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

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

Abstract

Abstract:

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

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]. Chinese Journal of Endourology(Electronic Edition), 2022, 16(06): 501-507.

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Figures/Tables 8

References 19

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		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

		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

		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

		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

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

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