Value Of Biparametric MRI Texture Analysis For Predicting High-Grade Prostate Cancer

Objective:To explore the potential value of biparametric MRI texture analysis?TA?combined with prostate-related biomarkers to predict high-grade prostate cancer?HGPCa?.Methods:Eight-five patients who underwent biparametric MRI scanning,including T₂-weighted imaging?T₂WI?and diffusion-weighted imaging?DWI?prior to trans-rectal ultrasound?TRUS?-guided core prostate biopsy or prostatectomy,were retrospectively enrolled.TA parameters derived from biparametric MRI,prostate-specific antigen?PSA?,and free PSA?fPSA?were compared between the HGPCa and non-high-grade prostate cancer?NHGPCa?groups using independent Student's t-test and the Mann-Whitney U test.Logistic regression and receiver operating characteristic?ROC?curve analyses were performed to assess the predictive value for HGPCa.Sensitivity,specificity and cutoff values of individual and combined predictors were calculated.Results:Univariate analysis showed that PSA and kurtosis,skewness,entropy based on apparent diffusion coefficient?ADC?map,correlation based on T₂WI differed significantly between the HGPCa and NHGPCa groups,which entropy based on ADC map and PSA showed higher diagnostic values for HGPCa?area under the curve?AUC?=82.0%and80.0%,respectively?.Logistic regression analysis further showed that kurtosis,skewness,entropy,uniformity based on ADC map and entropy based on T₂WI were independent predictors of HGPCa,but ROC curve analysis showed that uniformity based on ADC map and entropy based on T₂WI had no prediction efficiency for HGPCa.Finally,kurtosis,skewness and entropy of the ADC map were used to predict HGPCa,and the entropy of ADC map showed a good prediction efficiency?AUC=80.0%;95%confidence interval?CI?:0.700,0.890;P=0.000?.when kurtosis,skewness and entropy of the ADC map were combined,the area under the ROC curve reached the maximum?AUC=84.6%;95%CI:0.758,0.935;P=0.000?.Conclusion:Textural parameters derived from bpMRI and PSA,fPSA can reflect biologic aggressiveness;In addition,TA parameters derived from ADC play a primary role in predicting HGPCa.The combination of specific textural parameters extracted from ADC map may be additional tools to predict HGPCa.