Abstract：
To develop and validate a nomogram for preoperative predicting the pathological upgrading of prostate cancer (PCa).
The prediction model was developed in a primary cohort that consisted of 208 PCa patients. All patients included in the study possessed both biopsy pathology specimens and radical prostatectomy pathology specimens, and completed the (68 Ga-prostate-specific membrane antigen [PSMA]) positron emission tomography/computed tomography (PET/CT) detection. The R function \"createDataPartition\" was used in a 7:3 ratio to randomly divide the patients into training and validation cohorts. In the training cohort, the independent predictors of pathological upgrading of PCa were determined by univariate analysis, univariate regression analysis and multivariate regression analysis. Based on these independent predictors, a nomogram was developed, and its performance was evaluated by receiver operating characteristic (ROC) curve, area under the curve (AUC) and calibration curve of training cohort and validation cohort.
The nomogram incorporated five independent predictors including prostate volume (PV), SUVmax of the 68 Ga-PSMA PET/CT examination on prostate lesions (SUVmax ), body mass index (BMI); percentage of cancer positive biopsy cores (PPC) and biopsy International Society of Urological Pathology (ISUP) grade. The nomogram showed good diagnostic accuracy for the pathological upgrading of both the training cohort and the validation cohort (AUC = 0.818 and 0.806, respectively). The calibration curves for the two cohorts both showed optimal agreement between nomogram prediction and actual observation.
We developed and validated a nomogram to accurately predict the risk of pathological upgrading after radical PCa surgery, which can provide accurate basis for therapeutic schedule and prognostic data of PCa patients.
The prediction model was developed in a primary cohort that consisted of 208 PCa patients. All patients included in the study possessed both biopsy pathology specimens and radical prostatectomy pathology specimens, and completed the (68 Ga-prostate-specific membrane antigen [PSMA]) positron emission tomography/computed tomography (PET/CT) detection. The R function \"createDataPartition\" was used in a 7:3 ratio to randomly divide the patients into training and validation cohorts. In the training cohort, the independent predictors of pathological upgrading of PCa were determined by univariate analysis, univariate regression analysis and multivariate regression analysis. Based on these independent predictors, a nomogram was developed, and its performance was evaluated by receiver operating characteristic (ROC) curve, area under the curve (AUC) and calibration curve of training cohort and validation cohort.
The nomogram incorporated five independent predictors including prostate volume (PV), SUVmax of the 68 Ga-PSMA PET/CT examination on prostate lesions (SUVmax ), body mass index (BMI); percentage of cancer positive biopsy cores (PPC) and biopsy International Society of Urological Pathology (ISUP) grade. The nomogram showed good diagnostic accuracy for the pathological upgrading of both the training cohort and the validation cohort (AUC = 0.818 and 0.806, respectively). The calibration curves for the two cohorts both showed optimal agreement between nomogram prediction and actual observation.
We developed and validated a nomogram to accurately predict the risk of pathological upgrading after radical PCa surgery, which can provide accurate basis for therapeutic schedule and prognostic data of PCa patients.
摘要：
建立并验证术前预测前列腺癌（PCa）病理升级的列线图。
预测模型是在由208名PCa患者组成的主要队列中开发的。纳入研究的所有患者均具有活检病理标本和前列腺癌根治术病理标本。并完成了（68Ga-前列腺特异性膜抗原[PSMA]）正电子发射断层扫描/计算机断层扫描（PET/CT）检测。以7:3的比例使用R函数“createDataPartition”将患者随机分为训练和验证队列。在训练组中，通过单因素分析确定PCa病理升级的独立预测因子，单变量回归分析和多元回归分析。基于这些独立的预测因子，开发了一个列线图，并通过受试者工作特性(ROC)曲线评价其性能，训练队列和验证队列的曲线下面积(AUC)和校准曲线。
列线图包含五个独立的预测因子，包括前列腺体积（PV），68Ga-PSMAPET/CT检查前列腺病变的SUVmax（SUVmax），体质量指数（BMI）；癌症活检阳性百分比（PPC）和活检国际泌尿外科病理学会（ISUP）等级。列线图显示训练队列和验证队列的病理升级的良好诊断准确性（AUC分别为0.818和0.806）。两个队列的校准曲线均显示了列线图预测与实际观察之间的最佳一致性。
我们开发并验证了一个列线图，以准确预测根治性PCa手术后病理升级的风险，为PCa患者的治疗方案和预后数据提供准确依据。
预测模型是在由208名PCa患者组成的主要队列中开发的。纳入研究的所有患者均具有活检病理标本和前列腺癌根治术病理标本。并完成了（68Ga-前列腺特异性膜抗原[PSMA]）正电子发射断层扫描/计算机断层扫描（PET/CT）检测。以7:3的比例使用R函数“createDataPartition”将患者随机分为训练和验证队列。在训练组中，通过单因素分析确定PCa病理升级的独立预测因子，单变量回归分析和多元回归分析。基于这些独立的预测因子，开发了一个列线图，并通过受试者工作特性(ROC)曲线评价其性能，训练队列和验证队列的曲线下面积(AUC)和校准曲线。
列线图包含五个独立的预测因子，包括前列腺体积（PV），68Ga-PSMAPET/CT检查前列腺病变的SUVmax（SUVmax），体质量指数（BMI）；癌症活检阳性百分比（PPC）和活检国际泌尿外科病理学会（ISUP）等级。列线图显示训练队列和验证队列的病理升级的良好诊断准确性（AUC分别为0.818和0.806）。两个队列的校准曲线均显示了列线图预测与实际观察之间的最佳一致性。
我们开发并验证了一个列线图，以准确预测根治性PCa手术后病理升级的风险，为PCa患者的治疗方案和预后数据提供准确依据。
