PDF(Pubmed)
Abstract：
BACKGROUND: This study evaluates the efficacy of a nomogram for predicting the pathology upgrade of apical prostate cancer (PCa).
METHODS: A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram\'s performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA).
RESULTS: Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group.
CONCLUSIONS: The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.
METHODS: A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram\'s performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA).
RESULTS: Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group.
CONCLUSIONS: The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.
摘要：
背景：这项研究评估了列线图预测根尖前列腺癌（PCa）病理升级的功效。
方法：在两家医院（培训：754，内部验证：182，内部验证：148），通过联合系统和磁共振成像（MRI）靶向前列腺活检，然后进行根治性前列腺切除术（RP），共754例符合条件的患者被诊断为根尖PCa。通过比较活检和RP的结果，建立了用于识别高风险病理升级中的根尖肿瘤的列线图，其中结合了基于单变量和多变量逻辑回归的具有统计学意义的危险因素。通过受试者工作特征(ROC)曲线评估列线图的性能，校准图,和决策曲线分析(DCA)。
结果：单变量和多变量分析确定的年龄，靶向活检,目标内核的数量，TNM阶段,前列腺影像学报告和数据系统评分是根尖肿瘤病理进展的重要预测因子。我们的列线图，基于这些变量，显示病理升级的ROC曲线，值为0.883（95％CI，0.847-0.929），0.865（95%CI，0.790-0.945），和0.840（95%CI，0.742-0.904）的训练，分别为内部验证和内部-外部验证队列。校准曲线在预测结果和实际结果之间显示出良好的一致性。验证组还显示了校准曲线的很好的通用性。DCA结果还证明了我们的列线图的出色表现，在训练和内部验证组的0-0.9阈值概率范围内具有积极的优势。对于内部-外部验证组，则为0-0.6。
结论：列线图，整合临床，放射学,和病理数据，有效预测根尖PCa肿瘤病理升级的风险。它具有指导临床医生优化这些患者的手术管理的巨大潜力。
方法：在两家医院（培训：754，内部验证：182，内部验证：148），通过联合系统和磁共振成像（MRI）靶向前列腺活检，然后进行根治性前列腺切除术（RP），共754例符合条件的患者被诊断为根尖PCa。通过比较活检和RP的结果，建立了用于识别高风险病理升级中的根尖肿瘤的列线图，其中结合了基于单变量和多变量逻辑回归的具有统计学意义的危险因素。通过受试者工作特征(ROC)曲线评估列线图的性能，校准图,和决策曲线分析(DCA)。
结果：单变量和多变量分析确定的年龄，靶向活检,目标内核的数量，TNM阶段,前列腺影像学报告和数据系统评分是根尖肿瘤病理进展的重要预测因子。我们的列线图，基于这些变量，显示病理升级的ROC曲线，值为0.883（95％CI，0.847-0.929），0.865（95%CI，0.790-0.945），和0.840（95%CI，0.742-0.904）的训练，分别为内部验证和内部-外部验证队列。校准曲线在预测结果和实际结果之间显示出良好的一致性。验证组还显示了校准曲线的很好的通用性。DCA结果还证明了我们的列线图的出色表现，在训练和内部验证组的0-0.9阈值概率范围内具有积极的优势。对于内部-外部验证组，则为0-0.6。
结论：列线图，整合临床，放射学,和病理数据，有效预测根尖PCa肿瘤病理升级的风险。它具有指导临床医生优化这些患者的手术管理的巨大潜力。
