[The value of DISCO and MUSE-DWI combined with prostate specific antigen density in the diagnosis and risk stratification of prostate cancer].

Abstract

Objective: 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.