Construction and validation of a clinical predictive nomogram for intraductal carcinoma of the prostate based on Chinese multicenter clinical data

YunKai Yang; Wei Zhang; LiJun Wan; ZhiLing Tang; Qi Zhang; YuChen Bai; DaHong Zhang

doi:10.3389/fonc.2022.1074478

Construction and validation of a clinical predictive nomogram for intraductal carcinoma of the prostate based on Chinese multicenter clinical data

Front Oncol. 2022 Dec 15:12:1074478. doi: 10.3389/fonc.2022.1074478. eCollection 2022.

Authors

YunKai Yang^{1

2}, Wei Zhang³, LiJun Wan⁴, ZhiLing Tang⁵, Qi Zhang¹, YuChen Bai¹, DaHong Zhang^{1

2}

Affiliations

¹ Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
² The 2nd Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
³ Zhejiang Provincial People's Hospital, Qingdao University, Shandong, Qingdao, China.
⁴ Department of Urology, Quzhou People's Hospital, Quzhou, Zhejiang, China.
⁵ Department of Urology, Jiaxing Second People's Hospital, Jiaxing, Zhejiang, China.

Abstract

Introduction: Intraductal carcinoma of the prostate (IDC-P) is a special pathological type of prostate cancer that is highly aggressive with poor prognostic outcomes.

Objective: To establish an effective predictive model for predicting IDC-P.

Methods: Data for 3185 patients diagnosed with prostate cancer at three medical centers in China from October 2012 to April 2022 were retrospectively analyzed. One cohort (G cohort) consisting of 2384 patients from Zhejiang Provincial People's Hospital was selected for construction (Ga cohort) and internal validate (Gb cohort)of the model. Another cohort (I cohort) with 344 patients from Quzhou People's Hospital and 430 patients from Jiaxing Second People's Hospital was used for external validation. Univariate and multivariate binary logistic regression analyses were performed to identify the independent predictors. Then, the selected predictors were then used to establish the predictive nomogram. The apparent performance of the model was evaluated via externally validated. Decision curve analysis was also performed to assess the clinical utility of the developed model.

Results: Univariate and multivariate logistic regression analyses showed that alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase were independent predictors of IDC-P. Therefore, a predictive nomogram of IDC-P was constructed. The nomogram had a good discriminatory power (AUC = 0.794). Internal validation (AUC = 0.819)and external validation (AUC = 0.903) also revealed a good predictive ability. Calibration curves showed good agreement between the predicted and observed incidences of IDC-P.

Conclusion: We developed a clinical predictive model composed of alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase (LDH) with a high precision and universality. This model provides a novel calculator for predicting the diagnosis of IDC-P and different treatment options for patients at an early stage.

Keywords: IDC-P; PCa; biochemical indicators; obesity; predictive nomogram.