Abstract:
ObjectiveTo establish a radiomic model based on SVM learning algorithm to evaluate the diagnostic efficiency in differentiating high-risk prostate cancer(PCa) from low-and medium-risk PCa.
Methods265 patients with PCa confirmed by histopathologic results were analyzed retrospectively, including 155 high risk patients and 110 low-and medium-risk patients.All patients were examined by MRI before operation.Regions of interest(ROIs)were manually delineated by two radiologists using DARWIN research platform, and the radiomic features were extracted from each segmented ROI of the T2WI and ADC images.The Receiver Operating Characteristic(ROC) curve and the area under the ROC curve(AUC) were used to validate the differential diagnosticefficiency of radiomic features, and the diagnostic performance of T2WI, ADC and T2WI+ADC were compared.
ResultsA total of 10 radiomic features were selected to identify high-risk, low-and medium-risk prostate cancer.The classification performance of the T2WI-based radiomic model was not satisfying with an AUC of 0.70(95%CI 0.63-0.77) in the training set and 0.58(95%CI 0.47-0.68) in the validation set.ADC-based model performed better with the training set achieved AUC of 0.79(95%CI 0.72-0.85) and 0.78(95%CI 0.68-0.86) of the validation set.The ensemble model constructed by both T2WI and ADC achieved the highest predictive AUCs, 0.84(95%CI 0.78-0.89) for training set and 0.80(95%CI 0.69-0.88) of the validation.
ConclusionsThe radiomic model based on T2WI and ADC maps distinguished patients with different levels of prostate cancer risk to a certain extent, which provides a non-invasive prediction method for the classification and treatment guidance of Pca.
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