Related Experiment Video
Updated: Jun 18, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Investigation of the Effectiveness of Prostate Biopsy Density in Predicting Prostate Cancer Under Cognitive and
Jiajin Feng1, Keming Chen1, Haifu Tian1
1General Hospital of Ningxia Medical University, Yinchuan, 750004, People's Republic of China.
Objective:
To explore the effectiveness of prostate biopsy density in predicting prostate cancer under cognitive and systematic biopsy mode in multi-parametric magnetic resonance imaging (mpMRI).
Methods:
A retrospective analysis was conducted on clinical data of 204 patients who were suspected of having prostate cancer with prostate-specific antigen (PSA) levels less than 50 ng mL-1 and underwent cognitive and systematic biopsy through the perineal approach in our hospital from 2022 to 2023. Univariate and multivariate logistic regression analyses were used to evaluate the odds ratios of prostate biopsy density and relevant clinical indicators. Logistic regression analysis was performed to establish a predictive model combining indicators with predictive value. The predictive value of each indicator and the new model was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC).
Results:
The detection rate of prostate cancer in the study population was 32.35%. Multivariate analysis showed that age, PSAD, PI-RADS 2.1 score, and prostate biopsy density were independent predictors of prostate cancer. The ROC curve analysis revealed an AUC of 0.707 (95% CI 0.625-0.790) for biopsy density, with a cutoff value of approximately 0.22 needle mL-1. The best predictive model consisted of age, PSAD, PI-RADS 2.1 score, and biopsy density, with an AUC of 0.857.
Conclusion:
Biopsy density is associated with the detection of prostate cancer, with a critical value of 0.22 needle mL-1. Combining biopsy density with other clinical indicators can significantly improve the ability to predict prostate cancer and avoid unnecessary prostate biopsy cores.
