PI-RADS≤3分患者预测前列腺穿刺阳性列线图模型的构建

doi:10.3877/cma.j.issn.1674-3253.2022.06.004

中华腔镜泌尿外科杂志(电子版) ›› 2022, Vol. 16 ›› Issue (06) : 501 -507. doi: 10.3877/cma.j.issn.1674-3253.2022.06.004

临床研究

PI-RADS≤3分患者预测前列腺穿刺阳性列线图模型的构建

张泳欣¹, 王忠泉², 张水兴³, 肖学红¹, 杨昂¹, 唐秉航¹, 黄红星⁴, 袁润强⁴, 卢扬柏^,⁴

^1. 528403　广东，中山市人民医院影像中心
^2. 528403　广东，中山市人民医院特诊中心门诊部
^3. 510630　广州，暨南大学附属第一医院影像中心
^4. 528403　广东，中山市人民医院泌尿外科

收稿日期:2022-09-06 出版日期:2022-12-01
通信作者: 卢扬柏
基金资助:
中山市人民医院放射影像中心重点专科科研项目(T2020016); 中山市科技计划项目(2019B1063，2020B1070，2020B1073); 中山市人民医院2022年度医院科研基金重大项目(BG20228249); 中山市人民医院高水平医院建设项目-泌尿外科(G330102097008)

Construction of a nomogram for predicting the risk of positiver prostate biospy with PI-RADS≤3

Yongxin Zhang¹, Zhongquan Wang², Shuixing Zhang³, Xuehong Xiao¹, Ang Yang¹, Binghang Tang¹, Hongxing Huang⁴, Runqiang Yuan⁴, Yangbai Lu^,⁴

^1. Department of Radiology, Zhongshan City People's Hospital Affiliated to Sun Yat sen University, Guangdong 528403, China
^2. Department of Kangyi VIP Outpatient Clinic, Zhongshan City People's Hospital Affiliated to Sun Yat sen University, Guangdong 528403, China
^3. Department of Radiology, the First Affiliated Hospital of Jinan University, Guangzhou 510630, China
^4. Department of Urology, Zhongshan City People's Hospital Affiliated to Sun Yat sen University, Guangdong 528403, China

Received:2022-09-06 Published:2022-12-01
Corresponding author: Yangbai Lu

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引用本文：

张泳欣, 王忠泉, 张水兴, 肖学红, 杨昂, 唐秉航, 黄红星, 袁润强, 卢扬柏. PI-RADS≤3分患者预测前列腺穿刺阳性列线图模型的构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2022, 16(06): 501-507.

Yongxin Zhang, Zhongquan Wang, Shuixing Zhang, Xuehong Xiao, Ang Yang, Binghang Tang, Hongxing Huang, Runqiang Yuan, Yangbai Lu. Construction of a nomogram for predicting the risk of positiver prostate biospy with PI-RADS≤3[J/OL]. Chinese Journal of Endourology(Electronic Edition), 2022, 16(06): 501-507.

目的

探讨前列腺影像报告及数据系统（PI-RADS.v2.1）联合前列腺特异性抗原及其他参数构建的列线图模型对PI-RADS≤3分患者活检阳性的预测价值。

方法

回顾性分析2018年1月至2021年12月198例在中山市人民医院接受经直肠超声穿刺前列腺首次活检患者的临床血清学和影像学资料，应用Logistic多因素回归分析前列腺癌相关独立风险因素，并构建对前列腺PI-RADS≤3分病变的列线图模型，利用受试者工作曲线、校准曲线和决策曲线对模型进行评估。

结果

多因素Logistic回归分析显示年龄（P<0.001）、PI-RADS（P=0.017）、游离PSA/总PSA（FPSA/TPSA）（P=0.049）及移行带体积（TZV）（P<0.001）是前列腺癌的独立危险因素。基于多变量构建的融合模型效能最优（AUC=0.823，95%CI=0.762~0.885），敏感性81.3%，特异性78.8%，准确性79.8%。校准曲线显示其预测概率与病理结果有良好的一致性。决策曲线显示模型具有良好的临床应用价值。

结论

基于多变量构建的列线图及预测模型能较好地术前预测患前列腺癌的风险。

关键词: 前列腺癌, PI-RADS, 列线图, 预测模型

Objective

To investigate the predictive value of the nomogram model based on prostate imaging reporting and data system (PI-RADS v2.1) combined with prostate-specific antigen and other parameters for puncture in patients with PI-RADS≤3.

Methods

The clinical serological and imaging data of 198 patients who underwent transrectal ultrasound for the first prostate biopsy in Zhongshan People's Hospital from January 2018 to December 2021 were retrospectively analyzed, and the risk scores were analyzed by Logistic multifactor regression. The independent risk factors related to prostate cancer were analyzed, and the rosette model of prostate PI-RADS≤3 was constructed, and the model was evaluated by the subject operating curve, calibration curve and decision curve.

Results

Multivariate logistic regression analysis showed that age (P<0.001), PI-RADS (P=0.017), FPSA/TPSA (P=0.049) and TZV (P<0.001) were statistically significant independent risk factors for prostate cancer. The fusion model based on multivariable construction had the best performance, with (AUC=0.823, 95%CI=0.762-0.885), sensitivity 81.3%, specificity 78.8%, accuracy 79.8%. The calibration curve showed a good agreement between the predicted probabilities of fusion model and pathologic findings. The decision curve model had good clinical application value.

Conclusion

The nomogram and prediction model can better predict the risk of prostate cancer before surgery.

Key words: Prostate, PI-RADS, Nomogram, Prediction model

图1 患者纳入流程

表1 198例前列腺病变患者的临床资料

		总(n=198)	良性(n=118)	恶性 (n=80)	P值
年龄[岁，(±s)]		69±9	67±8	70±9	0.037
PI-RADS[例(%)]					<0.001
	2	58(29.3)	46(39.0)	12(15.0)
	3	140(70.7)	72(61.0)	68(85.0)
tPSA[ng/ml，M(IQR)]		8.89(6.50，13.80)	8.18(6.26，11.10)	10.00(6.91，20.50)	0.01
fPSA[ng/ml，M(IQR)]		1.20(0.73，2.16)	1.21(0.89，2.00)	1.10(0.67，2.90)	0.569
fPSA/tPSA[例(%)]					0.011
	≥0.16	86(43.4)	60(50.8)	26(32.5)
	< 0.16	112(56.6)	58(49.2)	54(67.5)
PV[ml，M(IQR)]		52.71(37.81，73.87)	64.04(44.33，79.95)	41.32(29.94，55.49)	<0.001
TZV[ml，M(IQR)]		25.83(13.87，42.26)	36.74(18.93，53.38)	15.07(8.01，25.83)	<0.001
PZV[ml，M(IQR)]		24.28(17.38，33.62)	26.26(17.5，34.61)	22.5(15.78，32.72)	0.219
PSAD[ng/ml²，M(IQR)]		0.17(0.11，0.30)	0.14(0.10，0.21)	0.28(0.15，0.42)	<0.001
TZ-PSAD[ng/ml²，M(IQR)]		0.36(0.19，0.77)	0.27(0.15，0.45)	0.71(0.33，1.54)	<0.001
PZ-PSAD[ng/ml²，M(IQR)]		0.37(0.23，0.66)	0.32(0.22，0.57)	0.45(0.32，0.95)	<0.006

表1 198例前列腺病变患者的临床资料

		总(n=198)	良性(n=118)	恶性 (n=80)	P值
年龄[岁，(±s)]		69±9	67±8	70±9	0.037
PI-RADS[例(%)]					<0.001
	2	58(29.3)	46(39.0)	12(15.0)
	3	140(70.7)	72(61.0)	68(85.0)
tPSA[ng/ml，M(IQR)]		8.89(6.50，13.80)	8.18(6.26，11.10)	10.00(6.91，20.50)	0.01
fPSA[ng/ml，M(IQR)]		1.20(0.73，2.16)	1.21(0.89，2.00)	1.10(0.67，2.90)	0.569
fPSA/tPSA[例(%)]					0.011
	≥0.16	86(43.4)	60(50.8)	26(32.5)
	< 0.16	112(56.6)	58(49.2)	54(67.5)
PV[ml，M(IQR)]		52.71(37.81，73.87)	64.04(44.33，79.95)	41.32(29.94，55.49)	<0.001
TZV[ml，M(IQR)]		25.83(13.87，42.26)	36.74(18.93，53.38)	15.07(8.01，25.83)	<0.001
PZV[ml，M(IQR)]		24.28(17.38，33.62)	26.26(17.5，34.61)	22.5(15.78，32.72)	0.219
PSAD[ng/ml²，M(IQR)]		0.17(0.11，0.30)	0.14(0.10，0.21)	0.28(0.15，0.42)	<0.001
TZ-PSAD[ng/ml²，M(IQR)]		0.36(0.19，0.77)	0.27(0.15，0.45)	0.71(0.33，1.54)	<0.001
PZ-PSAD[ng/ml²，M(IQR)]		0.37(0.23，0.66)	0.32(0.22，0.57)	0.45(0.32，0.95)	<0.006

表2 198例前列腺病变患者的临床资料单因素和多因素Logistic回归分析

		单因素		多因素
		OR(95%CI)	P值	OR(95%CI)	P值
年龄[岁，(±s)]		1.041(1.005，1.078)	0.023	1.087(1.040，1.136)	<0.001
PI-RADSS[例(%)]			<0.001		0.017
	2
	3	3.620(1.768，7.412)		2.629(1.189，5.812)
tPSA[ng/ml，M(IQR)]		1.006(0.994，1.018)	0.331
fPSA[ng/ml，M(IQR)]		1.032(0.954，1.115)	0.433
fPSA/tPSA			0.011		0.049
	≥0.16
	< 0.16	2.149(1.190，3.879)		1.980(0.996，3.936)
PV[ml，M(IQR)]		0.971(0.958，0.984)	<0.001
TZV[ml，M(IQR)]		0.959(0.942，0.976)	<0.001	0.949(0.928，0.970)	<0.001
PZV([ml，M(IQR)]		0.987(0.966，1.008)	0.227
PSAD[ng/ml²，M(IQR)]		2.954(1.096，7.963)	0.032
TZ-PSAD[ng/ml²，M(IQR)]		1.501(1.078，2.090)	0.016
PZ-PSAD[ng/ml²，M(IQR)]		1.346(0.976，1.857)	0.070

表2 198例前列腺病变患者的临床资料单因素和多因素Logistic回归分析

		单因素		多因素
		OR(95%CI)	P值	OR(95%CI)	P值
年龄[岁，(±s)]		1.041(1.005，1.078)	0.023	1.087(1.040，1.136)	<0.001
PI-RADSS[例(%)]			<0.001		0.017
	2
	3	3.620(1.768，7.412)		2.629(1.189，5.812)
tPSA[ng/ml，M(IQR)]		1.006(0.994，1.018)	0.331
fPSA[ng/ml，M(IQR)]		1.032(0.954，1.115)	0.433
fPSA/tPSA			0.011		0.049
	≥0.16
	< 0.16	2.149(1.190，3.879)		1.980(0.996，3.936)
PV[ml，M(IQR)]		0.971(0.958，0.984)	<0.001
TZV[ml，M(IQR)]		0.959(0.942，0.976)	<0.001	0.949(0.928，0.970)	<0.001
PZV([ml，M(IQR)]		0.987(0.966，1.008)	0.227
PSAD[ng/ml²，M(IQR)]		2.954(1.096，7.963)	0.032
TZ-PSAD[ng/ml²，M(IQR)]		1.501(1.078，2.090)	0.016
PZ-PSAD[ng/ml²，M(IQR)]		1.346(0.976，1.857)	0.070

图2 多因素回归模型绘制列线图注：预测患者得前列腺癌概率，模型中包含年龄、PI-RADS、FPSA/TPSA及TZV 4个变量。每个变量都被评分，总分的范围是0~180。恶性风险轴上的总分代表患前列腺癌的概率。分数越高，患者得前列腺癌的风险就越高

图3 融合模型及单一变量对前列腺癌预测效能的ROC曲线注：融合模型AUC=0.823，95%CI=0.762~0.885，敏感性81.3%，特异性78.8%，准确性79.8%

表3 融合模型及单一变量模型对PI-RADS≤3的病变患前列腺癌预测效能

组别	AUC(95%CI)	敏感性(%)	特异性(%)	准确性(%)
融合模型	0.823(0.762，0.885)	81.3	78.8	79.8
年龄	0.588(0.506，0.670)	52.5	64.4	59.6
PI-RADS	0.620(0.542，0.698)	85.0	39.0	57.6
FPSA/TPSA	0.592(0.511，0.672)	67.5	50.8	57.6
TZV	0.747(0.677，0.816)	69.5	75.0	73.2

表3 融合模型及单一变量模型对PI-RADS≤3的病变患前列腺癌预测效能

组别	AUC(95%CI)	敏感性(%)	特异性(%)	准确性(%)
融合模型	0.823(0.762，0.885)	81.3	78.8	79.8
年龄	0.588(0.506，0.670)	52.5	64.4	59.6
PI-RADS	0.620(0.542，0.698)	85.0	39.0	57.6
FPSA/TPSA	0.592(0.511，0.672)	67.5	50.8	57.6
TZV	0.747(0.677，0.816)	69.5	75.0	73.2

图4 列线图的校准曲线

图5 列线图的决策曲线分析

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Construction of a nomogram for predicting the risk of positiver prostate biospy with PI-RADS≤3

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Construction of a nomogram for predicting the risk of positiver prostate biospy with PI-RADS≤3