Abstract:
ObjectiveTo explore the value of mp-MRI radiomics in predicting the Gleason grading of prostate cancer.
MethodsThe clinical data of 266 prostate cancer patients were retrospectively analyzed, and the patients were divided into the Gleason score high-risk group(Gleason ≥ 4+3 points) and low-risk group(Gleason ≤ 3+4 points) according to the pathological results.After the lesions were manually delineated on the T2WI cross-section and ADC map(b value 0, 1 500 s/mm2), the image features were extracted and quantified.The data characteristics of cases were randomly divided into the training group and test group(test set ratio for 0.3), and the support vector machine classification model was constructed to obtain the ROC curve and area under the curve(AUC) of training group and test group.
ResultsThere were 118 cases in the Gleason score high-risk group and 148 cases in the Gleason score low-risk group, and there were 186 cases in the training group(including 83 cases in the high-risk group and 103 cases in the low-risk group) and 80 cases in the test group(including 35 cases in the high-risk group and 45 cases in the low-risk group).The AUC value of the training group and test group in the T2WI and ADC radiomics support vector machine model were 0.753 and 0.741, respectively, and the accuracy rate of test group was 62.5%(95%CI: 0.572-0.893).The AUC value of the training group and test group in the T2WI, ADC radiomics combined with PSA value support vector machine model were 0.768 and 0.752, respectively, and the accuracy rate of test group was 72.5%(95%CI: 0.613-0.917).The results of Delong verification showed that the difference between the two was not statistically significant(P>0.05).
ConclusionsThe mp-MRI radiomics has a high reference value in predicting the Gleason grading of prostate cancer.
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