Feng Jiajin, Chen Keming, Tian Haifu, Abdulkarem Al-Qaisi Mohammed, Tuo Yunshang, Wang Xuehao, Huang Bincheng, Gao Yu, Lv Zhiyong, He Rui, Li Guangyong
General Hospital of Ningxia Medical University, Yinchuan, 750004, People's Republic of China.
Key Laboratory of Fertility Preservation and Maintenance of Ministry of Education, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, 750004, People's Republic of China.
Cancer Manag Res. 2024 Jul 24;16:883-890. doi: 10.2147/CMAR.S476636. eCollection 2024.
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).
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 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).
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. The best predictive model consisted of age, PSAD, PI-RADS 2.1 score, and biopsy density, with an AUC of 0.857.
Biopsy density is associated with the detection of prostate cancer, with a critical value of 0.22 needle mL. Combining biopsy density with other clinical indicators can significantly improve the ability to predict prostate cancer and avoid unnecessary prostate biopsy cores.
探讨在多参数磁共振成像（mpMRI）的认知和系统活检模式下，前列腺活检密度预测前列腺癌的有效性。
对2022年至2023年在我院因前列腺特异性抗原（PSA）水平低于50 ng/mL而疑似患有前列腺癌且经会阴途径进行认知和系统活检的204例患者的临床资料进行回顾性分析。采用单因素和多因素逻辑回归分析评估前列腺活检密度及相关临床指标的比值比。进行逻辑回归分析以建立结合具有预测价值指标的预测模型。使用受试者工作特征（ROC）曲线和曲线下面积（AUC）评估各指标及新模型的预测价值。
研究人群中前列腺癌的检出率为32.35%。多因素分析显示，年龄、前列腺特异抗原密度（PSAD）、PI-RADS 2.1评分和前列腺活检密度是前列腺癌的独立预测因素。ROC曲线分析显示活检密度的AUC为0.707（95%CI 0.625-0.790），临界值约为0.22针/mL。最佳预测模型由年龄、PSAD、PI-RADS 2.1评分和活检密度组成，AUC为0.857。
活检密度与前列腺癌的检测相关，临界值为0.22针/mL。将活检密度与其他临床指标相结合可显著提高预测前列腺癌的能力，并避免不必要的前列腺活检针数。
