A predictive model established based on MRI parameters for prostate biopsy in population with PSA greater than 4 ng/ml

doi:10.3877/cma.j.issn.1674-3253.2021.04.002

Abstract

Abstract:

Objective

To establish a prostate cancer biopsy prediction model that includes common clinical indicators, imaging indicators.

Methods

The clinical data of patients who underwent ultrasound-guided prostate biopsy in the University of Hong Kong-Shenzhen Hospital from January 2018 to February 2021 were analyzed retrospectively. Univariate analysis and multivariate analysis were conducted on MRI indicators and clinical indicators which include age, prostate volume, digital rectal examination results, transrectal prostate B-ultrasound results, fPSA/tPSA, PI-RADs v2 scores, dynamic contrast enhancement, apparent diffusion coefficient, etc.. A prostate biopsy prediction model was established and the area under the ROC curve was calculated to judge the diagnostic efficacy.

Result

Univariate analysis showed that differences of age, prostate volume, digital rectal examination results, prostate ultrasound results, apparent diffusion coefficient, dynamic contrast enhancement and PI-RADS v2 score were statistically significant (P<0.05), and there was no difference in tPSA (P=0.1). Multivariate analysis showed that fPSA/tPSA, transrectal ultrasound results, apparent diffusion coefficient and PI-RADS v2 score were independent risk factors. The prediction model of prostate biopsy established by the above indicators is P=1/1+exp[11.1-3.49×(fPSA/tPSA)-2.02×TRUS-1.59×ADC-1.73×PI-RADS].

Conclusion

Compared with only PSA and other clinical indicators, the establishment of a prostate cancer biopsy prediction model based on the PI-RADS v2 score and apparent diffusion coefficient can improve the diagnostic efficiency of prostate cancer.

Key words: Prostate cancer, Biopsy, MRI, Predictive model

Zhenquan Lu, Bingfeng Luo, Peifeng Li, Yuan Yuan, Yaping Xing, Lin Luo, Yan Wang, Yizhen Jia, Jian Hou, Songwu Liang, Ruidong Ji, Guangyan Luo, Yifei Zhu. A predictive model established based on MRI parameters for prostate biopsy in population with PSA greater than 4 ng/ml[J]. Chinese Journal of Endourology(Electronic Edition), 2021, 15(04): 275-279.

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References 14

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因变量	变量名	赋值说明
年龄	X1	连续变量
PV	X2	连续变量
DRE	X3	无结节=0，有结节=1
fPSA/tPSA	X4	比值<0.16=0，比值≥0.16=1
TRUS	X5	无低回声结节=0，有低回声结节=1
ADC	X6	ADC≥1=1，0.8<ABC<1=2，X<0.8=3
DCE	X7	无早期强化=0，有早期强化=1
PI-RADS评分	X8	1-2分=1，3分=2，4-5分=3

因变量	变量名	赋值说明
年龄	X1	连续变量
PV	X2	连续变量
DRE	X3	无结节=0，有结节=1
fPSA/tPSA	X4	比值<0.16=0，比值≥0.16=1
TRUS	X5	无低回声结节=0，有低回声结节=1
ADC	X6	ADC≥1=1，0.8<ABC<1=2，X<0.8=3
DCE	X7	无早期强化=0，有早期强化=1
PI-RADS评分	X8	1-2分=1，3分=2，4-5分=3

	例数	年龄[岁，(±s)]	PV [cm³，(±s)]	DER（例）		tPSA [ng/ml，M(Q)]	TRUS（例）
	例数	年龄[岁，(±s)]	PV [cm³，(±s)]	有结节	无结节	tPSA [ng/ml，M(Q)]	有结节	无结节
恶性肿瘤组	33	67.9±1.6	46.8±4.3	29	4	80.6(41.2)	25	8
非恶性肿瘤组	48	62.1±1.3	64.6±4.3	14	34	18.3(12.4)	8	40
P值		P<0.05	P<0.05	P<0.05		P=0.1	P<0.05
	ADC（例）			DCE（例）		PI-RADS（例）
	ADC≥1	0.8<ADC<1	ADC≤0.8	有动态对比增强	无动态对比增强	PI-RADS 1-2分	PI-RADS 3分	PI-RADS 4-5分
恶性肿瘤组	4	2	27	25	8	1	4	28
非恶性肿瘤组	27	18	3	14	34	12	24	12
P值	P<0.05			P<0.05		P<0.05

	例数	年龄[岁，(±s)]	PV [cm³，(±s)]	DER（例）		tPSA [ng/ml，M(Q)]	TRUS（例）
	例数	年龄[岁，(±s)]	PV [cm³，(±s)]	有结节	无结节	tPSA [ng/ml，M(Q)]	有结节	无结节
恶性肿瘤组	33	67.9±1.6	46.8±4.3	29	4	80.6(41.2)	25	8
非恶性肿瘤组	48	62.1±1.3	64.6±4.3	14	34	18.3(12.4)	8	40
P值		P<0.05	P<0.05	P<0.05		P=0.1	P<0.05
	ADC（例）			DCE（例）		PI-RADS（例）
	ADC≥1	0.8<ADC<1	ADC≤0.8	有动态对比增强	无动态对比增强	PI-RADS 1-2分	PI-RADS 3分	PI-RADS 4-5分
恶性肿瘤组	4	2	27	25	8	1	4	28
非恶性肿瘤组	27	18	3	14	34	12	24	12
P值	P<0.05			P<0.05		P<0.05

	回归系数	P值	OR值	95%置信区间
	回归系数	P值	OR值	下限	上限
fPSA/tPSA	3.485	0.002	32.608	3.529	301.309
TRUS	2.027	0.038	7.593	1.114	51.753
ADC	1.594	0.010	4.922	1.464	16.545
PI-RADS	1.732	0.034	5.653	1.143	27.962
常量	-11.118	0.000

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