were used to evaluate the correlation between PNI and BCR. 157 patients were divided into a training group and a validation group by a ratio of 7:3. By univariate and multivariate Cox regression analysis, independent prognostic factors were screened from the relevant clinicopathological factors, a BCR prediction model and nomogramwere established, then verified its value. Results: According to the ROC curve, the optimal cutoff value of PNI for 157 patients in this study was 47.425. According to multivariate Cox regression analysis, PNI and prostate-specific antigen (PSA) were identified as independent prognostic factors for predicting BCR in patients treated with RARP. A BCR prediction model formula was established based on PNI and PSA. It was proved to have good predictive value in both the training group and the validation group. Nomogram was constructed to predict the BCR of patients treated with RARP at 6-, 12-, and 24-months after surgery. The results of the calibration plots showed that the nomogram performed well in the training group and the validation group. Conclusion: PNI is an independent prognostic factor for predicting BCR in patients treated with RARP. The scoring model and nomogram based on PNI and PSA can effectively predict the risk of BCR in patients treated with RARP. prostate cancer biochemical recurrence prognostic factors prognostic nutritional index robot-assisted laparoscopic radical prostatectomy risk model nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Prostate cancer is the most common malignant tumor in the male genitourinary system, and its incidence rate second only to lung cancer among all malignant tumors in men ( 1 ). In recent years, with the PSA screening of high-risk population, the detection rate of prostate cancer has increased significantly, which also provides great help for the early diagnosis and treatment of prostate cancer patients. Radical prostatectomy (RP) is considered to be one of the most effective methods for the treatment of localized and locally advanced prostate cancer, but there are still 20%-40% of patients who develop BCR after RP ( 2 , 3 ). Although some clinical and pathological factors, such as clinical case stage, surgical margin status and Gleason score, have been proved to be independent prognostic factors for predicting postoperative BCR of prostate cancer, they have a certain degree of invasive risk and limitations ( 4 ). Therefore, there is an urgent need to find new accurate and non-invasive prognostic indicators to predict the risk of BCR early, so as to provide the best treatment plan for patients. The nutritional and inflammatory immune status of cancer patients have been considered to be important factors affecting the prognosis, and play an important role in the process of cancer progression ( 5 – 7 ). Systemic inflammation has been widely recognized as a key factor in cancer invasion, proliferation and metastasis ( 8 ). Previous studies have reported that some systemic inflammation indicators, such as neutrophil-lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) have been used to predict the prognosis of cancer ( 9 , 10 ). In addition, the deterioration of nutritional status has been a common problem in cancer patients. PNI happens to be a coefficient that combines nutritional and inflammatory immune indicators. By calculating serum albumin concentration and peripheral blood lymphocyte count, it can simply assess the condition of a patient. PNI was originally used to evaluate preoperative nutritional status and surgical complications in patients with gastrointestinal cancers. It has been proven to be a good prognostic indicator for oral cancer, breast cancer, pancreatic cancer, etc. ( 11 – 13 ). In recent years, robot-assisted surgery has made significant progress in various surgical fields, because it has the advantages of 3D surgical field of view, greater magnification effect, freer range of motion, more stable and more precise mechanical operation, which makes up for some of the technical defects of traditional laparoscopy, thus it can reduce the occurrence of intraoperative and postoperative complications ( 14 ). And it is more conducive to the recovery of patients. However, some prostate cancer patients treated with RARP are still unable to avoid BCR, so there is an urgent need to find an easily accessible and low-cost biomarker to predict it. To our knowledge, previous studies have evaluated PNI as a predictor of prognosis in patients with RARP ( 15 ). In this study, we will further confirm their correlation. We also included other commonly used inflammatory nutritional biomarkers, such as NLR, SII and albumin-globulin ratio (AGR). Finally, we combined the clinicopathological characteristics of the patients to screen out the independent prognostic factors for predicting BCR in patients treated with RARP, constructed a prognostic model and verify its effectiveness. 2. Patients and methods 2.1 Study population The clinical and pathological information of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University from December 2019 to November 2022 were retrospectively analyzed. This study inclusion criteria: ( 1 ) Prostate biopsy was performed before surgery, and the pathological report confirmed prostate cancer; ( 2 ) According to the relevant clinical examination, computed tomography (CT), magnetic resonance imaging (MRI) and bone scan examination, in accordance with the international TNM staging of prostate cancer, have a clear clinical staging; ( 3 ) There were no obvious preoperative complications; ( 4 ) Preoperative 1 week with serological samples available; ( 5 ) Received robot-assisted radical resection of prostate cancer in the First Affiliated Hospital of Soochow University, and was discharged successfully without significant complications after surgery; ( 6 ) Postoperative pathology confirmed prostate cancer, and Gleason score was used. Exclusion criteria:( 1 ) Lack of relevant clinical data and complete follow-up information; ( 2 ) Suffering from diseases affecting systemic nutritional and immune indexes; ( 3 ) Combined with other primary tumor diseases; ( 4 ) preoperative neoadjuvant therapy; ( 5 ) Endocrine therapy was given immediately without follow-up. The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University. All patients gave informed written consent before they were enrolled in this study. 2.2 Data collection and follow-up Relevant clinical data of patients were collected, including age, body mass index (BMI), preoperative total PSA, free PSA (f PSA), free PSA/total PSA (f/t), clinical stage, and postoperative pathological results, including tumor nature, Gleason score, incisal margin, lymph node status, nerve invasion, seminal vesicle invasion, and vas deferens invasion. The clinical staging was performed according to the American Joint Committee on Cancer 7th edition tumor, node, and metastasis staging system ( 16 ). Preoperative complete blood count and related biochemical indexes were obtained by fasting blood samples collected in the morning within 1 week before surgery. According to the established formula to calculate the research related to nutrition and immune scores: PNI = serum albumin (g/L) + 5 *lymphocyte count (10 9 /L); NLR = neutrophil count (10 9 /L)/ lymphocyte count (10 9 /L); AGR = serum albumin (g/L)/ serum globulin (g/L); SII = platelet count (10 9 /L) *neutrophil count (10 9 /L)/ lymphocyte count (10 9 /L). We obtained the required follow-up information by inquiring the patients' postoperative outpatient review data and telephone interview. Patients are usually required to retest PSA 6–8 weeks after surgery, every 3 months in the first and second years, and every 6 months thereafter. We defined BCR by a consecutive increase of PSA ≥ 0.2 ng/ml on two blood tests. The follow-up time was defined as the period from the time of RARP to BCR (1 decimal place reserved) ( 17 ). 2.3 Statistical analysis The statistical analyses were conducted using SPSS version 27 (SPSS Inc.) and R software (3.5.2). Medians with interquartile ranges (IQRs) and frequencies were adopted to report continuous and categorical variables, respectively. The ROC curve analysis is performed to select the most suitable demarcation point for the PNI. Differences in continuous variables were analyzed using the Mann–Whitney U-test and differences in categorical data were analyzed using the x 2 test. Kaplan–Meier survival analysis and log‐rank test were used to compare the effects of different PNI groups on the BCR rate of patients receiving RARP. Univariate and multivariate cox regression analyses were used to assess the influence of prognostic factors and to establish a risk prediction model. P < 0.05 was considered statistically significant. 3. Results 3.1 Basic information A total of 157 patients with RARP were included in this study, and detailed clinicopathological characteristics of all patients were summarized in Table 1 . The median age was 71 years, median BMI was 23.875 kg/m 2 , median PSA was 12.538ng/ml and median f/t value was 0.104. The median values of PNI, AGR, NLR and SII were 48.70, 1.7, 2.41 and 482.14. In addition, 71 patients (45.2%) had positive surgical margins, 109 patients (69.4%) had nerve invasion, 30 patients (19.1%) had seminal vesicle invasion and 16 patients (10.2%) had vas deferens invasion. The clinical staging between T3 and T4 was 72.0%. Lymph node metastasis occurred in 9 patients (5.7%). Table 1 Characteristics of the 157 participants. Variable Total n = 157 % Age (years) ≤ 70 77 49.0 >70 80 51.0 BMI (kg/m2) 47.425 104 66.2 Gleason score 6 7 4.4 7 119 75.8 8 15 9.6 9 16 10.2 Margin status Negative 86 54.8 Positive 71 45.2 Nerve invasion Negative 48 30.6 Positive 109 69.4 Seminal vesicle invasion Negative 127 80.9 Positive 30 19.1 Vas deferens invasion Negative 141 89.8 Positive 16 10.2 Lymph node status Negative 148 94.3 Positive 9 5.7 BCR No 106 67.5 Yes 51 32.5 Age (years) median (IQR) 71(66–75) BMI (kg/m2) median (IQR) 23.875(22.039–25.374) PSA (ng/ml) median (IQR) 12.538(7.961–23.500) f/t median (IQR) 0.104(0.076–0.149) AGR median (IQR) 1.7(1.5–1.9) PNI median (IQR) 48.70(45.25–50.95) NLR median (IQR) 2.41(1.73–3.27) SII median (IQR) 482.14 (321.39-713.71) 3.2 Comparison of clinicopathological data between BCR group and non-BCR group The correlation between BCR and clinical characteristics was confirmed by the x 2 test and Mann–Whitney U test. The clinical data of patients in different BCR groups are shown in the Table 2 . The results showed that the BCR group had higher clinical stage (P = 0.006), higher PSA value (P < 0.001), higher Gleason score (P < 0.001), higher NLR score (P = 0.008), higher margin positive rate (P = 0.002), higher lymph node positive rate (P < 0.001), higher invasion rate of nerve, seminal vesicle and vas deferens (P = 0.015, P < 0.001, P < 0.001), but lower PNI value (P < 0.001) compared with the non-BCR group. There was no significant difference in age, BMI, f/t, AGR and SII scores in different groups (Table 2 ). Table 2 Comparison of different BCR group. Variable BCR group non-BCR group P Number of cases 51 106 Age (years) median (IQR) 72(67,76) 70(66,74) 0.092 BMI (kg/m2) median (IQR) 24.424(21.671,25.344) 23.577(22.039,25.454) 0.464 PSA (ng/ml) median (IQR) 24.820(15.048,46.987) 9.696(6.598,15.397) <0.001 f/t median (IQR) 0.091(0.064,0.142) 0.113(0.078,0.159) 0.062 PNI median (IQR) 44.85(43.25,48.70) 49.53(47.98,51.64) <0.001 AGR median (IQR) 1.7(1.4,1.9) 1.7(1.5,1.9) P = 0.064 NLR median (IQR) 2.65(2.08,3.70) 2.29(1.58,3.03) P = 0.008 SII median (IQR) 528.27(358.22,751.01) 458.42(291.92,671.39) P = 0.113 Clinical staging T1-T2 7 37 T3-T4 44 69 0.006 Gleason score ≤ 7 28 98 >7 23 8 <0.001 Margin status Negative 19 67 Positive 32 39 0.002 Nerve invasion Negative 9 39 Positive 42 67 0.015 Seminal vesicle invasion Negative 32 95 Positive 19 11 <0.001 Vas deferens invasion Negative 37 104 Positive 14 2 <0.001 Lymph node status Negative 42 106 Positive 9 0 <0.001 3.3 Optimal PNI cutoff value before treatment 157 patients ranged from 1.7 to 47.0 months, with a median follow-up time of 15.2 months. Among them, 51 cases experienced BCR. ROC curves were drawn based on whether BCR occurred at the end of follow-up. It was found that when PNI = 47.425, the AUC was 0.799 (95% confidence interval: 0.726–0.873, Youden index = 0.532, sensitivity = 70.0%, specificity = 83.2%, p < 0.001) (Fig. 1 ). Therefore, PNI = 47.425 was determined as the optimal cutoff value. Among 157 patients, 53 patients (33.8%) were in the low PNI group (PNI ≤ 47.425), and 104 patients (66.2%) in the high PNI group (PNI > 47.425). 3.4 Clinicopathological features in different PNI groups The correlation between PNI values and clinical characteristics was confirmed by x 2 test and Mann-Whitney U test. The clinical data of patients in different PNI groups are shown in the Table 3 . The results showed that compared with the high PNI group, the low PNI group had an older age (P = 0.004), a higher Gleason score (P < 0.001), and a higher BCR rate (P < 0.001), higher seminal vesicle and vas deferens invasion rate (P = 0.036, P = 0.002), higher PSA value (P = 0.022), higher NLR score (P < 0.001) and higher SII score (P = 0.022). There was no statistical significance between different PNI groups in terms of BMI, clinical stage, f/t, positive resection margin rate, nerve invasion rate, lymph node positive rate and AGR score. Kaplan-Meier analysis of all patients showed that patients in the low PNI group had a lower BCR-free survival rate compared with the high PNI group. The log-rank test results showed that the difference between the two groups was statistically significant (x 2 = 21.292, p 47.425) P Number of cases 53 104 Age (years) median (IQR) 73(69,76) 70(66,73) 0.004 BMI (kg/m2) median (IQR) 23.665(21.593,25.438) 23.875(22.111,25.388) 0.640 PSA (ng/ml) median (IQR) 15.157(9.400,26.454) 11.303(7.053,21.024) 0.022 f/t median (IQR) 0.095(0.072,0.138) 0.108(0.076,0.151) 0.318 AGR median (IQR) 1.6(1.4,1.9) 1.7(1.5,1.9) 0.054 NLR median (IQR) 2.95(2.16,4.49) 2.27(1.57,2.90) 7 19 12 <0.001 Margin status Negative 26 60 Positive 27 44 0.304 Nerve invasion Negative 15 33 Positive 38 71 0.659 Seminal vesicle invasion Negative 38 89 Positive 15 15 0.036 Vas deferens invasion Negative 42 99 Positive 11 5 0.002 Lymph node status Negative 47 101 Positive 6 3 0.074 BCR No 18 88 Yes 35 16 <0.001 3.5 Establishment and verification of the BCR risk score model 157 patients were divided into training group and validation group according to the ratio of 7:3. The clinical data of patients in different groups are shown in the Table 4 . Among them, there were 110 patients in the training group and 47 patients in the validation group. There was no statistical difference in each variable between the two groups (P > 0.05). As shown in the Table 5 , according to univariate Cox regression analysis, in the training group, clinical stage (P = 0.038 )、PSA (P < 0.001 ), Gleason score (P < 0.001), PNI (P < 0.001), NLR (P = 0.012), positive resection margin (P = 0.011), positive lymph node (P < 0.001), nerve invasion (P = 0.043), seminal vesicle invasion (P < 0.001 ) and vas deferens invasion (P < 0.001 ) had a significant correlation with BCR. The above factors were included in multiple Cox regression analysis to further determine independent predictors. It was determined that PSA (P = 0.014) and PNI (P = 0.002) were independent predictors of BCR in patients with RARP. Then a BCR prediction model formula was established and the risk score was calculated (risk score = 0.021* PSA-0.170* PNI). The AUC values of this model in the training group and validation group were 0.870 (95% confidence interval: 0.804–0.937, sensitivity = 82.4%, specificity = 80.3%) and 0.898 (95% confidence interval: 0.800-0.996, sensitivity = 2.4%, specificity = 86.7%) respectively (Fig. 3 ). Based on the above results of Cox regression analysis, we combined the two independent predictive factors of PSA and PNI to construct a nomogram to predict the probability of BCR in patients with RARP at 6-, 12-, and 24-months after surgery (Fig. 4 ). The results showed that for every 2 points decreases in PNI, the nomogram score increased by 9 points; for every 10 ng/ml increase in PSA, the nomogram score increased by 5 points. And the corresponding BCR probability of patients increased at 6-, 12-, and 24-months after surgery. Based on calibration plots (Fig. 5 ), the predicted 6-, 12-, and 24-months BCR probabilities of the nomogram performed well in both the training and validation group. Table 4 Comparison of training group and validation group. Variable Training group Validation group P Number of cases 110 47 Age (years) median (IQR) 71(67,75) 70(64,73) 0.085 BMI (kg/m2) median (IQR) 23.559(21.664,25.359) 24.167(22.857,25.977) 0.237 PSA (ng/ml) median (IQR) 12.476(7.927,23.065) 13.060(8.000,24.820) 0.833 f/t (ng/ml) median (IQR) 0.099(0.075,0.149) 0.116(0.078,0.150) 0.747 PNI (ng/ml) median (IQR) 48.90(45.28,50.81) 47.90(44.85,51.15) 0.472 AGR (ng/ml) median (IQR) 1.7(1.5,1.9) 1.7(1.5,1.9) 0.466 NLR (ng/ml) median (IQR) 2.40(1.78,3.19) 2.41(1.73,3.62) 0.513 SII (ng/ml) median (IQR) 469.00(294.33,682.88) 520.97(358.22,770.86) 0.297 Clinical staging T1-T2 29 15 T3-T4 81 32 0.478 Gleason score ≤ 7 90 36 >7 20 11 0.452 Margin status Negative 62 24 Positive 48 23 0.541 Nerve invasion Negative 32 16 Positive 78 31 0.537 Seminal vesicle invasion Negative 91 36 Positive 19 11 0.371 Vas deferens invasion Negative 100 41 Positive 10 6 0.682 Lymph node status Negative 105 43 Positive 5 4 0.546 BCR No 76 30 Yes 34 17 0.519 Table 5 Univariate and multivariate analysis of factors associated with BCR in RARP patients. Variable Univariate analysis HR (95%CI) P Multivariate analysis HR (95%CI) P Age (continuous variable) 1.032(0.976–1.090) 0.269 BMI (continuous variable) 1.049(0.920–1.195) 0.474 PSA (continuous variable) 1.024(1.014–1.035) <0.001 1.021(1.004–1.038) 0.014 f/t (continuous variable) 0.113(0.001–11.317) 0.354 PNI (continuous variable) 0.817(0.757–0.882) <0.001 0.869(0.794–0.950) 0.002 AGR (continuous variable) 0.519(0.147–1.827) 0.307 NLR (continuous variable) 1.253(1.051–1.495) 0.012 1.133(0.914–1.405) 0.253 SII (continuous variable) 1.000(1.000-1.001) 0.185 Clinical staging (T1–T2/T3–T4) 0.331(0.117–0.940) 0.038 1.182(0.091–15.335) 0.898 Gleason score (≤ 7/>7) 0.212(0.107–0.419) <0.001 0.773(0.222–2.690) 0.686 Margin status (negative/positive) 0.406(0.203–0.813) 0.011 1.135(0.439–2.935) 0.793 Nerve invasion (negative/positive) 0.375(0.145–0.969) 0.043 2.190(0.209–22.949) 0.513 Seminal vesicle invasion (negative/positive) 0.265(0.132–0.530) <0.001 0.604(0.190–1.917) 0.392 Vas deferens invasion (negative/positive) 0.236(0.106–0.525) <0.001 0.822(0.232–2.911) 0.761 Lymph nodes (negative/positive) 0.191(0.073–0.501) <0.001 0.921(0.282–3.009) 0.892 4. Discussion In recent years, the incidence of prostate cancer has shown a significant upward trend worldwide ( 18 ). Many countries and regions have included PSA screening in routine physical examinations for older men. This will help improve the detection rate of prostate cancer, diagnose and treat of early prostate cancer, especially prostate cancer of clinical significance. Although with the development of RP technology, prostate cancer patients can obtain good tumor control and achieve radical results, there are still some patients who will inevitably experience BCR, or even fail to take appropriate measures in time, leading to the progression of the disease ( 19 , 20 ). Therefore, how to accurately predict which patients will have BCR has become a major problem for clinicians. The purpose of this study was to examine the predictive value of the PNI for BCR in patients undergoing RARP. PNI combines serum albumin concentration and lymphocyte count. It is a simple and effective objective data assessment system that can objectively reflect the patients’ preoperative nutritional and inflammatory immune status. At the same time, it overcomes the invasiveness risk caused by previous predictive factors, which can be measured with a simple blood test. Many researchers currently believe that inflammation has a certain relationship with the recurrence and metastasis of tumors ( 21 ). Because the uncontrolled persistence of inflammatory responses may lead to severe cell and genome damage, resulting in wanton cell proliferation and genome instability, and increasing the risk of malignant tumors ( 22 ). Previous studies have shown that inflammation may be one of the causes of prostate cancer. There may be a certain relationship between prostatitis and the occurrence and development of prostate cancer ( 23 ). Among numerous inflammatory cells, lymphocytes play an important role in tumor immune surveillance ( 24 ). They can inhibit tumor proliferation and metastasis by promoting cytotoxic cell death and the production of cytokines ( 7 ). The reduction of lymphocytes will lead to immune responses. Studies have shown that lymphopenia is an independent prognostic factor for overall and progression-free survival in cancer patients ( 25 – 27 ). There is also a close relationship between the nutritional status and prognosis of cancer patients ( 28 ). Malignant tumor itself is a chronic wasting disease, especially in advanced patients, who often develop cachexia. Malnutrition can also inhibit the function of the immune system to a certain extent, leading to the recurrence and progression of tumors ( 29 ). In clinical practice, we usually use serum albumin concentration to evaluate the nutritional status of patients. Low preoperative albumin may affect the enzyme production ability and self-repair ability of tissues and organs, which may lead to a poor prognosis ( 30 ). Liu et al. ( 31 ) conducted a meta-analysis on 23 studies and found that low preoperative serum albumin levels were associated with poor prognosis of urothelial cancer. PNI on the prognosis of different tumors has been analyzed in some previous studies. Xu et al. ( 12 ) conducted a prognostic analysis on 508 patients after radical breast cancer resection. They found that higher PNI was associated with better disease-free survival (DFS). Kubota et al. ( 11 ) analyzed the prognosis of 183 cases of oral cancer and found that higher pre-treatment PNI was associated with better OS, while lower pre-treatment PNI and higher treatment SII were associated with worse DFS. The correlation between many urinary tumors and PNI has also been gradually discovered. Kim et al. ( 32 ) showed that the OS and cancer-specific survival rate of renal cell carcinoma patients in the low PNI group were relatively poor. KARSIYAKALI et al. ( 33 ) found that PNI can be used to predict tumor stage in patients with primary bladder cancer, and lower PNI level is associated with higher stage disease. In a 2021 study, Li et al. ( 15 ) have evaluated the impact of PNI on BCR in patients with RARP. They used a cutoff value of 46.03 to divide 136 patients into a high PNI group and a low PNI group, and Cox proportional hazard analysis confirmed that PNI is an independent prognostic factor for predicting BCR in patients with RARP. In this study, the optimal cutoff value of PNI was 47.425, then patients were divided into high PNI group and low PNI group. Data analysis confirmed that PNI is an independent prognostic factor for predicting BCR in patients with RARP. Patients with a low level of PNI have a higher rate of BCR after RARP, which is similar to the conclusion of previous studies. This also shows that there is a correlation between the patients’ preoperative nutrition and inflammatory immune status and prognosis. We can take certain intervention measures during the perioperative period to increase the patients’ albumin and lymphocyte levels, thereby improving the treatment effect and long-term prognosis. In addition, we also found that PSA is an independent prognostic factor in predicting BCR in patients with RARP, and PSA levels are positively correlated with the rate of BCR. Thereafter, we established a BCR prediction score model based on PNI and PSA. And its predictive value was confirmed in both the training group and the validation group. In addition, we also constructed a nomogram to predict the probability of BCR in patients with RARP at 6-, 12-, and 24-months after surgery. According to the results of the calibration plots, nomogram performed well in predicting the probability of BCR in 6-, 12-, and 24-months in both the training group and the validation group. So, we can use this model to predict the BCR risk of patients, consequently identifying high-risk patients as early as possible and helping them optimize treatment plans to obtain better survival results. 5. Conclusion This study shows that PNI is an independent predictor of BCR after surgery in patients with RARP, and lower PNI is associated with BCR. The risk score model and nomogram established based on PNI and PSA can effectively predict the risk of biochemical recurrence of prostate cancer patients after RARP. Therefore, in clinical practice, we can apply this scoring model to identify high-risk patients who may experience BCR, so as to reduce the postoperative BCR rate of patients with RARP. Abbreviations PNI Prognostic Nutritional Index BCR Biochemical Recurrence RARP Robot-Assisted Laparoscopic Radical Prostatectomy ROC Receiver Operating Characteristic PSA Prostate-Specific Antigen RP Radical Prostatectomy NLR Neutrophil-Lymphocyte Ratio PLR Platelet–Lymphocyte Ratio SII Systemic Immune-Inflammation Index AGR Albumin-Globulin Ratio CT Computed Tomography MRI Magnetic Resonance Imaging BMI Body Mass Index f PSA Free Prostate-Specific Antigen f/t Free Prostate-Specific Antigen /Total Prostate-Specific Antigen IQRs Interquartile Ranges HR Hazard Ratio Declarations Author contributions All authors contributed to the study of conception and design. Xiaojun Zhao coordinated and managed all parts of the study. Yifan Zhao carried out the literature search. All authors conducted data collection and performed preliminary data preparations. Yifan Zhao conducted data analyses and contributed to the interpretation of data. Yifan Zhao wrote the draft of the paper and all authors provided substantive feedback on the paper and contributed to the final manuscript. All authors read and approved the final manuscript. Funding None applicable. Data availability All data is available from corresponding author on reasonable request. Ethics approval and consent to participate The experimental protocol was developed in accordance with the ethical guidelines of the Declaration of Helsinki and was approved by the Human Ethics Committee of the First Affiliated Hospital of Soochow University, the name of the institutional Ethics committee. Written informed consent was obtained from individual or guardian participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Cookson MS, Aus G, Burnett AL, Canby-Hagino ED, D'Amico AV, Dmochowski RR, et al. Variation in the definition of biochemical recurrence in patients treated for localized prostate cancer: the American Urological Association Prostate Guidelines for Localized Prostate Cancer Update Panel report and recommendations for a standard in the reporting of surgical outcomes. J Urol. 2007;177(2):540–5. Wong MC, Goggins WB, Wang HH, Fung FD, Leung C, Wong SY, et al. Global Incidence and Mortality for Prostate Cancer: Analysis of Temporal Patterns and Trends in 36 Countries. Eur Urol. 2016;70(5):862–74. Bill-Axelson A, Holmberg L, Garmo H, Taari K, Busch C, Nordling S, et al. Radical Prostatectomy or Watchful Waiting in Prostate Cancer – 29-Year Follow-up. N Engl J Med. 2018;379(24):2319–29. Van den Broeck T, van den Bergh RCN, Arfi N, Gross T, Moris L, Briers E, et al. Prognostic Value of Biochemical Recurrence Following Treatment with Curative Intent for Prostate Cancer: A Systematic Review. Eur Urol. 2019;75(6):967–87. Murata M. Inflammation and cancer. Environ Health Prev Med. 2018;23(1):50. Kummar S, Kinders R, Rubinstein L, Parchment RE, Murgo AJ, Collins J, et al. Compressing drug development timelines in oncology using phase '0' trials. Nat Rev Cancer. 2007;7(2):131–9. Nakai Y, Nonomura N. Inflammation and prostate carcinogenesis. Int J Urol. 2013;20(2):150–60. Thibodeau J, Bourgeois-Daigneault MC, Lapointe R. Targeting the MHC Class II antigen presentation pathway in cancer immunotherapy. Oncoimmunology. 2012;1(6):908–16. Mehrazin R, Uzzo RG, Kutikov A, Ruth K, Tomaszewski JJ, Dulaimi E, et al. Lymphopenia is an independent predictor of inferior outcome in papillary renal cell carcinoma. Urol Oncol. 2015;33(9):388e19–25. Siddiqui M, Ristow K, Markovic SN, Witzig TE, Habermann TM, Colgan JP, et al. Absolute lymphocyte count predicts overall survival in follicular lymphomas. Br J Haematol. 2006;134(6):596–601. Iseki Y, Shibutani M, Maeda K, Nagahara H, Tamura T, Ohira G, et al. The impact of the preoperative peripheral lymphocyte count and lymphocyte percentage in patients with colorectal cancer. Surg Today. 2017;47(6):743–54. Viani K, Trehan A, Manzoli B, Schoeman J. Assessment of nutritional status in children with cancer: A narrative review. Pediatr Blood Cancer. 2020;67(Suppl 3):e28211. Alwarawrah Y, Kiernan K, MacIver NJ. Changes in Nutritional Status Impact Immune Cell Metabolism and Function. Front Immunol. 2018;9:1055. Panotopoulos J, Posch F, Funovics PT, Willegger M, Scharrer A, Lamm W, et al. Elevated serum creatinine and low albumin are associated with poor outcomes in patients with liposarcoma. J Orthop Res. 2016;34(3):533–8. Liu J, Wang F, Li S, Huang W, Jia Y, Wei C. The prognostic significance of preoperative serum albumin in urothelial carcinoma: a systematic review and meta-analysis. Biosci Rep. 2018;38(4):BSR20180214. Kim SI, Kim SJ, Kim SJ, Cho DS. Prognostic nutritional index and prognosis in renal cell carcinoma: A systematic review and meta-analysis. Urol Oncol. 2021;39(10):623–30. Karsiyakali N, Karabay E, Yucetas U. Predictive value of prognostic nutritional index on tumor stage in patients with primary bladder cancer. Arch Esp Urol. 2020;73(2):132–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3872940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267852308,"identity":"73cc335b-f651-43b5-82de-35d14a2af4ac","order_by":0,"name":"Yifan Zhao","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Zhao","suffix":""},{"id":267852309,"identity":"6cfa3d1f-e4ea-4ef8-bed2-e43f590bb36c","order_by":1,"name":"Shian Qian","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Shian","middleName":"","lastName":"Qian","suffix":""},{"id":267852310,"identity":"bd609681-cc11-4631-93f4-59da9d3ca62a","order_by":2,"name":"Xianchuang Li","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xianchuang","middleName":"","lastName":"Li","suffix":""},{"id":267852311,"identity":"cd86a2be-b226-4519-b7de-2afb6740d862","order_by":3,"name":"Hengxi Jin","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Hengxi","middleName":"","lastName":"Jin","suffix":""},{"id":267852312,"identity":"93275263-3e50-4688-91bd-b207693c500b","order_by":4,"name":"Xiaojun Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie2PsWrDMBRFbR44i4pWCfIRDwoNhlD/ikwgU7ZCyShw0ZQPSD+jS8j4jIYsJl4D6ZAQyNTBXkrHyt06RM4YiA7c4cE9XF4UBQK3CLgIF3QhNR8zzvX1SkyHajqUS7pyzSlQHt/sGLXqaW7geEjNZzYaWKJc1wwjipt2dlmRRfKI0pwhXUwV5es9G4EG+b66rHCInoQ0NkGaIeXVnqWaEnjwKAkMvjuFYf3lFLNlSMqvcGB/KwJ33YqhfkUW7AXF1iLuzopUNWFyWRbeX7DefJzEq82wntj2Z/6ccV6UTetROkD8v2Pt73eVprcSCAQCd80vL6hVUeWbPUAAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-01-17 12:45:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49990793,"identity":"e72f8e6d-7522-4744-a705-d184f8577322","added_by":"auto","created_at":"2024-01-22 18:43:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17886,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve for the prognostic nutritional index depending on biochemical recurrence‐free survival.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3872940/v1/79140a3ee0b825bc3620497d.png"},{"id":49990559,"identity":"26a3c893-00a0-4ed9-bc63-6f0c16564400","added_by":"auto","created_at":"2024-01-22 18:35:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21613,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves and log‐rank test (p\u0026lt; 0.001) showing BCR free survival according to the pretherapeutic optimal value of the PNI in 157 RARP patients.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3872940/v1/f3ac67576f6fc486f7c1201f.png"},{"id":49990561,"identity":"e7a785d7-6fcb-4df2-857b-5f71175640e5","added_by":"auto","created_at":"2024-01-22 18:35:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34968,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve for the risk score depending on biochemical recurrence‐free survival. (\u003cstrong\u003eA\u003c/strong\u003e) Training group; (\u003cstrong\u003eB\u003c/strong\u003e) Validation group.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3872940/v1/1c6b77e7017d13d6bffbe483.png"},{"id":49990562,"identity":"461c36fe-45cd-4a4c-8530-e546a09c21f7","added_by":"auto","created_at":"2024-01-22 18:35:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138467,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting 6-, 12-, and 24-months BCR probability for RARP patients in the training group based on PNI and PSA.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3872940/v1/65f840cbb83b3642f43c206e.png"},{"id":49990563,"identity":"2e12c65d-b02b-467a-a363-c6936d5f721d","added_by":"auto","created_at":"2024-01-22 18:35:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46132,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curves of nomograms between predicted and observed 6-, 12- and 24-months BCR in the training group. The dashed line of 45° represents the perfect prediction of the nomogram.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3872940/v1/26e3d853b69945da8c94c4c3.png"},{"id":54571746,"identity":"eac48fc5-8264-4699-af62-14f78555d9d7","added_by":"auto","created_at":"2024-04-12 12:52:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872940/v1/37bb784e-8ae0-4631-bc6a-eb2018542b5f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProstate cancer is the most common malignant tumor in the male genitourinary system, and its incidence rate second only to lung cancer among all malignant tumors in men (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In recent years, with the PSA screening of high-risk population, the detection rate of prostate cancer has increased significantly, which also provides great help for the early diagnosis and treatment of prostate cancer patients. Radical prostatectomy (RP) is considered to be one of the most effective methods for the treatment of localized and locally advanced prostate cancer, but there are still 20%-40% of patients who develop BCR after RP (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Although some clinical and pathological factors, such as clinical case stage, surgical margin status and Gleason score, have been proved to be independent prognostic factors for predicting postoperative BCR of prostate cancer, they have a certain degree of invasive risk and limitations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, there is an urgent need to find new accurate and non-invasive prognostic indicators to predict the risk of BCR early, so as to provide the best treatment plan for patients.\u003c/p\u003e \u003cp\u003eThe nutritional and inflammatory immune status of cancer patients have been considered to be important factors affecting the prognosis, and play an important role in the process of cancer progression (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Systemic inflammation has been widely recognized as a key factor in cancer invasion, proliferation and metastasis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Previous studies have reported that some systemic inflammation indicators, such as neutrophil-lymphocyte ratio (NLR), platelet\u0026ndash;lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) have been used to predict the prognosis of cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition, the deterioration of nutritional status has been a common problem in cancer patients. PNI happens to be a coefficient that combines nutritional and inflammatory immune indicators. By calculating serum albumin concentration and peripheral blood lymphocyte count, it can simply assess the condition of a patient. PNI was originally used to evaluate preoperative nutritional status and surgical complications in patients with gastrointestinal cancers. It has been proven to be a good prognostic indicator for oral cancer, breast cancer, pancreatic cancer, etc. (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, robot-assisted surgery has made significant progress in various surgical fields, because it has the advantages of 3D surgical field of view, greater magnification effect, freer range of motion, more stable and more precise mechanical operation, which makes up for some of the technical defects of traditional laparoscopy, thus it can reduce the occurrence of intraoperative and postoperative complications (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). And it is more conducive to the recovery of patients. However, some prostate cancer patients treated with RARP are still unable to avoid BCR, so there is an urgent need to find an easily accessible and low-cost biomarker to predict it. To our knowledge, previous studies have evaluated PNI as a predictor of prognosis in patients with RARP (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In this study, we will further confirm their correlation. We also included other commonly used inflammatory nutritional biomarkers, such as NLR, SII and albumin-globulin ratio (AGR). Finally, we combined the clinicopathological characteristics of the patients to screen out the independent prognostic factors for predicting BCR in patients treated with RARP, constructed a prognostic model and verify its effectiveness.\u003c/p\u003e"},{"header":"2. Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe clinical and pathological information of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University from December 2019 to November 2022 were retrospectively analyzed. This study inclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Prostate biopsy was performed before surgery, and the pathological report confirmed prostate cancer; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) According to the relevant clinical examination, computed tomography (CT), magnetic resonance imaging (MRI) and bone scan examination, in accordance with the international TNM staging of prostate cancer, have a clear clinical staging; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) There were no obvious preoperative complications; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Preoperative 1 week with serological samples available; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Received robot-assisted radical resection of prostate cancer in the First Affiliated Hospital of Soochow University, and was discharged successfully without significant complications after surgery; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Postoperative pathology confirmed prostate cancer, and Gleason score was used. Exclusion criteria:(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Lack of relevant clinical data and complete follow-up information; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Suffering from diseases affecting systemic nutritional and immune indexes; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Combined with other primary tumor diseases; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) preoperative neoadjuvant therapy; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Endocrine therapy was given immediately without follow-up. The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University. All patients gave informed written consent before they were enrolled in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and follow-up\u003c/h2\u003e \u003cp\u003eRelevant clinical data of patients were collected, including age, body mass index (BMI), preoperative total PSA, free PSA (f PSA), free PSA/total PSA (f/t), clinical stage, and postoperative pathological results, including tumor nature, Gleason score, incisal margin, lymph node status, nerve invasion, seminal vesicle invasion, and vas deferens invasion. The clinical staging was performed according to the American Joint Committee on Cancer 7th edition tumor, node, and metastasis staging system (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Preoperative complete blood count and related biochemical indexes were obtained by fasting blood samples collected in the morning within 1 week before surgery. According to the established formula to calculate the research related to nutrition and immune scores: PNI\u0026thinsp;=\u0026thinsp;serum albumin (g/L)\u0026thinsp;+\u0026thinsp;5 *lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e /L); NLR\u0026thinsp;=\u0026thinsp;neutrophil count (10\u003csup\u003e9\u003c/sup\u003e /L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e /L); AGR\u0026thinsp;=\u0026thinsp;serum albumin (g/L)/ serum globulin (g/L); SII\u0026thinsp;=\u0026thinsp;platelet count (10\u003csup\u003e9\u003c/sup\u003e /L) *neutrophil count (10\u003csup\u003e9\u003c/sup\u003e /L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e /L).\u003c/p\u003e \u003cp\u003eWe obtained the required follow-up information by inquiring the patients' postoperative outpatient review data and telephone interview. Patients are usually required to retest PSA 6\u0026ndash;8 weeks after surgery, every 3 months in the first and second years, and every 6 months thereafter. We defined BCR by a consecutive increase of PSA\u0026thinsp;\u0026ge;\u0026thinsp;0.2 ng/ml on two blood tests. The follow-up time was defined as the period from the time of RARP to BCR (1 decimal place reserved) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analyses were conducted using SPSS version 27 (SPSS Inc.) and R software (3.5.2). Medians with interquartile ranges (IQRs) and frequencies were adopted to report continuous and categorical variables, respectively. The ROC curve analysis is performed to select the most suitable demarcation point for the PNI. Differences in continuous variables were analyzed using the Mann\u0026ndash;Whitney U-test and differences in categorical data were analyzed using the x \u003csup\u003e2\u003c/sup\u003e test. Kaplan\u0026ndash;Meier survival analysis and log‐rank test were used to compare the effects of different PNI groups on the BCR rate of patients receiving RARP. Univariate and multivariate cox regression analyses were used to assess the influence of prognostic factors and to establish a risk prediction model. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic information\u003c/h2\u003e \u003cp\u003eA total of 157 patients with RARP were included in this study, and detailed clinicopathological characteristics of all patients were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 71 years, median BMI was 23.875 kg/m\u003csup\u003e2\u003c/sup\u003e, median PSA was 12.538ng/ml and median f/t value was 0.104. The median values of PNI, AGR, NLR and SII were 48.70, 1.7, 2.41 and 482.14. In addition, 71 patients (45.2%) had positive surgical margins, 109 patients (69.4%) had nerve invasion, 30 patients (19.1%) had seminal vesicle invasion and 16 patients (10.2%) had vas deferens invasion. The clinical staging between T3 and T4 was 72.0%. Lymph node metastasis occurred in 9 patients (5.7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the 157 participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal n\u0026thinsp;=\u0026thinsp;157\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5(inclusive)-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24 (inclusive)-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical staging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;47.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;47.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGleason score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMargin status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNerve invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeminal vesicle invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVas deferens invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years) median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e71(66\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (kg/m2) median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e23.875(22.039\u0026ndash;25.374)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSA\u003c/b\u003e (ng/ml) median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12.538(7.961\u0026ndash;23.500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ef/t\u003c/b\u003e median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.104(0.076\u0026ndash;0.149)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGR\u003c/b\u003e median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.7(1.5\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e48.70(45.25\u0026ndash;50.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.41(1.73\u0026ndash;3.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII\u003c/b\u003e median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e482.14 (321.39-713.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of clinicopathological data between BCR group and non-BCR group\u003c/h2\u003e \u003cp\u003eThe correlation between BCR and clinical characteristics was confirmed by the x\u003csup\u003e2\u003c/sup\u003e test and Mann\u0026ndash;Whitney U test. The clinical data of patients in different BCR groups are shown in the Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results showed that the BCR group had higher clinical stage (P\u0026thinsp;=\u0026thinsp;0.006), higher PSA value (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher Gleason score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher NLR score (P\u0026thinsp;=\u0026thinsp;0.008), higher margin positive rate (P\u0026thinsp;=\u0026thinsp;0.002), higher lymph node positive rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher invasion rate of nerve, seminal vesicle and vas deferens (P\u0026thinsp;=\u0026thinsp;0.015, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but lower PNI value (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with the non-BCR group. There was no significant difference in age, BMI, f/t, AGR and SII scores in different groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of different BCR group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCR group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-BCR group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of cases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(67,76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(66,74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.424(21.671,25.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.577(22.039,25.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSA (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.820(15.048,46.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.696(6.598,15.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ef/t median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.091(0.064,0.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113(0.078,0.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.85(43.25,48.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.53(47.98,51.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGR median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7(1.4,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7(1.5,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65(2.08,3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29(1.58,3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528.27(358.22,751.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458.42(291.92,671.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical staging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGleason score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMargin status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNerve invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeminal vesicle invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVas deferens invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Optimal PNI cutoff value before treatment\u003c/h2\u003e \u003cp\u003e157 patients ranged from 1.7 to 47.0 months, with a median follow-up time of 15.2 months. Among them, 51 cases experienced BCR. ROC curves were drawn based on whether BCR occurred at the end of follow-up. It was found that when PNI\u0026thinsp;=\u0026thinsp;47.425, the AUC was 0.799 (95% confidence interval: 0.726\u0026ndash;0.873, Youden index\u0026thinsp;=\u0026thinsp;0.532, sensitivity\u0026thinsp;=\u0026thinsp;70.0%, specificity\u0026thinsp;=\u0026thinsp;83.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, PNI\u0026thinsp;=\u0026thinsp;47.425 was determined as the optimal cutoff value. Among 157 patients, 53 patients (33.8%) were in the low PNI group (PNI\u0026thinsp;\u0026le;\u0026thinsp;47.425), and 104 patients (66.2%) in the high PNI group (PNI\u0026thinsp;\u0026gt;\u0026thinsp;47.425).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Clinicopathological features in different PNI groups\u003c/h2\u003e \u003cp\u003eThe correlation between PNI values and clinical characteristics was confirmed by x\u003csup\u003e2\u003c/sup\u003e test and Mann-Whitney U test. The clinical data of patients in different PNI groups are shown in the Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results showed that compared with the high PNI group, the low PNI group had an older age (P\u0026thinsp;=\u0026thinsp;0.004), a higher Gleason score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a higher BCR rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher seminal vesicle and vas deferens invasion rate (P\u0026thinsp;=\u0026thinsp;0.036, P\u0026thinsp;=\u0026thinsp;0.002), higher PSA value (P\u0026thinsp;=\u0026thinsp;0.022), higher NLR score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher SII score (P\u0026thinsp;=\u0026thinsp;0.022). There was no statistical significance between different PNI groups in terms of BMI, clinical stage, f/t, positive resection margin rate, nerve invasion rate, lymph node positive rate and AGR score. Kaplan-Meier analysis of all patients showed that patients in the low PNI group had a lower BCR-free survival rate compared with the high PNI group. The log-rank test results showed that the difference between the two groups was statistically significant (x\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;21.292, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of different PNI group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow PNI group (PNI\u0026thinsp;\u0026le;\u0026thinsp;47.425)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh PNI group (PNI\u0026gt;47.425)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of cases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(69,76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(66,73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.665(21.593,25.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.875(22.111,25.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSA (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.157(9.400,26.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.303(7.053,21.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ef/t median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095(0.072,0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108(0.076,0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGR median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6(1.4,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7(1.5,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95(2.16,4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27(1.57,2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579.55(355.25,980.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458.42(294.75,656.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical staging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGleason score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMargin status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNerve invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeminal vesicle invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVas deferens invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Establishment and verification of the BCR risk score model\u003c/h2\u003e \u003cp\u003e157 patients were divided into training group and validation group according to the ratio of 7:3. The clinical data of patients in different groups are shown in the Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Among them, there were 110 patients in the training group and 47 patients in the validation group. There was no statistical difference in each variable between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). As shown in the Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, according to univariate Cox regression analysis, in the training group, clinical stage (P\u0026thinsp;=\u0026thinsp;0.038 )、PSA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), Gleason score (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PNI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NLR (P\u0026thinsp;=\u0026thinsp;0.012), positive resection margin (P\u0026thinsp;=\u0026thinsp;0.011), positive lymph node (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), nerve invasion (P\u0026thinsp;=\u0026thinsp;0.043), seminal vesicle invasion (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ) and vas deferens invasion (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ) had a significant correlation with BCR. The above factors were included in multiple Cox regression analysis to further determine independent predictors. It was determined that PSA (P\u0026thinsp;=\u0026thinsp;0.014) and PNI (P\u0026thinsp;=\u0026thinsp;0.002) were independent predictors of BCR in patients with RARP. Then a BCR prediction model formula was established and the risk score was calculated (risk score\u0026thinsp;=\u0026thinsp;0.021* PSA-0.170* PNI). The AUC values of this model in the training group and validation group were 0.870 (95% confidence interval: 0.804\u0026ndash;0.937, sensitivity\u0026thinsp;=\u0026thinsp;82.4%, specificity\u0026thinsp;=\u0026thinsp;80.3%) and 0.898 (95% confidence interval: 0.800-0.996, sensitivity\u0026thinsp;=\u0026thinsp;2.4%, specificity\u0026thinsp;=\u0026thinsp;86.7%) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the above results of Cox regression analysis, we combined the two independent predictive factors of PSA and PNI to construct a nomogram to predict the probability of BCR in patients with RARP at 6-, 12-, and 24-months after surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results showed that for every 2 points decreases in PNI, the nomogram score increased by 9 points; for every 10 ng/ml increase in PSA, the nomogram score increased by 5 points. And the corresponding BCR probability of patients increased at 6-, 12-, and 24-months after surgery. Based on calibration plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the predicted 6-, 12-, and 24-months BCR probabilities of the nomogram performed well in both the training and validation group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of training group and validation group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of cases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(67,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(64,73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.559(21.664,25.359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.167(22.857,25.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSA (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.476(7.927,23.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.060(8.000,24.820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ef/t (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.099(0.075,0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116(0.078,0.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.90(45.28,50.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.90(44.85,51.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGR (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7(1.5,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7(1.5,1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40(1.78,3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41(1.73,3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII (ng/ml) median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469.00(294.33,682.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520.97(358.22,770.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical staging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGleason score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMargin status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNerve invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeminal vesicle invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVas deferens invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of factors associated with BCR in RARP patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis HR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate analysis HR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.032(0.976\u0026ndash;1.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.049(0.920\u0026ndash;1.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSA (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.024(1.014\u0026ndash;1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.021(1.004\u0026ndash;1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ef/t (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113(0.001\u0026ndash;11.317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePNI (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.817(0.757\u0026ndash;0.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869(0.794\u0026ndash;0.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGR (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.519(0.147\u0026ndash;1.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.253(1.051\u0026ndash;1.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.133(0.914\u0026ndash;1.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000(1.000-1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical staging (T1\u0026ndash;T2/T3\u0026ndash;T4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.331(0.117\u0026ndash;0.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.182(0.091\u0026ndash;15.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGleason score (\u0026le;\u0026thinsp;7/\u0026gt;7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.212(0.107\u0026ndash;0.419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.773(0.222\u0026ndash;2.690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMargin status (negative/positive)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.406(0.203\u0026ndash;0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.135(0.439\u0026ndash;2.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNerve invasion (negative/positive)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.375(0.145\u0026ndash;0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.190(0.209\u0026ndash;22.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeminal vesicle invasion (negative/positive)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.265(0.132\u0026ndash;0.530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.604(0.190\u0026ndash;1.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVas deferens invasion (negative/positive)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.236(0.106\u0026ndash;0.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.822(0.232\u0026ndash;2.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph nodes (negative/positive)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.191(0.073\u0026ndash;0.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.921(0.282\u0026ndash;3.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn recent years, the incidence of prostate cancer has shown a significant upward trend worldwide (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Many countries and regions have included PSA screening in routine physical examinations for older men. This will help improve the detection rate of prostate cancer, diagnose and treat of early prostate cancer, especially prostate cancer of clinical significance. Although with the development of RP technology, prostate cancer patients can obtain good tumor control and achieve radical results, there are still some patients who will inevitably experience BCR, or even fail to take appropriate measures in time, leading to the progression of the disease (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Therefore, how to accurately predict which patients will have BCR has become a major problem for clinicians. The purpose of this study was to examine the predictive value of the PNI for BCR in patients undergoing RARP. PNI combines serum albumin concentration and lymphocyte count. It is a simple and effective objective data assessment system that can objectively reflect the patients\u0026rsquo; preoperative nutritional and inflammatory immune status. At the same time, it overcomes the invasiveness risk caused by previous predictive factors, which can be measured with a simple blood test.\u003c/p\u003e \u003cp\u003eMany researchers currently believe that inflammation has a certain relationship with the recurrence and metastasis of tumors (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Because the uncontrolled persistence of inflammatory responses may lead to severe cell and genome damage, resulting in wanton cell proliferation and genome instability, and increasing the risk of malignant tumors (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Previous studies have shown that inflammation may be one of the causes of prostate cancer. There may be a certain relationship between prostatitis and the occurrence and development of prostate cancer (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Among numerous inflammatory cells, lymphocytes play an important role in tumor immune surveillance (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). They can inhibit tumor proliferation and metastasis by promoting cytotoxic cell death and the production of cytokines (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The reduction of lymphocytes will lead to immune responses. Studies have shown that lymphopenia is an independent prognostic factor for overall and progression-free survival in cancer patients (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is also a close relationship between the nutritional status and prognosis of cancer patients (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Malignant tumor itself is a chronic wasting disease, especially in advanced patients, who often develop cachexia. Malnutrition can also inhibit the function of the immune system to a certain extent, leading to the recurrence and progression of tumors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In clinical practice, we usually use serum albumin concentration to evaluate the nutritional status of patients. Low preoperative albumin may affect the enzyme production ability and self-repair ability of tissues and organs, which may lead to a poor prognosis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Liu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) conducted a meta-analysis on 23 studies and found that low preoperative serum albumin levels were associated with poor prognosis of urothelial cancer.\u003c/p\u003e \u003cp\u003ePNI on the prognosis of different tumors has been analyzed in some previous studies. Xu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) conducted a prognostic analysis on 508 patients after radical breast cancer resection. They found that higher PNI was associated with better disease-free survival (DFS). Kubota et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) analyzed the prognosis of 183 cases of oral cancer and found that higher pre-treatment PNI was associated with better OS, while lower pre-treatment PNI and higher treatment SII were associated with worse DFS. The correlation between many urinary tumors and PNI has also been gradually discovered. Kim et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) showed that the OS and cancer-specific survival rate of renal cell carcinoma patients in the low PNI group were relatively poor. KARSIYAKALI et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) found that PNI can be used to predict tumor stage in patients with primary bladder cancer, and lower PNI level is associated with higher stage disease. In a 2021 study, Li et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) have evaluated the impact of PNI on BCR in patients with RARP. They used a cutoff value of 46.03 to divide 136 patients into a high PNI group and a low PNI group, and Cox proportional hazard analysis confirmed that PNI is an independent prognostic factor for predicting BCR in patients with RARP.\u003c/p\u003e \u003cp\u003eIn this study, the optimal cutoff value of PNI was 47.425, then patients were divided into high PNI group and low PNI group. Data analysis confirmed that PNI is an independent prognostic factor for predicting BCR in patients with RARP. Patients with a low level of PNI have a higher rate of BCR after RARP, which is similar to the conclusion of previous studies. This also shows that there is a correlation between the patients\u0026rsquo; preoperative nutrition and inflammatory immune status and prognosis. We can take certain intervention measures during the perioperative period to increase the patients\u0026rsquo; albumin and lymphocyte levels, thereby improving the treatment effect and long-term prognosis. In addition, we also found that PSA is an independent prognostic factor in predicting BCR in patients with RARP, and PSA levels are positively correlated with the rate of BCR. Thereafter, we established a BCR prediction score model based on PNI and PSA. And its predictive value was confirmed in both the training group and the validation group. In addition, we also constructed a nomogram to predict the probability of BCR in patients with RARP at 6-, 12-, and 24-months after surgery. According to the results of the calibration plots, nomogram performed well in predicting the probability of BCR in 6-, 12-, and 24-months in both the training group and the validation group. So, we can use this model to predict the BCR risk of patients, consequently identifying high-risk patients as early as possible and helping them optimize treatment plans to obtain better survival results.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study shows that PNI is an independent predictor of BCR after surgery in patients with RARP, and lower PNI is associated with BCR. The risk score model and nomogram established based on PNI and PSA can effectively predict the risk of biochemical recurrence of prostate cancer patients after RARP. Therefore, in clinical practice, we can apply this scoring model to identify high-risk patients who may experience BCR, so as to reduce the postoperative BCR rate of patients with RARP.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePNI Prognostic Nutritional Index\u003c/p\u003e\n\u003cp\u003eBCR Biochemical Recurrence\u003c/p\u003e\n\u003cp\u003eRARP Robot-Assisted Laparoscopic Radical Prostatectomy\u003c/p\u003e\n\u003cp\u003eROC Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003ePSA Prostate-Specific Antigen\u003c/p\u003e\n\u003cp\u003eRP Radical Prostatectomy\u003c/p\u003e\n\u003cp\u003eNLR Neutrophil-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003ePLR Platelet\u0026ndash;Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eSII Systemic Immune-Inflammation Index\u003c/p\u003e\n\u003cp\u003eAGR Albumin-Globulin Ratio\u003c/p\u003e\n\u003cp\u003eCT Computed Tomography\u003c/p\u003e\n\u003cp\u003eMRI Magnetic Resonance Imaging\u003c/p\u003e\n\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\n\u003cp\u003ef PSA Free Prostate-Specific Antigen\u003c/p\u003e\n\u003cp\u003ef/t Free Prostate-Specific Antigen /Total Prostate-Specific Antigen\u003c/p\u003e\n\u003cp\u003eIQRs Interquartile Ranges\u003c/p\u003e\n\u003cp\u003eHR Hazard Ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study of conception and design. Xiaojun Zhao coordinated and managed all parts of the study. Yifan Zhao carried out the literature search. All authors conducted data collection and performed preliminary data preparations. Yifan Zhao conducted data analyses and contributed to the interpretation of data. Yifan Zhao wrote the draft of the paper and all authors provided substantive feedback on the paper and contributed to the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data is available from corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was developed in accordance with the ethical guidelines of the Declaration of Helsinki and was approved by the Human Ethics Committee of the First Affiliated Hospital of Soochow University, the name of the institutional Ethics committee. Written informed consent was obtained from individual or guardian participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostello AJ. Considering the role of radical prostatectomy in 21st century prostate cancer care. 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Arch Esp Urol. 2020;73(2):132\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"prostate cancer, biochemical recurrence, prognostic factors, prognostic nutritional index, robot-assisted laparoscopic radical prostatectomy, risk model, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-3872940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3872940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the predictive value of pre-operative prognostic nutritional index (PNI) in biochemical recurrence (BCR) in patients with robot-assistedlaparoscopic radical prostatectomy (RARP) and to establish a BCR risk score model based on PNI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe clinical data of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University were retrospectively analyzed. The endpoint of observation was BCR. The area under the receiver operating characteristic (ROC) curve was evaluated to determine the optimal cutoff value for PNI. Kaplan-Meier analysis and Cox regression analysis were used to evaluate the correlation between PNI and BCR. 157 patients were divided into a training group and a validation group by a ratio of 7:3. By univariate and multivariate Cox regression analysis, independent prognostic factors were screened from the relevant clinicopathological factors, a BCR prediction model and nomogramwere established, then verified its value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAccording to the ROC curve, the optimal cutoff value of PNI for 157 patients in this study was 47.425. According to multivariate Cox regression analysis, PNI and prostate-specific antigen (PSA) were identified as independent prognostic factors for predicting BCR in patients treated with RARP. A BCR prediction model formula was established based on PNI and PSA. It was proved to have good predictive value in both the training group and the validation group. Nomogram was constructed to predict the BCR of patients treated with RARP at 6-, 12-, and 24-months after surgery. The results of the calibration plots showed that the nomogram performed well in the training group and the validation group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003ePNI is an independent prognostic factor for predicting BCR in patients treated with RARP. The scoring model and nomogram based on PNI and PSA can effectively predict the risk of BCR in patients treated with RARP.\u003c/p\u003e","manuscriptTitle":"Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 18:35:09","doi":"10.21203/rs.3.rs-3872940/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8893b0e1-e94c-40e6-b3ef-71744845383a","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-12T12:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 18:35:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3872940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3872940","identity":"rs-3872940","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}