These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Establishment and validation of serum lipid-based nomogram for predicting the risk of prostate cancer.
    Author: Feng F, Zhong YX, Chen Y, Lin FX, Huang JH, Mai Y, Zhao PP, Wei W, Zhu HC, Xu ZP.
    Journal: BMC Urol; 2023 Jul 14; 23(1):120. PubMed ID: 37452418.
    Abstract:
    BACKGROUND: This study aimed to explore the value of combined serum lipids with clinical symptoms to diagnose prostate cancer (PCa), and to develop and validate a Nomogram and prediction model to better select patients at risk of PCa for prostate biopsy. METHODS: Retrospective analysis of 548 patients who underwent prostate biopsies as a result of high serum prostate-specific antigen (PSA) levels or irregular digital rectal examinations (DRE) was conducted. The enrolled patients were randomly assigned to the training groups (n = 384, 70%) and validation groups (n = 164, 30%). To identify independent variables for PCa, serum lipids (TC, TG, HDL, LDL, apoA-1, and apoB) were taken into account in the multivariable logistic regression analyses of the training group, and established predictive models. After that, we evaluated prediction models with clinical markers using decision curves and the area under the curve (AUC). Based on training group data, a Nomogram was developed to predict PCa. RESULTS: 210 (54.70%) of the patients in the training group were diagnosed with PCa. Multivariate regression analysis showed that total PSA, f/tPSA, PSA density (PSAD), TG, LDL, DRE, and TRUS were independent risk predictors of PCa. A prediction model utilizing a Nomogram was constructed with a cut-off value of 0.502. The training and validation groups achieved area under the curve (AUC) values of 0.846 and 0.814 respectively. According to the decision curve analysis (DCA), the prediction model yielded optimal overall net benefits in both the training and validation groups, which is better than the optimal net benefit of PSA alone. After comparing our developed prediction model with two domestic models and PCPT-RC, we found that our prediction model exhibited significantly superior predictive performance. Furthermore, in comparison with clinical indicators, our Nomogram's ability to predict prostate cancer showed good estimation, suggesting its potential as a reliable tool for prognostication. CONCLUSIONS: The prediction model and Nomogram, which utilize both blood lipid levels and clinical signs, demonstrated improved accuracy in predicting the risk of prostate cancer, and consequently can guide the selection of appropriate diagnostic strategies for each patient in a more personalized manner.
    [Abstract] [Full Text] [Related] [New Search]