To analyze the value of radiomics based on different diffusion model parameter maps in the early diagnosis of clinically significant prostate cancer by magnetic resonance imaging
Objective:To explore the predictive value of radiomics analysis basedon magnetic resonance single-index and diffusion kurtosis model functional parameter maps for clinically significant prostate cancer(csPCa).Materials and Methods:A retrospective analysis was conducted on 238 prostate patients who visited Ma'anshan People's Hospital from April 2022 to July 2023.They were confirmed by ultrasound-guided puncture or surgical pathology,including 96 csPCa patients and 142 non-csPCa patients.The age of the patients 56-84(62.34±7.62)years old.The Clinical data within and between the groups were compared.All patients underwent magnetic resonance multi-parameter scanning,after post-processing,the apparent diffusion coefficient(ADC)pseudo-color plots were generated,and the mean kurtosis(MK)and mean diffusivty(MD)pseudo-color plots in the diffusion kurtosis model were obtained.After image preprocessing,the image features of eachfunctional parameter map are extracted.There are a total of 1 056 radiomics features.The maximum correlation minimum redundancy(MRMR)algorithm and least absolute shrinkage and selection operator(LASSO)are used to eliminateredundancy,perform feature dimensionality reduction,and retain high-quality labels for the data of ADC,MD,and MK models.For relevant features,10-foldcross-validation was applied to obtain a feature subset,and 238 patients were randomly divided into groups in a ratio of 7∶3.Finally,the ADC model screened out 5 omics features,and the MD model screened out 6 omics features.The MK model screened out 6 omics features,established alogistic regression model,calculated the threshold,accuracy,sensitivity,and specificity of the clinical models,radiology,and clinical-radiology models,and drew the receiver operating characteristic(ROC)curve.Calculate the area under the curve(AUC)and 95%confidence interval(CI),use the DeLong test to combine each model in pairs,compare whether the AUC values between the two groups are statistically significant,and further use decision curve analysis(DCA)to evaluate model performance.Results:The AUC,specificity and sensitivity of the clinical model in the training set were 0.840(95%CI:0.778-0.901),78.7%and 76.8%,and in the test set were 0.675(95%CI:0.539-0.812),79.0%and 59.2%,respectively.The AUC,specificity and sensitivity of the ADC model in the training set were 0.927(95%CI:0.890-0.964),81.9%,86.9%,and in the test set were 0.909(95%CI:0.835-0.983),90.6%,84.1%,respectively;the AUC,specificity and sensitivity of the MD model in the trainingset were 0.934(95%CI:0.899-0.969),85.1%,84.0%,and in the test set were 0.960(95%CI:0.910-1.000),93.0%,85.1%,respectively;the AUC,specificity and sensitivity of the MK model in the training set were 0.935(95%CI:0.900-0.971),90.4%,84.0%,and in the test set were 0.856(95%CI:0.770-0.941),81.3%,66.6%,respectively.The AUC,specificity and sensitivity of the clinical-radiology model in the training set were 0.946(95%CI:0.912-0.980),88.2%and 89.8%,and in the test set were 0.963(95%CI:0.925-1.000),93.0%and 85.1%,respectively.DeLong test results showed that there was no significant difference between the radiology model and the clinical-radiology combined model(P>0.05).There was a significant difference in AUC value between the clinical model and the other two models(Z= 2.836,P=0.004),and there was no significant difference between the other two groups of models(P>0.05).The decision curve shows that the threshold probability of each model is in the range of 0.1-1.0,which has a net benefit for clinical practice.Different models have a positive effect on the diagnosis of csPCa.The clinical-radiology model having the highest diagnostic performance.Conclusions:The radiomics analysis technology of MRI mono-exponential and diffusion kurtosis model functional parameter map is an effective method for the detection of csPCa.The clinical-radiology combined model has high diagnostic value for csPCa,which can provide relevant technical support for early clinical diagnosis and treatment.
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