Prediction of pathological upgrading after radical prostatectomy for ISUP grade 1 prostate cancer: construction of a nomogram model based on clinical, imaging, and puncture biopsy
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摘要:
目的 分析穿刺活检确诊为国际泌尿病理学会（ISUP）1级前列腺癌患者行根治术后病理升级的危险因素，并构建预测列线图模型。 方法 回顾性纳入2017年1月至2024年5月于海军军医大学第一附属医院穿刺活检诊断为ISUP 1级前列腺癌，且接受根治性前列腺切除术的患者256例。收集患者的临床、影像学及穿刺活检资料，采用单因素及多因素二元logistic回归筛选独立预测因素并构建列线图模型，通过ROC曲线、临床影响曲线和决策曲线分析评估模型性能，采用Hosmer-Lemeshow检验评估模型的稳定性。 结果 多因素二元logistic回归分析显示，穿刺活检阳性针数（OR＝1.80）、前列腺影像报告数据系统（PI-RADS）评分（OR＝1.88）及前列腺特异性抗原密度（PSAD）分层（OR＝1.43）是术后病理升级的独立预测因子（均P＜0.01）。基于这3个预测因子建立的列线图模型预测病理升级的AUC值为0.82（95%CI 0.77~0.87）；决策曲线分析显示模型在阈值概率0.01~0.99范围内具有良好临床适用性；临床影响曲线分析表明，当模型阈值提升至0.40时可减少不必要治疗45例（假阳性率降低12%），净临床获益达0.46；Hosmer-Lemeshow检验显示模型拟合良好（P＝0.45）。 结论 本研究构建的列线图模型能准确预测ISUP 1级前列腺癌患者根治术后病理升级风险，为主动监测策略的个体化实施提供了量化依据。 -
关键词:
- 前列腺肿瘤 /
- 列线图 /
- 病理升级 /
- 风险分层 /
- 国际泌尿病理学会分级
Abstract:Objective To identify risk factors for pathological upgrading after radical prostatectomy in patients with biopsy-confirmed International Society of Urological Pathology (ISUP) grade 1 prostate cancer and to develop a predictive nomogram. Methods A total of 256 patients with ISUP grade 1 prostate cancer diagnosed by biopsy and undergoing radical prostatectomy in The First Affiliated Hospital of Naval Medical University between Jan. 2017 and May 2024 were retrospectively enrolled. Clinical, imaging, and biopsy data were collected. Independent predictors were identified using univariate and multivariate binary logistic regression, and a nomogram model was constructed. Model performance was evaluated using receiver operating characteristic curve, clinical impact curve, and decision curve analysis. The stability of the model was evaluated by Hosmer-Lemeshow test. Results Multivariate binary logistic regression analysis revealed that the number of positive puncture cores (odds ratio [OR] ＝1.80), prostate imaging and reporting data system (PI-RADS) score (OR＝1.88), and prostate specific antigen density (PSAD) stage (OR＝1.43) were independent predictors of pathological upgrading (all P < 0.01). The area under curve (AUC) value of the nomogram model based on the above 3 predictors was 0.82 (95% confidence interval 0.77-0.87). Decision curve analysis demonstrated favourable clinical utility within a threshold probability range of 0.01-0.99. Clinical impact curve analysis showed that at a threshold probability of 0.40, the model could avoid 45 unnecessary interventions (12% reduction in false-positive rate) with a net clinical benefit of 0.46. The Hosmer-Lemeshow test indicated good model fit (P＝0.45). Conclusion The constructed nomogram model can accurately predict the risk of pathological upgrading after radical prostatectomy in patients with ISUP grade 1 prostate cancer, providing a quantitative tool to support individualized decision-making for active surveillance. -
前列腺癌是男性最常见的泌尿生殖系统肿瘤，在全球男性中预期发病率排第1位，死亡率排第2位[1]。基于2014年国际泌尿病理学会（International Society of Urological Pathology，ISUP）分级分组系统[2]，分级越高恶性程度越高，其中ISUP 1级的前列腺癌生物学行为通常表现为惰性，美国国立综合癌症网络[3]及欧洲泌尿外科学会[4]指南均建议对于此类患者进行主动监测管理，而非根治性切除手术。然而，受限于前列腺癌的异质性、多灶性及穿刺活检漏诊风险，约半数术前诊断为ISUP 1级的前列腺癌患者在前列腺癌根治术后会病理升级（pathological upgrading，PU）至ISUP≥2级[5]。这一现象导致临床上倾向于对所有穿刺结果为ISUP 1级的患者实施手术治疗，从而可能对真正的极低危风险患者过度治疗。既往研究多聚焦在欧美人群中识别PU[6-7]，而前列腺癌具有明显种族异质性，国内研究主要针对全部前列腺癌患者进行分析[8-11]。目前，中国针对穿刺活检确诊为ISUP 1级的前列腺癌患者仍缺乏大样本、具有潜在临床转化价值的研究来优化治疗决策。本研究拟通过整合多个临床指标构建列线图预测模型，旨在为此类患者提供个体化风险评估工具。
1 资料和方法
1.1 病例资料
回顾性选择2017年1月至2024年5月于海军军医大学第一附属医院行前列腺多参数磁共振成像（multi-parameter magnetic resonance imaging，mp-MRI）检查且穿刺活检病理证实为ISUP 1级的前列腺癌患者。通过住院病历系统收集患者穿刺术前血清前列腺特异性抗原（prostate specific antigen，PSA）、前列腺穿刺活检针数、穿刺活检阳性针数。纳入标准：（1）患者在mp-MRI检查后2周内行穿刺活检，且病理组织证实为ISUP 1级；（2）穿刺后12周内行根治性前列腺切除术。排除标准：（1）临床、病理资料不全；（2）未进行前列腺系统穿刺或穿刺活检针数＜10针；（3）合并其他系统恶性肿瘤；（4）既往接受过前列腺癌局部治疗，或mp-MRI检查前6周内行前列腺穿刺活检术；（5）因MRI图像质量欠佳难以诊断。
1.2 mp-MRI扫描方法及图像分析
所有图像均通过2台3.0 T MRI扫描仪采集，分别为Discovery GE750 3.0 T MRI扫描仪（美国GE Healthcare公司）和Magnetom Skyra 3.0 T MRI扫描仪（德国Siemens Healthcare公司）。射频发射线圈为18通道相控阵体部线圈，接收线圈为32通道集成脊柱线圈。患者取仰卧位，扫描序列包括T2加权成像（T2-weighted imaging，T2WI）、多b值弥散加权成像（diffusion-weighted imaging，DWI）、动态对比增强（dynamic contrast enhancement，DCE）序列。2021年5月前的病例检查参数参考前列腺影像报告数据系统（prostate imaging and reporting data system，PI-RADS）v2.0版[12]规范，2021年5月后的病例参考PI-RADS v2.1版[13]规范。
由2名不同年资（有3~10年诊断经验）放射科医师在对临床和病理信息完全未知的情况下，分别对图像进行评估。评估内容包括：（1）结合T2WI、DWI、DCE序列进行PI-RADS评分；（2）在T2WI上测量前列腺的前后径、左右径、上下径。当评估结果出现分歧时，由另外1名放射科副主任医师（有20年诊断经验）做出裁决。PI-RADS评分由低到高分别为1~5分，代表存在ISUP≥2级前列腺癌的风险逐级增大。根据公式计算前列腺体积和前列腺特异性抗原密度（prostate specific antigen density，PSAD）：前列腺体积（cm3）＝前后径×左右径×上下径×0.52；PSAD＝PSA（ng/mL）/前列腺体积（cm3）。
国内外研究对PSAD诊断PU的临界值存在差异：国外多采用0.10~0.20 ng/（mL·cm3）[14-16]；而国内为0.15~0.37 ng/（mL·cm3）[10-11]，目前尚未达成统一标准。由此本研究提出PSAD分层：PSAD＜0.1 ng/（mL·cm3）、0.1 ng/（mL·cm3）≤PSAD＜0.2 ng/（mL·cm3）、0.2 ng/（mL·cm3）≤PSAD＜0.3 ng/（mL·cm3）、0.3 ng/（mL·cm3）≤PSAD＜0.4 ng/（mL·cm3）、0.4 ng/（mL·cm3）≤PSAD＜0.5 ng/（mL·cm3）、0.5 ng/（mL·cm3）≤PSAD＜0.7 ng/（mL·cm3）、PSAD≥0.7 ng/（mL·cm3）。
1.3 穿刺活检方法
所有操作均由经验丰富的泌尿外科主治医师在超声引导下进行。PI-RADS评分≥3分的患者先实施靶向穿刺活检3针，随后进行10~12针的系统穿刺活检；PI-RADS评分＜3分的患者直接行前列腺系统穿刺活检。前列腺穿刺活检阳性针数占比（percentage of positive biopsy cores，PCB）为穿刺活检证实为前列腺癌的针数与穿刺活检总针数的比值，根据PCB的负荷定义不同穿刺活检阳性针数负荷（positive core burden，PCB）分层：PCB＜10%、10%≤PCB＜20%、20%≤PCB＜30%、30%≤PCB＜40%、40%≤PCB＜50%、PCB≥50%。
1.4 统计学处理
采用SPSS 26.0和R 3.8.0软件进行数据分析。符合正态分布的计量资料以x±s表示，组间比较采用独立样本t检验；不符合正态分布的计量资料以M（Q1，Q3）表示，组间比较采用非参数检验；计数资料以例数和百分数表示，组间比较采用χ2检验。使用Kappa一致性系数分析2名阅片医师之间的诊断一致性，Kappa值≤0.4表示一致性欠佳，Kappa值＞0.4~0.6表示一致性中等、Kappa值＞0.6表示一致性好。采用单因素二元logistic回归分析筛选变量，然后通过多因素二元logistic回归分析构建模型以预测ISUP 1级前列腺癌患者根治术后PU的发生，通过向后法逐步剔除无统计学意义的变量。采用Bootstrap自抽样进行内部验证，通过决策曲线分析（decision curve analysis）和临床影响曲线（clinical impact curve）评估模型的预测效能；绘制校准曲线并计算一致性指数（consistency index，C-index），以评价模型的校准度；采用Hosmer-Lemeshow检验评估模型的稳定性，P＞0.05表示模型稳定性较好。绘制ROC曲线计算AUC值、灵敏度、特异度，评价模型的诊断效能。检验水准（α）为0.05。
样本量计算方法如下：在训练集中，根据二元logistic回归模型每个自变量的事件数（event-per-variable）准则[17]，每个自变量需至少对应10个阳性事件。本研究纳入3个危险因子，所需最小阳性事件数为10×3＝30。通过生成随机数划分80%的样本量，阳性事件数为130，显著高于阈值，表明训练集样本量足以支持模型稳定性与参数估计的可靠性。
2 结果
2.1 基本资料
共纳入256例术前穿刺活检诊断为ISUP 1级前列腺癌的患者，年龄为51~83岁，其中前列腺癌根治术后发生PU的患者共164例（64%）。PU阴性组与PU阳性组患者的年龄和BMI差异均无统计学意义（均P＞0.05）。ISUP 1级前列腺癌术后发生PU可能与血清PSA水平、活检阳性针数、PCB、PI-RADS评分、前列腺体积、PSAD等因素相关（均P＜0.01）。见表 1。
表 1 前列腺癌根治术前穿刺活检评估为ISUP 1级前列腺癌患者的临床和影像学特征Table 1 Clinical and imaging characteristics of patients with ISUP grade 1 prostate cancer assessed by biopsy before radical prostatectomyItem PU negative N＝92 PU positive N＝164 Statistic P value Age/year, x±s 65.5±6.6 66.2±6.3 t＝0.84 0.40 BMI/(kg·m-2), x±s 24.1±2.8 24.4±2.7 t＝0.73 0.47 PSA/(ng·mL-1), M (Q1, Q3) 7.3 (5.9, 11.6) 9.7 (7.2, 14.6) Z＝3.48 ＜0.01 Positive core, n (%) Z＝6.75 ＜0.01 1 47 (51.1) 27 (16.5) 2 26 (28.3) 38 (23.2) 3 10 (10.9) 43 (26.2) 4 6 (6.5) 15 (9.1) 5 2 (2.2) 22 (13.4) 6 1 (1.1) 6 (3.7) 7 0 5 (3.0) 8 0 3 (1.8) 9 0 4 (2.4) 10 0 1 (0.6) PCB stage, n (%) Z＝6.87 ＜0.01 ＜10% 46 (50.0) 25 (15.2) 10%-＜20% 28 (30.4) 41 (25.0) 20%-＜30% 12 (13.0) 50 (30.5) 30%-＜40% 3 (3.3) 21 (12.8) 40%-＜50% 3 (3.3) 12 (7.3) ≥50% 0 15 (9.1) ISUP grade after radical prostatectomy, n (%) Z＝14.31 ＜0.01 1 92 (100.0) 0 2 0 120 (73.2) 3 0 32 (19.5) 4 0 7 (4.3) 5 0 5 (3.0) PI-RADS score, n (%) Z＝5.98 ＜0.01 1 1 (1.1) 1 (0.6) 2 43 (46.7) 30 (18.3) 3 25 (27.2) 35 (21.3) 4 22 (23.9) 72 (43.9) 5 1 (1.1) 26 (15.9) Prostate volume/cm3, M (Q1, Q3) 53.0 (38.1, 69.9) 41.1 (29.5, 53.8) Z＝4.04 ＜0.01 PSAD/(ng·mL-1·cm-3), M (Q1, Q3) 0.16 (0.10, 0.22) 0.24 (0.15, 0.41) Z＝5.41 ＜0.01 PSAD＞0.15 ng·mL-1·cm-3, n (%) χ2＝13.44 ＜0.01 No 43 (46.7) 40 (24.4) Yes 49 (53.3) 124 (75.6) PSAD stage, n (%) Z＝5.44 ＜0.01 ＜0.1 ng·mL-1·cm-3 24 (26.1) 12 (7.3) 0.1-＜0.2 ng·mL-1·cm-3 39 (42.4) 51 (31.1) 0.2-＜0.3 ng·mL-1·cm-3 15 (16.3) 35 (21.3) 0.3-＜0.4 ng·mL-1·cm-3 7 (7.6) 23 (14.0) 0.4-＜0.5 ng·mL-1·cm-3 3 (3.3) 11 (6.7) 0.5-＜0.7 ng·mL-1·cm-3 3 (3.3) 19 (11.6) ≥0.7 ng·mL-1·cm-3 1 (1.1) 13 (7.9) ISUP: International Society of Urological Pathology; PU: Pathological upgrading; BMI: Body mass index; PSA: Prostate specific antigen; PCB: Percentage of positive biopsy cores; PI-RADS: Prostate imaging reporting and data system; PSAD: Prostate specific antigen density. 2.2 PI-RADS评分一致性分析
2名阅片医师的PI-RADS评分结果一致性中等，Kappa值为0.594。
2.3 二元logistic回归分析
单因素二元logistic回归分析显示，血清PSA水平、前列腺穿刺活检阳性针数、PCB、PI-RADS评分、前列腺体积、PSAD、PSAD＞0.15 ng/（mL·cm3）及PSAD分层是ISUP 1级前列腺癌患者术后PU的独立预测因子（均P＜0.05）。根据筛选出来的独立预测因子，将相关变量纳入多因素二元logistic回归模型，采用向后法逐步剔除不显著变量，最终确定前列腺穿刺活检阳性针数、PI-RADS评分、PSAD分层为模型构建的预测因子（均P＜0.01）。见表 2。
表 2 前列腺癌根治术后发生PU危险因素的单因素和多因素二元logistic回归分析Table 2 Univariate and multivariate binary logistic regression analyses for independent risk factors of PU after radical prostatectomyVariable Univariate analysis Multivariate analysis Z value OR (95%CI) P value Z value OR (95%CI) P value Age 0.84 1.02 (0.98, 1.06) 0.40 BMI 0.73 1.04 (0.94, 1.14) 0.47 PSA 2.47 1.05 (1.01, 1.09) 0.01 0.19 1.00 (0.95, 1.04) 0.85 Positive core 5.62 2.00 (1.57, 2.55) ＜0.01 4.47 1.80 (1.39, 2.32) ＜0.01 PCB 5.56 1.10 (1.06, 1.13) ＜0.01 0.44 1.02 (0.93, 1.12) 0.66 PCB stage 5.89 2.27 (1.73, 2.98) ＜0.01 0.05 1.03 (0.37, 2.88) 0.96 PI-RADS score 5.69 2.34 (1.75, 3.13) ＜0.01 3.85 1.88 (1.37, 2.60) ＜0.01 Prostate volume 3.64 0.98 (0.97, 0.99) ＜0.01 0.28 1.00 (0.99, 1.02) 0.78 PSAD 4.22 66.09 (9.43, 463.37) ＜0.01 0.14 1.30 (0.03, 54.19) 0.89 PSAD＞0.15 ng·mL-1·cm-3 3.61 2.72 (1.58, 4.68) ＜0.01 0.42 0.84 (0.38, 1.86) 0.67 PSAD stage 4.82 1.64 (1.34, 2.01) ＜0.01 3.34 1.43 (1.16, 1.77) ＜0.01 PU: Pathological upgrading; BMI: Body mass index; PSA: Prostate specific antigen; PCB: Percentage of positive biopsy cores; PI-RADS: Prostate imaging reporting and data system; PSAD: Prostate specific antigen density; OR: Odds ratio; 95%CI: 95% confidence interval. 2.4 列线图预测模型
基于多因素二元logistic回归模型生成识别ISUP 1级前列腺癌患者根治术后PU的列线图预测模型（图 1）。ROC曲线分析显示，该模型预测PU的AUC值为0.82（95%CI 0.77~0.87）（图 2A）。决策曲线分析结果显示，该模型在阈值概率0.01~0.99范围内的净获益率均优于“全干预”或“全不干预”策略，具有显著临床应用价值（图 2B）。临床影响曲线分析显示，该模型在阈值提升至0.40时，保持0.91真阳性识别率的同时显著减少不必要治疗45例（假阳性率降低12%），净临床获益达到0.46，有效优化了临床资源利用率（图 2C）。通过1 000次Bootstrap自抽样验证绘制校准曲线，模型的平均C-index为0.82（95%CI 0.81~0.82），表现出良好的一致性。Hosmer-Lemeshow检验显示性能稳定（P＝0.45，图 2D）。
图 1 穿刺活检确诊ISUP 1级前列腺癌患者术后PU预测的列线图Fig. 1 Nomogram for predicting postoperative PU in patients with ISUP grade 1 prostate cancer confirmed by puncture biopsyPSAD stage 1-7 represented PSAD being < 0.1, 0.1- < 0.2, 0.2- < 0.3, 0.3- < 0.4, 0.4- < 0.5, 0.5- < 0.7, and ≥0.7 ng/(mL·cm3).ISUP: International Society of Urological Pathology; PU: Pathological upgrading; PI-RADS: Prostate imaging reporting and data system; PSAD: Prostate specific antigen density.
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图 2 列线图预测穿刺活检确诊ISUP 1级前列腺癌患者术后发生PU的表现Fig. 2 Performance of nomogram for predicting PU after surgery in patients with puncture biopsy-proven ISUP grade 1 prostate cancerA: ROC curve of nomogram; B: Nomogram for evaluating clinical net benefit in decision curve analysis; C: Nomogram for evaluating clinical net benefit in clinical impact curve; D: The calibration curve of nomogram. ISUP: International Society of Urological Pathology; PU: Pathological upgrading; AUC: Area under curve; 95%CI: 95% confidence interval; ROC: Receiver operating characteristic.下载:
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3 讨论
前列腺癌的异质性较大，对极低危前列腺癌采取主动监测策略的目的在于平衡过度治疗的风险，本研究分析发现64%的ISUP 1级前列腺癌患者术后发生PU，这凸显了精准预测工具的临床必要性。本研究通过整合PI-RADS评分、PSAD分层及穿刺活检阳性针数等指标构建了基于中国人群的列线图预测模型，模型诊断PU的AUC值为0.82，不仅诊断能力突出且在验证集中表现稳定。
单因素二元logistic回归分析显示，前列腺穿刺活检阳性针数、前列腺体积、PCB、PI-RADS评分、PSAD等在是否发生PU方面差异均有统计学意义（均P＜0.05）。多因素二元logistic回归分析显示，前列腺穿刺活检阳性针数、PI-RADS评分及PSAD分层是前列腺癌根治术后发生PU的独立预测因子。前列腺穿刺活检阳性针数反映肿瘤的空间异质性，高负荷提示病灶范围广泛，与Epstein等[2]提出的“显著前列腺癌”定义（肿瘤体积≥0.5 cm3或ISUP≥2级）高度相关。PI-RADS评分同样是对“显著前列腺癌”做出评估的标准化工具，本研究显示PI-RADS评分每提高1分PU风险增加1.88倍（OR＝1.88），既往也有研究显示PI-RADS评分＞3分PU风险增加2.47倍[18]。由于亚洲人群的前列腺体积小于欧美人群[19]，PSAD的具体阈值仍存在争议，所以本研究创新性提出的PSAD分层在模型中表现出更细粒度的预测价值，优于传统的二分法[如PSAD＞0.15 ng/（mL·cm3）]。
目前虽然有较多模型用于前列腺癌根治术后PU的预测，但主要针对全部ISUP分级患者。现有广泛应用的预测工具如李强等[6]构建的G.P.A.P.模型和Chun等[20]构建的列线图模型预测PU的AUC值分别为0.75和0.80，均基于全ISUP分级人群（以美国国家癌症研究所统计的白种人为主），其预测参数如临床T分期、PSA、活检ISUP分级虽然在异质性人群中表现尚可，但可能不太适用于ISUP 1级这种特定患者。Oh等[21]通过外显子测序结合PSAD、活检ISUP分级、活检阳性针数等临床参数构建模型，并发现模型预测PU的AUC值为0.76，特异度为95%。Ozbozduman等[22]引入影像学参数后开发了基于PI-RADS评分、PSAD和前列腺活检阳性针数的机器学习模型，该模型诊断PU的AUC值为0.79。Kim等[23]纳入450例ISUP 1~2级的前列腺癌患者，通过年龄、PI-RADS评分、PSA、活检阳性针数等临床参数构建列线图，在外部验证集中预测PU的AUC值为0.78。罗程等[24]分析了155例ISUP 1~3级前列腺癌患者的BMI、PSA、穿刺ISUP分级及临床T分期，并建立临床模型，预测PU的AUC值为0.799。以上研究样本量均较小，往往纳入多个ISUP分级的患者，且预测效能不佳（AUC值＜0.8）。此外，多项研究证实，PI-RADS评分诊断ISUP≥2级的前列腺癌的价值日益凸显[25-26]，其应用场景高度契合穿刺活检病理结果为ISUP 1级但存在PU风险的病例评估。既往多数预测模型未能充分整合影像学信息。因此，本研究创新性地纳入PI-RADS评分并基于相关人群进行危险因素模型构建。结果表明，模型预测根治术后PU的AUC值达0.82，均高于以上研究模型。
本研究存在以下局限性。第一，本研究的单中心回顾性设计可能引入选择偏倚，严格的纳排标准也会导致样本数量的局限，建立的模型仍需进一步进行验证。第二，前列腺穿刺活检存在操作者依赖性，各医院之间难以统一，而本研究仅分析前列腺系统穿刺的患者数据，这可能导致研究结果不能全面反映所有穿刺方式的情况，存在一定局限性。
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图 1 穿刺活检确诊ISUP 1级前列腺癌患者术后PU预测的列线图
Fig. 1 Nomogram for predicting postoperative PU in patients with ISUP grade 1 prostate cancer confirmed by puncture biopsy
PSAD stage 1-7 represented PSAD being < 0.1, 0.1- < 0.2, 0.2- < 0.3, 0.3- < 0.4, 0.4- < 0.5, 0.5- < 0.7, and ≥0.7 ng/(mL·cm3).ISUP: International Society of Urological Pathology; PU: Pathological upgrading; PI-RADS: Prostate imaging reporting and data system; PSAD: Prostate specific antigen density.
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图 2 列线图预测穿刺活检确诊ISUP 1级前列腺癌患者术后发生PU的表现
Fig. 2 Performance of nomogram for predicting PU after surgery in patients with puncture biopsy-proven ISUP grade 1 prostate cancer
A: ROC curve of nomogram; B: Nomogram for evaluating clinical net benefit in decision curve analysis; C: Nomogram for evaluating clinical net benefit in clinical impact curve; D: The calibration curve of nomogram. ISUP: International Society of Urological Pathology; PU: Pathological upgrading; AUC: Area under curve; 95%CI: 95% confidence interval; ROC: Receiver operating characteristic.
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表 1 前列腺癌根治术前穿刺活检评估为ISUP 1级前列腺癌患者的临床和影像学特征
Table 1 Clinical and imaging characteristics of patients with ISUP grade 1 prostate cancer assessed by biopsy before radical prostatectomy
Item PU negative N＝92 PU positive N＝164 Statistic P value Age/year, x±s 65.5±6.6 66.2±6.3 t＝0.84 0.40 BMI/(kg·m-2), x±s 24.1±2.8 24.4±2.7 t＝0.73 0.47 PSA/(ng·mL-1), M (Q1, Q3) 7.3 (5.9, 11.6) 9.7 (7.2, 14.6) Z＝3.48 ＜0.01 Positive core, n (%) Z＝6.75 ＜0.01 1 47 (51.1) 27 (16.5) 2 26 (28.3) 38 (23.2) 3 10 (10.9) 43 (26.2) 4 6 (6.5) 15 (9.1) 5 2 (2.2) 22 (13.4) 6 1 (1.1) 6 (3.7) 7 0 5 (3.0) 8 0 3 (1.8) 9 0 4 (2.4) 10 0 1 (0.6) PCB stage, n (%) Z＝6.87 ＜0.01 ＜10% 46 (50.0) 25 (15.2) 10%-＜20% 28 (30.4) 41 (25.0) 20%-＜30% 12 (13.0) 50 (30.5) 30%-＜40% 3 (3.3) 21 (12.8) 40%-＜50% 3 (3.3) 12 (7.3) ≥50% 0 15 (9.1) ISUP grade after radical prostatectomy, n (%) Z＝14.31 ＜0.01 1 92 (100.0) 0 2 0 120 (73.2) 3 0 32 (19.5) 4 0 7 (4.3) 5 0 5 (3.0) PI-RADS score, n (%) Z＝5.98 ＜0.01 1 1 (1.1) 1 (0.6) 2 43 (46.7) 30 (18.3) 3 25 (27.2) 35 (21.3) 4 22 (23.9) 72 (43.9) 5 1 (1.1) 26 (15.9) Prostate volume/cm3, M (Q1, Q3) 53.0 (38.1, 69.9) 41.1 (29.5, 53.8) Z＝4.04 ＜0.01 PSAD/(ng·mL-1·cm-3), M (Q1, Q3) 0.16 (0.10, 0.22) 0.24 (0.15, 0.41) Z＝5.41 ＜0.01 PSAD＞0.15 ng·mL-1·cm-3, n (%) χ2＝13.44 ＜0.01 No 43 (46.7) 40 (24.4) Yes 49 (53.3) 124 (75.6) PSAD stage, n (%) Z＝5.44 ＜0.01 ＜0.1 ng·mL-1·cm-3 24 (26.1) 12 (7.3) 0.1-＜0.2 ng·mL-1·cm-3 39 (42.4) 51 (31.1) 0.2-＜0.3 ng·mL-1·cm-3 15 (16.3) 35 (21.3) 0.3-＜0.4 ng·mL-1·cm-3 7 (7.6) 23 (14.0) 0.4-＜0.5 ng·mL-1·cm-3 3 (3.3) 11 (6.7) 0.5-＜0.7 ng·mL-1·cm-3 3 (3.3) 19 (11.6) ≥0.7 ng·mL-1·cm-3 1 (1.1) 13 (7.9) ISUP: International Society of Urological Pathology; PU: Pathological upgrading; BMI: Body mass index; PSA: Prostate specific antigen; PCB: Percentage of positive biopsy cores; PI-RADS: Prostate imaging reporting and data system; PSAD: Prostate specific antigen density. 表 2 前列腺癌根治术后发生PU危险因素的单因素和多因素二元logistic回归分析
Table 2 Univariate and multivariate binary logistic regression analyses for independent risk factors of PU after radical prostatectomy
Variable Univariate analysis Multivariate analysis Z value OR (95%CI) P value Z value OR (95%CI) P value Age 0.84 1.02 (0.98, 1.06) 0.40 BMI 0.73 1.04 (0.94, 1.14) 0.47 PSA 2.47 1.05 (1.01, 1.09) 0.01 0.19 1.00 (0.95, 1.04) 0.85 Positive core 5.62 2.00 (1.57, 2.55) ＜0.01 4.47 1.80 (1.39, 2.32) ＜0.01 PCB 5.56 1.10 (1.06, 1.13) ＜0.01 0.44 1.02 (0.93, 1.12) 0.66 PCB stage 5.89 2.27 (1.73, 2.98) ＜0.01 0.05 1.03 (0.37, 2.88) 0.96 PI-RADS score 5.69 2.34 (1.75, 3.13) ＜0.01 3.85 1.88 (1.37, 2.60) ＜0.01 Prostate volume 3.64 0.98 (0.97, 0.99) ＜0.01 0.28 1.00 (0.99, 1.02) 0.78 PSAD 4.22 66.09 (9.43, 463.37) ＜0.01 0.14 1.30 (0.03, 54.19) 0.89 PSAD＞0.15 ng·mL-1·cm-3 3.61 2.72 (1.58, 4.68) ＜0.01 0.42 0.84 (0.38, 1.86) 0.67 PSAD stage 4.82 1.64 (1.34, 2.01) ＜0.01 3.34 1.43 (1.16, 1.77) ＜0.01 PU: Pathological upgrading; BMI: Body mass index; PSA: Prostate specific antigen; PCB: Percentage of positive biopsy cores; PI-RADS: Prostate imaging reporting and data system; PSAD: Prostate specific antigen density; OR: Odds ratio; 95%CI: 95% confidence interval. -
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