AUTHOR=Yang YunKai , Zhang Wei , Wan LiJun , Tang ZhiLing , Zhang Qi , Bai YuChen , Zhang DaHong 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1074478

DOI=10.3389/fonc.2022.1074478

ISSN=2234-943X

ABSTRACT=Objective: Intraductal carcinoma of the prostate (IDC-P) is a special pathological type of prostate cancer that is highly aggressive and has a poor prognosis. Herein, an effective predictive model was constructed to predict IDC-P.
Methods: Data of 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 to construct (Ga cohort) and internal validate (Gb cohort) 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 utilized to identify independent predictors. The selected predictors were then used to establish the predictive nomogram. The apparent performance of the model was evaluated via external validation. A decision curve analysis was also performed to assess the clinical utility of our 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 good discriminatory power (AUC = 0.794). Internal validation (AUC = 0.819) and external validation (AUC = 0.903) also displayed a good predictive ability. Calibration curves demonstrated good agreement between the predicted and observed incidence of IDC-P. 
Conclusion: This study proposes 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 with 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.