Prediction of pathological upgrading after radical prostatectomy for ISUP grade 1 prostate cancer:construction of a nomogram model based on clinical,imaging,and puncture biopsy
10.16781/j.CN31-2187/R.20250195
- VernacularTitle:ISUP 1级前列腺癌根治术后病理升级预测:基于临床、影像学及穿刺活检数据的列线图模型构建
- Author:
Fang LIU
1
;
Hanchang WU
;
Yun BIAN
;
Chengwei SHAO
Author Information
1. 海军军医大学(第二军医大学)第一附属医院放射诊断科,上海 200433
- Keywords:
prostatic neoplasms;
nomogram;
pathological upgrading;
risk stratification;
International Society of Urological Pathology grading
- From:
Academic Journal of Naval Medical University
2025;46(10):1297-1303
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify risk factors for pathological upgrading after radical prostatectomy in patients with biopsy-confirmed International Society of Urological Pathology(ISUP)grade 1 prostate cancer and to develop a predictive nomogram.Methods A total of 256 patients with ISUP grade 1 prostate cancer diagnosed by biopsy and undergoing radical prostatectomy in The First Affiliated Hospital of Naval Medical University between Jan.2017 and May 2024 were retrospectively enrolled.Clinical,imaging,and biopsy data were collected.Independent predictors were identified using univariate and multivariate binary logistic regression,and a nomogram model was constructed.Model performance was evaluated using receiver operating characteristic curve,clinical impact curve,and decision curve analysis.The stability of the model was evaluated by Hosmer-Lemeshow test.Results Multivariate binary logistic regression analysis revealed that the number of positive puncture cores(odds ratio[OR]=1.80),prostate imaging and reporting data system(PI-RADS)score(OR=1.88),and prostate specific antigen density(PSAD)stage(OR=1.43)were independent predictors of pathological upgrading(all P＜0.01).The area under curve(AUC)value of the nomogram model based on the above 3 predictors was 0.82(95％confidence interval 0.77-0.87).Decision curve analysis demonstrated favourable clinical utility within a threshold probability range of 0.01-0.99.Clinical impact curve analysis showed that at a threshold probability of 0.40,the model could avoid 45 unnecessary interventions(12％reduction in false-positive rate)with a net clinical benefit of 0.46.The Hosmer-Lemeshow test indicated good model fit(P=0.45).Conclusion The constructed nomogram model can accurately predict the risk of pathological upgrading after radical prostatectomy in patients with ISUP grade 1 prostate cancer,providing a quantitative tool to support individualized decision-making for active surveillance.
