Application Of Nomogram Based On Multiparametric Magnetic Resonance Imaging In Prostate Cancer Prediction

Background and Objective Multiparametric magnetic resonance imaging?mp-MRI?is becoming a better choice than single imaging methods of assessing prostate cancer.The purpose of this study was to investigate the diagnostic value of various biological indicators for PCa,and to establish a nomogram model for predicting PCa,which provides a basis for clinical prediction of PCa.Materials and Methods A clear pathology of prostate biopsy or radical prostatectomy was used as a reference standard.89 cases of pathologically confirmed prostate cases in Maanshan People's Hospital were retrospectively analyzed,including 48 benign lesions and 41 cases of PCa.Antigen?PSA?and conventional MRI,diffusion-weighted imaging?DWI?and dynamic enhanced magnetic resonance imaging?DCE-MRI?,observation of lesion-related MR images,determination of PSA,prostate specific antigen density?PSAD?,apparent diffusion coefficient?ADC?Slow component(ADC_slow),ADC fast component(ADC_fast),standard ADC(ADC_standard),perfusion fraction?f?,K^trans,K_ep,V_e,prostate imaging report,and data system version 2?PI-RADSv2?score.Statistical analysis was performed using R software.Measurement data were expressed as mean±standard deviation?x±s?,and single factor comparison was performed by t test.The diagnostic threshold was determined to use a receiver operator characteristic curve?ROC?.The diagnostic value of the above biological indicators for PCa was analyzed by using two-class logistic regression?acceptance standard 0.05,exclusion criterion 0.10?,and a predictive PCa nomogram model was established.Results The PCa group was compared with the benign prostatic lesion group,with variables PSA?t=-5.29,P<0.001?,PSAD?t=-7.33,P<0.001?,ADC_slow?t=7.02,P<0.001?,ADC_standard?t=9.04,P<0.001?,K^trans?t=-10.34,P<0.001?,K_ep?t=-7.92,P<0.001?,V_e?t=-2.99,P=0.004?,and PI-RADS v2?x²=54.09,P<0.001?The difference was statistically significant.There was no significant difference between ADC_fast and f groups?P>0.05?.All of the above parameters were used as the field under the receiver operating characteristic curve?ROCAUC?:0.788,0.894,0.863,0.621,0.907,0.418,0.938,0.874,0.706,0.885;PSA,PSAD,ADC_slow,ADC_fast,ADC_standard,K^trans,K_ep,V_e,PI-RADS v2 sensitivity and specificity were 73.17%,81.25%,85.37%,79.17%,75.61%,93.75%,63.41%,66.67%,80.49%,93.75%,95.12%,83.33%,78.05%,83.33%,82.93%,56.25%,100%,77.08%.Multivariate logistic regression analysis:K^trans?OR=999.99,95%CI:11.163-999.99,P=0.015?,PI-RADS v2 score?OR=7.69,95%CI:1.719-34.398,P=0.008?for PCa The independent predictor,the concordance index?C-index?was 0.978.Conclusion:1.PSAD,ADC_slow,ADC_standard,K^trans,K_ep,V_e,PI-RADS v2 have diagnostic significance for PCa,of which Ktrans has the greatest diagnostic value;.2.K^transrans and PI-RADS v2 are independent factors for predicting PCa.The nomogram model provides a better and more accurate method to predict PCa.