瘤体-瘤周细胞外容积模型对前列腺癌的诊断价值

doi:10.3877/cma.j.issn.1674-3253.2026.01.008

目的

探讨基于瘤体（I）及瘤周（P）双区域构建的细胞外容积（ECV）模型（ECV-IP）对前列腺癌（PCa）的诊断价值，并验证瘤周ECV（ECV-P）通过调控瘤内微环境间接驱动肿瘤恶性进展的作用机制。

方法

回顾性纳入117例前列腺疾病患者（BPH 63例，PCa 54例），平均年龄（69.67±8.64）岁。比较两组影像参数包括瘤体表观扩散系数（ADC-I）、瘤周表观扩散系数（ADC-P）、瘤体ECV（ECV-I）、ECV-P、ECV-IP等，通过二元逻辑回归建模，ROC曲线（DeLong检验）、5折交叉验证评估模型诊断效能，Bootstrap中介效应模型（5 000次抽样）验证ECV-P驱动肿瘤恶性进展的机制。

结果

ECV-IP诊断PCa的ROC曲线下面积（AUC）达0.883（95%CI：0.800~0.941），灵敏度87.2%，特异度83.0%，显著优于单区域参数（ECV-I：AUC=0.854，P=0.017；ADC-I：AUC=0.797，P=0.032）、PSA（AUC=0.775，P=0.002）、ADC-IP（AUC=0.810，P<0.001）。5折交叉验证示ECV-IP在训练集（AUC=0.865，准确率85.6%）和测试集（AUC=0.889，准确率70.6%）均保持较高诊断性能。PCa组ECV-P显著低于BPH组[0.28（0.17，0.38） vs 0.36（0.25，0.49），P=0.045]，且与瘤体ADC-I呈正相关（r=0.278，P=0.007）。中介效应验证ECV-P上升1单位，ADC-I上升256.945×10^-6 mm²/s（P=0.027）；ADC-I下降100×10^-6 mm²/s，恶性风险上升（β=-0.001，P<0.001），间接效应显著，即ECV-P通过ADC-I影响PCa的路径贡献度59.2%（β=-0.294，P=0.048），而直接效应不显著（P=0.366），证实ECV-P通过下调瘤体水分子扩散间接驱动恶性进展。

结论

ECV-IP作为双区域整合指标，通过捕捉瘤体-瘤周微环境交互特征，显著提升PCa诊断效能，优于传统单区域参数及PSA；在影像学层面验证"ECV-P通过影响ADC-I而导致PCa恶性进展"的间接调控机制，为靶向肿瘤微环境提供了新型无创生物标志物。

关键词: 前列腺癌, 细胞外容积, 瘤周微环境, 表观扩散系数, 中介效应, 肿瘤微环境

Objective

To investigate the diagnostic value of an extracellular volume (ECV) model integrating intratumoral(I) and peritumoral (P) regions (ECV-IP) for prostate cancer (PCa), and to validate the mechanism by which peritumoral ECV (ECV-P) indirectly drives malignant progression via modulation of the intratumoral microenvironment.

Methods

A retrospective study was conducted on 117 patients with prostatic disease, 63 cases of benign prostatic hyperplasia (BPH), 54 cases of PCa, the age of patients was (69.67±8.64) years. Imaging parameters including intratumoral apparent diffusion coefficient (ADC-I), peritumoral apparent diffusion coefficient (ADC-P), intratumoral extracellular volume (ECV-I), ECV-P, ECV-IP were compared. Diagnostic performance was evaluated using binary logistic regression modeling, receiver operating characteristic (ROC) curves with DeLong's test, 5-fold cross-validation, and the underlying mechanism was assessed via bootstrap-mediated effect analysis (5 000 resamples).

Results

The ECV-IP model achieved an AUC of 0.883 (95%CI: 0.800-0.941) for PCa diagnosis, with a sensitivity of 87.2% and a specificity of 83.0%. It demonstrated significantly superior diagnostic performance compared to single-region parameters (ECV-I: AUC=0.854, P=0.017; ADC-I: AUC=0.797, P=0.032), PSA (AUC=0.775, P=0.002), and the ADC-IP model (AUC=0.810, P<0.001). Cross-validation demonstrated robust performance: training set (AUC=0.865, accuracy=85.6%); test set (AUC=0.889, accuracy=70.6%). ECV-P was significantly lower in PCa than BPH [0.28 (0.17, 0.38) vs 0.36 (0.25, 0.49), P=0.045] and showed a positive correlation with intratumoral ADC-I (r=0.278, P=0.007). A 1-unit increase in ECV-P increased ADC-I by 256.945×10^-6mm²/s (P=0.027). A decrease of 100×10^-6 mm²/s in ADC-I increased malignant risk (β=-0.001, P<0.001).The indirect effect of ECV-P on PCa progression mediated by ADC-I was significant (β=-0.294, 95%CI: -0.626 to -0.049, P=0.048; proportion mediated: 59.2%). The direct effect of ECV-P on PCa was non-significant (P=0.366). This validates that ECV-P indirectly drives malignant progression of tumors by down-regulating the diffusion of water molecules.

Conclusion

The ECV-IP model, as a dual-region approach, significantly enhances PCa diagnostic accuracy over conventional single-region parameters and PSA.This study provides the first imaging-based validation of the "ECV-P leads to the malignant progression of PCa by influencing ADC-I" indirect regulatory mechanism, establishing ECV-IP as a novel non-invasive biomarker for targeting the prostate cancer tumor microenvironment.

Key words: Prostate cancer, Extracellular volume, Peritumoral microenvironment, Apparent diffusion coefficient, Mediation effect, Tumor microenvironment

图1 瘤体-瘤周双区域弥散加权模型（ADC-IP）和双区域细胞外容积模型（ECV-IP）勾画感兴趣区域策略及案例展示注：图a~e为同一BPH患者（年龄65岁）动态增强5 min时相、平扫（蒙片）、弥散加权表观扩散系数（ADC）图、动态增强曲线、病理图（HE×400）；图f~j为同一PCa患者（年龄61岁）动态增强5 min时相、平扫（蒙片）、弥散加权ADC图、动态增强曲线、病理图（HE×400）；基于勾画参数和公式，BPH患者ADC-I、ADC-P高于PCa患者，BPH患者ECV-I（0.54）、ECV-P（0.36）高于PCa患者ECV-I（0.24）、ECV-P（0.31）；动态增强曲线BPH患者呈流入型，PCa患者呈流出型，均与病理结果一致；ADC-I和ADC-P分别为瘤体、瘤周弥散加权系数，ECV-I、ECV-P分别为瘤体、瘤周细胞外容积

表1 117例前列腺疾病患者基线特征

变量	例数	PI-RADS[例(%)]				时间强度曲线[例(%)]			解剖分区[例(%)]		是否多发[例(%)]
变量	例数	2分	3分	4分	5分	上升	平台	廓清	外周带	移行带	否	是
BPH组	63	20 (31.75)	20 (31.75)	18 (28.57)	5 (7.94)	22 (34.92)	24 (38.10)	17 (26.98)	26 (41.27)	37 (58.73)	56 (88.89)	7 (11.11)
PCa组	54	2 (3.70)	7 (12.96)	22 (40.74)	23 (42.59)	4 (7.41)	20 (37.04)	30 (55.56)	32 (59.26)	22 (40.74)	49 (90.74)	5 (9.26)
χ²值		χ²=32.458				χ²=15.822			χ²=3.764		χ²=0.108
P值		<0.001				<0.001			0.052		0.742
变量	PSA[ng/ml，M(Q)]	TTP[s，M(Q)]	Hct(±s)	BMI[kg/m²，(±s)]	年龄[岁，(±s)]	体积[cm³，(±s)]	ISUP分级[例(%)]
变量	PSA[ng/ml，M(Q)]	TTP[s，M(Q)]	Hct(±s)	BMI[kg/m²，(±s)]	年龄[岁，(±s)]	体积[cm³，(±s)]	1	2	3	4	5
BPH组	7.75 (5.26，12.65)	90 (65，146)	0.42±0.04	23.64±3.02	69.08±8.94	0.05 (0.04，0.08)
PCa组	29.37 (8.56，70.26)	60 (47，81)	0.41±0.04	23.24±2.67	70.39±8.21	0.03 (0.01，0.05)	7 (12.96)	8 (14.82)	6 (11.11)	22 (40.74)	11 (20.37)
统计值	Z=-4.965	Z=3.997	t=0.842	t=0.736	t=-0.801	Z=3.953	-
P值	<0.001	<0.001	0.402	0.463	0.425	<0.001	-

表1 117例前列腺疾病患者基线特征

变量	例数	PI-RADS[例(%)]				时间强度曲线[例(%)]			解剖分区[例(%)]		是否多发[例(%)]
变量	例数	2分	3分	4分	5分	上升	平台	廓清	外周带	移行带	否	是
BPH组	63	20 (31.75)	20 (31.75)	18 (28.57)	5 (7.94)	22 (34.92)	24 (38.10)	17 (26.98)	26 (41.27)	37 (58.73)	56 (88.89)	7 (11.11)
PCa组	54	2 (3.70)	7 (12.96)	22 (40.74)	23 (42.59)	4 (7.41)	20 (37.04)	30 (55.56)	32 (59.26)	22 (40.74)	49 (90.74)	5 (9.26)
χ²值		χ²=32.458				χ²=15.822			χ²=3.764		χ²=0.108
P值		<0.001				<0.001			0.052		0.742
变量	PSA[ng/ml，M(Q)]	TTP[s，M(Q)]	Hct(±s)	BMI[kg/m²，(±s)]	年龄[岁，(±s)]	体积[cm³，(±s)]	ISUP分级[例(%)]
变量	PSA[ng/ml，M(Q)]	TTP[s，M(Q)]	Hct(±s)	BMI[kg/m²，(±s)]	年龄[岁，(±s)]	体积[cm³，(±s)]	1	2	3	4	5
BPH组	7.75 (5.26，12.65)	90 (65，146)	0.42±0.04	23.64±3.02	69.08±8.94	0.05 (0.04，0.08)
PCa组	29.37 (8.56，70.26)	60 (47，81)	0.41±0.04	23.24±2.67	70.39±8.21	0.03 (0.01，0.05)	7 (12.96)	8 (14.82)	6 (11.11)	22 (40.74)	11 (20.37)
统计值	Z=-4.965	Z=3.997	t=0.842	t=0.736	t=-0.801	Z=3.953	-
P值	<0.001	<0.001	0.402	0.463	0.425	<0.001	-

表2 前列腺疾病患者核心指标与病理结果皮尔逊相关性分析

指标	PSA	ADC-I	ADC-P	ECV-I	ECV-P	SER-I	SER-P	MSI-I	MSI-P	MSD-I	MSD-P	ADC-IP	ECV-IP
r值	0.314	-0.503	-0.09	-0.567	-0.28	0.277	0.086	0.167	0.045	0.259	0.142	0.533	0.648
P值	0.002	<0.001	0.393	<0.001	0.007	0.007	0.416	0.111	0.672	0.013	0.176	<0.001	<0.001
ICC	-	0.913	0.922	0.932	0.912	0.914	0.918	0.916	0.928	0.922	0.917	-	-

表2 前列腺疾病患者核心指标与病理结果皮尔逊相关性分析

指标	PSA	ADC-I	ADC-P	ECV-I	ECV-P	SER-I	SER-P	MSI-I	MSI-P	MSD-I	MSD-P	ADC-IP	ECV-IP
r值	0.314	-0.503	-0.09	-0.567	-0.28	0.277	0.086	0.167	0.045	0.259	0.142	0.533	0.648
P值	0.002	<0.001	0.393	<0.001	0.007	0.007	0.416	0.111	0.672	0.013	0.176	<0.001	<0.001
ICC	-	0.913	0.922	0.932	0.912	0.914	0.918	0.916	0.928	0.922	0.917	-	-

表3 BPH组与PCa组核心指标比较[M（Q）]

组别	例数	ADC-I	ADC-P	ECV-I	ECV-P	SER-I	SER-P
BPH组	63	890 (748，996)	1210.94 (1101.12，1351.21)	0.37 (0.24，0.47)	0.36 (0.25，0.49)	2.00 (1.00，4.20)	3.27 (1.89，5.89)
PCa组	54	571 (493，735)	1121.41 (992.14，1292.01)	0.15 (0.06，0.23)	0.28 (0.17，0.38)	4.50 (2.75，9.67)	3.59 (2.07，5.92)
Z值		6.061	2.006	5.669	2.009	-3.523	-0.196
P值		<0.001	0.045	<0.001	0.045	<0.001	0.847
组别	MSI-I	MSI-P	MSD-I	MSD-P	ACD-IP	ECV-IP
BPH组	234.13 (135.78，365.10)	147.59 (88.57，230.02)	37.44 (20.67，64.11)	33.39 (19.46，49.67)	0.30 (0.12，0.51)	0.26 (0.09，0.38)
PCa组	286.78 (150.00，468.00)	169.25 (81.99，275.73)	57.00 (25.34，90.75)	30.97 (19.78，50.11)	0.68 (0.49，0.82)	0.64 (0.54，0.78)
Z值	-1.636	-0.650	-2.194	-0.289	-5.744	-6.346
P值	0.103	0.517	0.028	0.775	<0.001	<0.001

表3 BPH组与PCa组核心指标比较[M（Q）]

组别	例数	ADC-I	ADC-P	ECV-I	ECV-P	SER-I	SER-P
BPH组	63	890 (748，996)	1210.94 (1101.12，1351.21)	0.37 (0.24，0.47)	0.36 (0.25，0.49)	2.00 (1.00，4.20)	3.27 (1.89，5.89)
PCa组	54	571 (493，735)	1121.41 (992.14，1292.01)	0.15 (0.06，0.23)	0.28 (0.17，0.38)	4.50 (2.75，9.67)	3.59 (2.07，5.92)
Z值		6.061	2.006	5.669	2.009	-3.523	-0.196
P值		<0.001	0.045	<0.001	0.045	<0.001	0.847
组别	MSI-I	MSI-P	MSD-I	MSD-P	ACD-IP	ECV-IP
BPH组	234.13 (135.78，365.10)	147.59 (88.57，230.02)	37.44 (20.67，64.11)	33.39 (19.46，49.67)	0.30 (0.12，0.51)	0.26 (0.09，0.38)
PCa组	286.78 (150.00，468.00)	169.25 (81.99，275.73)	57.00 (25.34，90.75)	30.97 (19.78，50.11)	0.68 (0.49，0.82)	0.64 (0.54，0.78)
Z值	-1.636	-0.650	-2.194	-0.289	-5.744	-6.346
P值	0.103	0.517	0.028	0.775	<0.001	<0.001

表4 各指标诊断前列腺癌的受试者工作特征曲线分析

指标	AUC值（95%CI）	灵敏度（95%CI）	特异度（95%CI）	约登指数（95%CI）
PSA	0.775 (0.658~0.847)	0.641 (0.401~0.842)	0.830 (0.599~0.981)	0.471 (0.364~0.619)
ADC-I	0.797 (0.712~0.905)	0.792 (0.717~0.977)	0.744 (0.527~0.907)	0.536 (0.408~0.779)
ADC-P	0.548 (0.396~0.648)	0.849 (0.146~0.976)	0.282 (0.108~0.973)	0.131 (0.055~0.278)
ECV-I	0.854 (0.787~0.928)	0.755 (0.560~0.906)	0.846 (0.711~0.977)	0.601 (0.500~0.784)
ECV-P	0.674 (0.541~0.786)	0.491 (0.330~0.896)	0.846 (0.477~0.964)	0.337 (0.191~0.482)
SER-I	0.720 (0.636~0.824)	0.795 (0.629~0.922)	0.642 (0.568~0.811)	0.436 (0.299~0.600)
SER-P	0.528 (0.431~0.626)	0.462 (0.092~0.959)	0.660 (0.155~0.991)	0.122 (0.057~0.293)
MSI-I	0.568 (0.468~0.684)	0.231 (0.228~1.000)	0.943 (0.146~0.981)	0.174 (0.126~0.359)
MSI-P	0.524 (0.404~0.619)	0.385 (0.153~1.000)	0.774 (0.088~0.972)	0.158 (0.056~0.304)
MSD-I	0.627 (0.552~0.723)	0.282 (0.225~0.926)	0.943 (0.295~1.000)	0.225 (0.181~0.385)
MSD-P	0.517 (0.392~0.575)	0.179 (0.093~0.883)	0.981 (0.253~1.000)	0.161 (0.068~0.267)
ADC-IP	0.810 (0.740~0.896)	0.641 (0.541~0.835)	0.925 (0.819~0.996)	0.566 (0.441~0.721)
ECV-IP	0.883 (0.800~0.941)	0.872 (0.714~0.978)	0.830 (0.691~0.964)	0.702 (0.546~0.845)
指标	最佳阈值（95%CI）	准确度（95%CI）	Z值	P值
PSA	18.791 (7.876~39.599)	0.585 (0.492~0.693)	1.734	0.002
ADC-I	748.000 (651.000~750.000)	0.424 (0.320~0.519)	10.527	0.032
ADC-P	1059.070 (857.990~1503.470)	0.432 (0.337~0.539)	5.464	<0.001
ECV-I	0.248 (0.184~0.328)	0.805 (0.717~0.886)	10.242	0.017
ECV-P	0.403 (0.250~0.461)	0.650 (0.529~0.737)	7.691	<0.001
SER-I	2.750 (2.750~4.462)	0.584 (0.502~0.667)	2.506	0.012
SER-P	4.310 (1.269~16.604)	0.573 (0.470~0.650)	5.300	<0.001
MSI-I	538.500 (100.750~603.143)	0.576 (0.470~0.671)	4.011	<0.001
MSI-P	241.130 (54.920~331.460)	0.570 (0.481~0.661)	4.551	<0.001
MSD-I	90.750 (20.536~131.170)	0.573 (0.470~0.663)	3.356	0.001
MSD-P	79.320 (19.780~93.726)	0.563 (0.472~0.680)	4.459	<0.001
ADC-IP	0.614 (0.450 ~0.647)	0.810 (0.742~0.878)	1.260	<0.001
ECV-IP	0.465 (0.379~0.576)	0.865 (0.782~0.913)	-	-

表4 各指标诊断前列腺癌的受试者工作特征曲线分析

指标	AUC值（95%CI）	灵敏度（95%CI）	特异度（95%CI）	约登指数（95%CI）
PSA	0.775 (0.658~0.847)	0.641 (0.401~0.842)	0.830 (0.599~0.981)	0.471 (0.364~0.619)
ADC-I	0.797 (0.712~0.905)	0.792 (0.717~0.977)	0.744 (0.527~0.907)	0.536 (0.408~0.779)
ADC-P	0.548 (0.396~0.648)	0.849 (0.146~0.976)	0.282 (0.108~0.973)	0.131 (0.055~0.278)
ECV-I	0.854 (0.787~0.928)	0.755 (0.560~0.906)	0.846 (0.711~0.977)	0.601 (0.500~0.784)
ECV-P	0.674 (0.541~0.786)	0.491 (0.330~0.896)	0.846 (0.477~0.964)	0.337 (0.191~0.482)
SER-I	0.720 (0.636~0.824)	0.795 (0.629~0.922)	0.642 (0.568~0.811)	0.436 (0.299~0.600)
SER-P	0.528 (0.431~0.626)	0.462 (0.092~0.959)	0.660 (0.155~0.991)	0.122 (0.057~0.293)
MSI-I	0.568 (0.468~0.684)	0.231 (0.228~1.000)	0.943 (0.146~0.981)	0.174 (0.126~0.359)
MSI-P	0.524 (0.404~0.619)	0.385 (0.153~1.000)	0.774 (0.088~0.972)	0.158 (0.056~0.304)
MSD-I	0.627 (0.552~0.723)	0.282 (0.225~0.926)	0.943 (0.295~1.000)	0.225 (0.181~0.385)
MSD-P	0.517 (0.392~0.575)	0.179 (0.093~0.883)	0.981 (0.253~1.000)	0.161 (0.068~0.267)
ADC-IP	0.810 (0.740~0.896)	0.641 (0.541~0.835)	0.925 (0.819~0.996)	0.566 (0.441~0.721)
ECV-IP	0.883 (0.800~0.941)	0.872 (0.714~0.978)	0.830 (0.691~0.964)	0.702 (0.546~0.845)
指标	最佳阈值（95%CI）	准确度（95%CI）	Z值	P值
PSA	18.791 (7.876~39.599)	0.585 (0.492~0.693)	1.734	0.002
ADC-I	748.000 (651.000~750.000)	0.424 (0.320~0.519)	10.527	0.032
ADC-P	1059.070 (857.990~1503.470)	0.432 (0.337~0.539)	5.464	<0.001
ECV-I	0.248 (0.184~0.328)	0.805 (0.717~0.886)	10.242	0.017
ECV-P	0.403 (0.250~0.461)	0.650 (0.529~0.737)	7.691	<0.001
SER-I	2.750 (2.750~4.462)	0.584 (0.502~0.667)	2.506	0.012
SER-P	4.310 (1.269~16.604)	0.573 (0.470~0.650)	5.300	<0.001
MSI-I	538.500 (100.750~603.143)	0.576 (0.470~0.671)	4.011	<0.001
MSI-P	241.130 (54.920~331.460)	0.570 (0.481~0.661)	4.551	<0.001
MSD-I	90.750 (20.536~131.170)	0.573 (0.470~0.663)	3.356	0.001
MSD-P	79.320 (19.780~93.726)	0.563 (0.472~0.680)	4.459	<0.001
ADC-IP	0.614 (0.450 ~0.647)	0.810 (0.742~0.878)	1.260	<0.001
ECV-IP	0.465 (0.379~0.576)	0.865 (0.782~0.913)	-	-

表5 ECV-IP、ADC-IP和PSA三个模型交叉验证结果

指标	数据集	AUC	截断值	准确率	敏感性	特异性	阳性率	阴性率	F1分数
ECV-IP	训练集	0.865	0.472	0.856	0.846	0.863	0.825	0.881	0.835
ADC-IP	训练集	0.808	0.548	0.778	0.649	0.891	0.84	0.745	0.731
PSA	训练集	0.785	0.427	0.766	0.688	0.834	0.786	0.756	0.731
ECV-IP	测试集	0.889	0.475	0.706	0.500	0.889	0.800	0.667	0.615
ADC-IP	测试集	0.921	0.561	0.813	0.714	0.889	0.833	0.800	0.769
PSA	测试集	0.650	0.392	0.765	0.400	0.917	0.667	0.786	0.500

表5 ECV-IP、ADC-IP和PSA三个模型交叉验证结果

指标	数据集	AUC	截断值	准确率	敏感性	特异性	阳性率	阴性率	F1分数
ECV-IP	训练集	0.865	0.472	0.856	0.846	0.863	0.825	0.881	0.835
ADC-IP	训练集	0.808	0.548	0.778	0.649	0.891	0.84	0.745	0.731
PSA	训练集	0.785	0.427	0.766	0.688	0.834	0.786	0.756	0.731
ECV-IP	测试集	0.889	0.475	0.706	0.500	0.889	0.800	0.667	0.615
ADC-IP	测试集	0.921	0.561	0.813	0.714	0.889	0.833	0.800	0.769
PSA	测试集	0.650	0.392	0.765	0.400	0.917	0.667	0.786	0.500

图2 ECV-P影响PCa进展的中介分析路径图注：ECV-P通过中介变量ADC-I间接推动恶性进展，间接效应比例59.2%，揭示了瘤周微环境（细胞外容积）通过瘤体水分子受限（细胞密度）对前列腺恶性进展的潜在作用

表6 ECV-P影响PCa进展的中介效应分析

路径	系数（β）	标准误	P值	95%CI
ECV-P → ADC-I	256.945	114.567	0.027	29.780~484.111
ADC-I → PCa	-0.001	0.000	<0.001	-0.002~-0.001
总效应	-0.497	0.254	0.053	-1.000~0.006
直接效应	-0.203	0.224	0.366	-0.646~0.240
间接效应	-0.294	0.146	0.048	-0.626~-0.049

表6 ECV-P影响PCa进展的中介效应分析

路径	系数（β）	标准误	P值	95%CI
ECV-P → ADC-I	256.945	114.567	0.027	29.780~484.111
ADC-I → PCa	-0.001	0.000	<0.001	-0.002~-0.001
总效应	-0.497	0.254	0.053	-1.000~0.006
直接效应	-0.203	0.224	0.366	-0.646~0.240
间接效应	-0.294	0.146	0.048	-0.626~-0.049

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Diagnostic value of an intratumor-peritumor extracellular volume model in prostate cancer

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Diagnostic value of an intratumor-peritumor extracellular volume model in prostate cancer