Predictive value of 18F-PSMA-1007 PET/CT for postoperative pathologic upgrading in patients with biopsy ISUP grade 1-2 prostate cancer

doi:10.3877/cma.j.issn.1674-3253.2025.04.008

Abstract

Abstract:

Objective

To investigate the diagnostic value of ¹⁸F-prostate-specific membrane antigen (PSMA)-1007 PET/CT for postoperative upgrading in patients with biopsy International Society of Urological Pathology (ISUP) grade 1-2 prostate cancer.

Method

Clinical and pathologic information were retrospectively collected from October 2020 to August 2023 for 73 patients with biopsy ISUP grade 1-2 prostate cancer who underwent radical prostatectomy (RP) at the First Affiliated Hospital of Xi'an Jiaotong University. Differences in ¹⁸F-PSMA-1007 PET/CT and clinical parameters were compared between the ISUP upgrading and ISUP non-upgrading groups. Univariate and multivariate logistic regression analyses were used to select the predictors of ¹⁸F-PSMA-1007 PET/CT and clinical parameters for predicting postoperative pathologic upgrading. A diagnostic prediction nomogram was constructed. Receiver operating characteristic (ROC) curve and decision curve were plotted to evaluate the predictive performance and clinical benefit of the model.

Results

Among 73 included prostate cancer patients, ISUP upgrading occurred in 44(60.3%) cases. Serum prostate-specific antigen (PSA) level, the maximum standardized uptake values (SUV_max) and mean standardized uptake values (SUV_mean) were significantly higher in the ISUP upgrading group than those in the ISUP non-upgrading group (all P<0.05). Age, metabolic tumor volume (MTV), and total lesion uptake (TLU) had no statistically significant differences between the two groups (all P>0.05). The univariate logistic regression analysis showed that PSA level, biopsy ISUP grade group, and SUV_mean could influence the postoperative upgrading. And the multivariate logistic regression analysis indicated that biopsy ISUP grade group and SUV_mean were independent predictors of ISUP upgrading in postoperative prostate cancer. The combined model for predicting postoperative ISUP upgrading was higher than that of biopsy ISUP grading group and SUV_mean (0.851 vs 0.623, 0.728, P=0.008, P=0.210), but there was no significantly statistical difference in the latter. Decision curve analysis showed that when the threshold probability was greater than 47%, the combined model was beneficial in clinical application.

Conclusion

¹⁸F-PSMA-1007 PET/CT is promising for diagnosing postoperative pathologic upgrading in patients with biopsy ISUP grade 1-2 prostate cancer.

Key words: Prostate cancer, Prostate-specific membrane antigen, Positron-emission tomography, Pathological upgrading, International Society of Urological Pathology (ISUP)

Yang Li, Liang Luo, Ruxi Chang, Weixuan Dong, Bo Wang, Kaijie Wu, Xiaoyi Duan. Predictive value of ¹⁸F-PSMA-1007 PET/CT for postoperative pathologic upgrading in patients with biopsy ISUP grade 1-2 prostate cancer[J]. Chinese Journal of Endourology(Electronic Edition), 2025, 19(04): 441-446.

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References 25

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项目	指标
年龄[岁，(±s)]	68±6
PSA [ng/mL，M(Q)]	16.2（10.2~27.7）
SUV_max[M(Q)]	13.8（9.4~20.6）
SUV_mean[M(Q)]	5.8（4.4~7.7）
MTV[cm³，M(Q)]	17.8（10.4~27.4）
TLU[cm³，M(Q)]	105.4（56.1~168.4）
ISUP升级情况[例(%)]
升级组	44（60.3%）
未升级组	29（39.7%）

项目	指标
年龄[岁，(±s)]	68±6
PSA [ng/mL，M(Q)]	16.2（10.2~27.7）
SUV_max[M(Q)]	13.8（9.4~20.6）
SUV_mean[M(Q)]	5.8（4.4~7.7）
MTV[cm³，M(Q)]	17.8（10.4~27.4）
TLU[cm³，M(Q)]	105.4（56.1~168.4）
ISUP升级情况[例(%)]
升级组	44（60.3%）
未升级组	29（39.7%）

		穿刺病理
		ISUP 1级	ISUP 2级
术后病理	ISUP 1级	8	-
	ISUP 2级	12	21
	ISUP 3级	8	15
	ISUP 4级	2	4
	ISUP 5级	1	2

		穿刺病理
		ISUP 1级	ISUP 2级
术后病理	ISUP 1级	8	-
	ISUP 2级	12	21
	ISUP 3级	8	15
	ISUP 4级	2	4
	ISUP 5级	1	2

组别	例数	年龄[岁，(±s)]	PSA[ng/mL，M(Q)]	SUV_max [M(Q)]	SUV_mean [M(Q)]	MTV[cm³，M(Q)]	TLU[cm³，M(Q)]
ISUP升级组	44	68±6	18.8(10.7，32.4)	15.9(12.2，22.6)	6.4(5.1，8.0)	17.5(10.1，25.5)	121.4(58.5，185.8)
ISUP未升级组	29	70±6	13.3(8.0，21.9)	11.4(6.3，15.9)	4.4(3.7，6.6)	19.3(10.4，35.5)	96.4(49.5，155.1)
统计值		t=1.267	Z=-2.119	Z=-2.689	Z=-3.281	Z=-0.614	-0.891
P值		0.209	0.034	0.007	0.001	0.539	0.373

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因素	单因素回归		多因素回归
因素	OR(95%CI)	P值	OR(95%CI)	P值
年龄(岁)	0.95(0.88~1.03)	0.209	-	-
PSA(ng/mL)	1.04(1.01~1.08)	0.047	1.04(0.99~1.09)	0.087
穿刺ISUP分级分组	0.35(0.13~0.95)	0.040	0.13(0.04~0.47)	0.002
SUV_max	1.05(1.00~1.11)	0.065	-	-
SUV_mean	1.50(1.14~1.98)	0.004	1.61(1.17~2.21)	0.003
MTV(cm³)	0.98(0.95~1.01)	0.274	-	-
TLU(cm³)	1.00(1.00~1.01)	0.290	-	-

	AUC(95%CI)	截断值	灵敏度(%)	特异度(%)
穿刺ISUP分级分组	0.623(0.502~0.734)	1	72.4	52.3
SUV_mean	0.728(0.611~0.826)	4.45	90.9	55.2
联合模型	0.851(0.748~0.924)	0.47	68.2	96.6

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因素	单因素回归		多因素回归
因素	OR(95%CI)	P值	OR(95%CI)	P值
年龄(岁)	0.92(0.80~1.05)	0.226	-	-
PSA(ng/mL)	1.02(0.95~1.09)	0.551	-	-
SUV_max	1.53(1.09~2.13)	0.013	-	-
SUV_mean	3.95(1.37~11.37)	0.011	3.70(1.25~10.94)	0.018
MTV(cm³)	0.94(0.89~1.00)	0.048	0.98(0.92~1.04)	0.503
TLU(cm³)	1.00(0.99~1.01)	0.758	-	-

因素	单因素回归		多因素回归
因素	OR(95%CI)	P值	OR(95%CI)	P值
年龄(岁)	0.92(0.80~1.05)	0.226	-	-
PSA(ng/mL)	1.02(0.95~1.09)	0.551	-	-
SUV_max	1.53(1.09~2.13)	0.013	-	-
SUV_mean	3.95(1.37~11.37)	0.011	3.70(1.25~10.94)	0.018
MTV(cm³)	0.94(0.89~1.00)	0.048	0.98(0.92~1.04)	0.503
TLU(cm³)	1.00(0.99~1.01)	0.758	-	-

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