Related Experiment Video
Updated: Apr 7, 2026
Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
Published on: November 17, 2023
[Analysis of influential factors for prostate biopsy and establishment of logistic regression model for prostate
Yonglin Li1, Zhengyan Tang1, Lin Qi1
1Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
Objective:
To establish logistic regression model for prostate cancer and provide basis for prostate biopsy.
Methods:
A total of 117 cases of prostate biopsy were retrospectively analyzed in chronological sequence. All cases were assigned into a model group (n=78) and a validation group (n=39). Logistic regression model was established and its value was estimated by receiver operating characteristic (ROC) curve.
Results:
Digital rectal examination(DRE), transrectal ultrasound(TRUS), MRI, prostate-specific antigen density (PSAD), and free PSA/total PSA (fPSA/tPSA) were the influential factors for prostate biopsy (P<0.01). The established logistic regression model for prostate cancer by regression coefficient was: logit P=-2.362+2.561×DRE+1.747×TRUS+2.901×MRI+1.126×PSAD- 2.569×fPSA/tPSA and area under curve was 0.907. When the cutoff aimed at 0.12, the sensitivity and specificity were 81.80% and 89.30%, respectively.
Conclusion:
Logistic regression model for prostate cancer can provide sufficient basis for prostate biopsy. Prostate biopsy should be performed when P value is more than 0.12.
More Related Videos
13:19Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
05:49Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
Published on: October 10, 2019
Related Concept Videos
01:21Cancer Survival Analysis
01:25Statistical Methods for Analyzing Epidemiological Data
01:20Comparing the Survival Analysis of Two or More Groups