摘要目的:探究基于笛卡尔采集的K空间共享三维容积快速动态成像（DISCO）和复合灵敏度编码的高分辨率扩散成像（MUSE-DWI）联合前列腺特异性抗原密度（PSAD）在前列腺癌（PCa）的诊断及危险分层中的价值。方法:回顾性收集2020年7月至2021年8月宁夏医科大学总医院183例［年龄：48~86（68±8）岁］前列腺疾病患者的资料。根据疾病情况分为非PCa组115例，PCa组68例，其中PCa组又根据危险程度分为低危PCa组14例，中高危PCa组54例。分析组间容积转移常数（Ktrans）、速率常数（Kep）、血管外细胞外体积分数（Ve）、表观扩散系数（ADC）和PSAD的差异。采用受试者工作特征（ROC）曲线评估各定量参数值及PSAD鉴别非PCa和PCa、低危PCa和中高危PCa的诊断效能。采用多因素logistic回归模型对非PCa组和PCa组间差异有统计学意义的指标进行分析，筛选出PCa的预测因子。结果:PCa组的Ktrans、Kep、Ve值和PSAD均高于非PCa组，ADC值低于非PCa组，差异均有统计学意义（均 P<0.001）；中高危PCa组的Ktrans、Kep值和PSAD均高于低危PCa组，ADC值低于低危PCa组，差异均有统计学意义（均 P<0.001）。鉴别非PCa和PCa时，联合模型（Ktrans+Kep+Ve+ADC+PSAD）的ROC曲线下面积（AUC）高于单一指标［0.958（95% CI：0.918~0.982）比0.881（95% CI：0.825~0.924）、0.836（95% CI：0.775~0.887）、0.672（95% CI：0.599~0.740）、0.940（95% CI：0.895~0.969）、0.816（95% CI：0.752~0.869），均 P<0.05］；鉴别低危PCa和中高危PCa时，联合模型（Ktrans+Kep+ADC+PSAD）的AUC高于Ktrans、Kep和PSAD［0.933（95% CI：0.845~0.979）比0.846（95% CI：0.738~0.922）、0.782（95% CI：0.665~0.873）、0.848（95% CI：0.740~0.923），均 P<0.05］。多因素logistic回归分析显示Ktrans（ OR=1.005，95% CI：1.001~1.010）和ADC值（ OR=0.992，95% CI：0.989~0.995）是PCa的预测因子（ P<0.05）。 结论:DISCO和MUSE-DWI联合PSAD可以鉴别前列腺良恶性病变，Ktrans和ADC值是PCa的预测因子；Ktrans、Kep、ADC值和PSAD有助于预测PCa的生物学行为。

abstractsObjective:To explore the value of differential subsampling with cartesian ordering (DISCO) and multiplexed sensitivity-encoding diffusion weighted-imaging (MUSE-DWI) combined with prostate specific antigen density (PSAD) in the diagnosis and risk stratification of prostate cancer (PCa).Methods:The data of 183 patients [aged from 48 to 86 (68±8) years] with prostate diseases in the General Hospital of Ningxia Medical University from July 2020 to August 2021 were retrospectively collected. Those patients were divided into non-PCa group ( n=115) and PCa group ( n=68) based on the disease condition. According to the risk degree, PCa group was subdivided into low risk PCa group ( n=14) and medium-to-high risk PCa group ( n=54). The differences of volume transfer constant (Ktrans), rate constant (Kep), extracellular volume fraction (Ve), apparent diffusion coefficient (ADC) and PSAD between groups were analyzed. Receiver operating characteristic (ROC) curves analysis were conducted for evaluating the diagnostic efficacy of quantitative parameters and PSAD in distinguishing non-PCa and PCa, low-risk PCa and medium-high risk PCa. Multivariate logistic regression model was used for screening out the predictors, which was statistically significant differences between non-PCa group and PCa group, for PCa prediction. Results:Ktrans, Kep, Ve and PSAD of PCa group all were higher than those of non-PCa group, and ADC value was lower than that of non-PCa group, and the differences all were statistically significant (all P<0.001). Ktrans, Kep and PSAD of medium-to-high risk PCa group all were higher than those of low risk PCa group, and ADC value was lower than that of low risk PCa group, and the differences were all statistically significant (all P<0.001). When distinguishing non-PCa from PCa, the area under ROC curve (AUC) of the combined model (Ktrans+Kep+Ve+ADC+PSAD) was higher than that of any single index [0.958 (95% CI: 0.918-0.982) vs 0.881 (95% CI: 0.825-0.924), 0.836 (95% CI: 0.775-0.887), 0.672 (95% CI: 0.599-0.740), 0.940(95% CI: 0.895-0.969), 0.816(95% CI:0.752-0.869), all P<0.05]. When distinguishing low-risk PCa and medium-to-high risk PCa, the AUC of the combined model (Ktrans+Kep+ADC+PSAD) were higher than those of Ktrans, Kep and PSAD[0.933 (95% CI: 0.845-0.979) vs 0.846 (95% CI:0.738-0.922), 0.782 (95% CI:0.665-0.873), 0.84 8(95% CI: 0.740-0.923), all P<0.05]. The multivariate logistic regression analysis showed that Ktrans ( OR=1.005, 95% CI:1.001-1.010) and ADC values ( OR=0.992, 95% CI:0.989-0.995) were predictors of PCa ( P<0.05). Conclusions:DISCO and MUSE-DWI combined with PSAD can distinguish benign and malignant prostate lesions. Ktrans and ADC values were predictors of PCa; Ktrans, Kep, ADC values and PSAD are helpful in predicting the biological behavior of PCa.