Purpose: To assess the performance of Support Vector Machines (SVM) classification to stratify the Gleason Score (GS) of prostate cancer (PCa) in the central gland (CG) based on image features across multiparametric magnetic resonance imaging (mpMRI).
Materials and methods: This retrospective study was approved by the institutional review board, and informed consent was waived. One hundred fifty-two CG cancerous ROIs were identified through radiological-pathological correlation. Eleven parameters were derived from the mpMRI and histogram analysis, including mean, median, the 10th percentile, skewness and kurtosis, was performed for each parameter. In total, fifty-five variables were calculated and processed in the SVM classification. The classification model was developed with 10-fold cross-validation and was further validated mutually across two separated datasets.
Results: With six variables selected by a feature-selection and variation test, the prediction model yielded an area under the receiver operating characteristics curve (AUC) of 0.99 (95% CI: 0.98, 1.00) when trained in dataset A2 and 0.91 (95% CI: 0.85, 0.95) for the validation in dataset B2. When the data sets were reversed, an AUC of 0.99 (95% CI: 0.99, 1.00) was obtained when the model was trained in dataset B2 and 0.90 (95% CI: 0.85, 0.95) for the validation in dataset A2.
Conclusion: The SVM classification based on mpMRI derived image features obtains consistently accurate classification of the GS of PCa in the CG.
Keywords: Diffusion; Multiparametric magnetic resonance imaging (mpMRI); Perfusion; Prostate cancer; Support Vector Machines.
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