Multimodal classification of prostate tissue: a feasibility study on combining multiparametric MRI and ultrasound

The common practice for biopsy guidance is through transrectal ultrasound, with the fusion of ultrasound and MRI-based targets when available. However, ultrasound is only used as a guidance modality in MR-targeted ultrasound-guided biopsy, even though previous work has shown the potential utility of ultrasound, particularly ultrasound vibro-elastography, as a tissue typing approach. We argue that multiparametric ultrasound, which includes B-mode and vibro-elastography images, could contain information that is not captured using multiparametric MRI (mpMRI) and therefore play a role in refining the biopsy and treatment strategies. In this work, we combine mpMRI with multiparametric ultrasound features from registered tissue areas to examine the potential improvement in cancer detection. All the images were acquired prior to radical prostatectomy and cancer detection was validated based on 36 whole mount histology slides. We calculated a set of 24 texture features from vibro-elastography and B-mode images, and five features from mpMRI. Then we used recursive feature elimination (RFE) and sparse regression through LASSO to find an optimal set of features to be used for tissue classification. We show that the set of these selected features increases the area under ROC curve from 0.87 with mpMRI alone to 0.94 with the selected mpMRI and multiparametric ultrasound features, when used with support vector machine classification on features extracted from peripheral zone. For features extracted from the whole-gland, the area under the curve was 0.75 and 0.82 for mpMRI and mpMRI along with ultrasound, respectively. These preliminary results provide evidence that ultrasound and ultrasound vibro-elastography could be used as modalities for improved cancer detection in combination with MRI.