A hematological and inflammatory marker-based model for prostate carcinoma diagnosis.

Prostate carcinoma (PC) is the most frequently diagnosed malignancy and the third leading cause of cancer-related death among men in the United States, with over 160,000 new cases reported annually. While prostate-specific antigen (PSA) screening has advanced the early detection and management of PC, its diagnostic accuracy, particularly in distinguishing malignant from benign conditions, remains controversial. Therefore, this study aimed to improve the accuracy and efficiency of early PC diagnosis by constructing a diagnostic model based on hematological indicators. Emerging inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP) were incorporated to supplement traditional PSA testing. This study employed a retrospective design and included 317 patients receiving prostate puncture at Foshan Fosun Chancheng Hospital of Guangdong Medical University between January 2019 and January 2022 as the research subjects. These patients were grouped into two categories: 126 diagnosed with PC and 191 diagnosed with benign prostatic hyperplasia, based on histopathological examination of the biopsy samples. Clinical and laboratory data were extracted from the electronic medical record system. Diagnostic markers for PC were screened by logistic regression and least absolute shrinkage and selection operator (LASSO) regression. The diagnostic performance of the model was evaluated using ROC and decision curve analysis. PSA, Neu, Mono, CRP, NLR, NAR, and CK-MB were identified as independent diagnostic indicators, effectively distinguishing PC from benign prostatic hyperplasia. The LASSO regression-based predictive model achieved an AUC of 0.850, significantly outperforming the traditional logistic regression model (AUC=0.792; P=0.042, Delong test), indicating superior diagnostic accuracy and model performance. In conclusion, the combination of traditional PSA testing and emerging inflammatory markers can significantly enhances early diagnostic accuracy for PC and the proposed model offers a promising approach for early detection and clinical decision-making.