The value of PI-RADS score combined with SII in predicting pathological upgrading in patients with localized prostate cancer post-radical prostatectomy
Objective To investigate the application value of combining Prostate Imaging Reporting and Data System(PI-RADS v2.1)score and Systemic Immune-Inflammation Index(SII)in predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy(RP).Methods A retrospective analysis was conducted on clinical data from 76 patients with localized prostate cancer who underwent prostate biopsy and radical prostatectomy at the Second Affiliated Hospital of Zhengzhou University between September 2019 and May 2024.The median age was 68(65,71)years.Total prostate-specific antigen(tPSA)was 17.4(8.4,30.9)ng/ml,and prostate volume was 43.1(29.9,58.9)ml.PI-RADS scores were ≤3 in 22 cases(28.9％)and＞3 in 54 cases(71.1％).According to the International Society of Urological Pathology(ISUP)grading of biopsy specimens,31 patients(40.8％)were classified as Group＜3 and 45 patients(59.2％)as Group ≥3.Postoperatively,25 patients(32.9％)were classified as ISUP Group＜3,and 51 patients(67.1％)as Group ≥3.Pathological upgrading was defined as either:①a higher ISUP grade in postoperative specimens compared to biopsy specimens or;② benign prostate tissue identified in biopsy specimens but confirmed as prostate cancer postoperatively.Clinical data were compared between the pathological upgrade and non-upgrade groups.Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for pathological upgrading and to construct a nomogram model.Receiver operating characteristic(ROC)curves were used to evaluate the predictive performance of individual indicators(PI-RADS,SII,％PSA,and the proportion of tumor tissue in biopsy specimens)and the combined nomogram model.Internal validation was conducted using cross-validation,and calibration and decision curves were generated to assess the nomogram's accuracy and clinical net benefit.Results Among the 76 patients included,10(13.2％)experienced pathological downgrading,36(47.4％)had consistent grading,and 30(39.5％)experienced pathological upgrading.The platelet-to-lymphocyte ratio(PLR)[118.2(93.5,139.1)vs.95.2(79.3,116.4),P=0.021],SII[394.8(331.0,513.6)vs.338.8(217.2,407.8),P=0.002],and the number of cases with a PI-RADS score＞3[26 cases(86.7％)vs.28 cases(60.9％),P=0.015]were significantly higher in the pathological upgrade group than in the non-upgrade group.Conversely,the percentage of positive biopsy cores[35.9％(12.6％,51.8％)vs.43.8％(21.0％,92.1％),P=0.045],the proportion of tumor tissue in biopsy specimens[6.9％(1.3％,20.1％)vs.19.3％(9.1％,58.4％),P＜0.01],and the number of cases in ISUP biopsy Group ≥3[12 cases(40.0％)vs.33 cases(71.7％),P=0.006]were significantly lower in the upgrade group(all P＜0.05).Univariate and multivariate logistic regression analyses showed that PI-RADS score(OR=17.111,95％CI 2.388-122.592,P＜0.01),SII(OR=1.009,95％CI 1.001-1.016,P=0.028),％PSA(OR=0.003,95％CI 0.002-0.004,P＜0.01),and the proportion of tumor tissue in biopsy specimens(OR=0.899,95％CI 0.837-0.966,P＜0.01)were independent predictors of pathological upgrading.The area under the ROC curve(AUC)for PI-RADS,SII,％PSA,and the proportion of tumor tissue in biopsy specimens were 0.607,0.711,0.618,and 0.778,respectively.The combined AUC for％PSA and the proportion of tumor tissue was 0.791,while the combined AUC of the four-indicator nomogram model was 0.914.The DeLong test indicated a statistically significant difference in diagnostic performance between the two models(P＜0.01).Calibration and decision curves demonstrated good accuracy and clinical net benefit for the nomogram model.Conclusions The PI-RADS v2.1 score and SII have significant predictive value for pathological upgrading after radical prostatectomy in prostate cancer.A nomogram model combining PI-RADS,SII,％PSA,and the proportion of tumor tissue in biopsy specimens shows excellent predictive performance.
Prostate neoplasmsProstate imaging reporting and data system(PI-RADS)Systemic immune-inflammation index(SII)Pathological upgradingPredictive model
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