Decision forests for learning prostate cancer probability maps from multiparametric MRI

Henry R. Ehrenberg; Daniel Cornfeld; Cayce B. Nawaf; Preston C. Sprenkle; James S. Duncan

doi:10.1117/12.2216904

24 March 2016 Decision forests for learning prostate cancer probability maps from multiparametric MRI

Henry R. Ehrenberg, Daniel Cornfeld, Cayce B. Nawaf, Preston C. Sprenkle, James S. Duncan

Author Affiliations +

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851J (2016) https://doi.org/10.1117/12.2216904
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Objectives: Advances in multiparametric magnetic resonance imaging (mpMRI) and ultrasound/MRI fusion imaging offer a powerful alternative to the typical undirected approach to diagnosing prostate cancer. However, these methods require the time and expertise needed to interpret mpMRI image scenes. In this paper, a machine learning framework for automatically detecting and localizing cancerous lesions within the prostate is developed and evaluated. Methods: Two studies were performed to gather MRI and pathology data. The 12 patients in the first study underwent an MRI session to obtain structural, diffusion-weighted, and dynamic contrast enhanced image vol- umes of the prostate, and regions suspected of being cancerous from the MRI data were manually contoured by radiologists. Whole-mount slices of the prostate were obtained for the patients in the second study, in addition to structural and diffusion-weighted MRI data, for pathology verification. A 3-D feature set for voxel-wise appear- ance description combining intensity data, textural operators, and zonal approximations was generated. Voxels in a test set were classified as normal or cancer using a decision forest-based model initialized using Gaussian discriminant analysis. A leave-one-patient-out cross-validation scheme was used to assess the predictions against the expert manual segmentations confirmed as cancer by biopsy. Results: We achieved an area under the average receiver-operator characteristic curve of 0.923 for the first study, and visual assessment of the probability maps showed 21 out of 22 tumors were identified while a high level of specificity was maintained. In addition to evaluating the model against related approaches, the effects of the individual MRI parameter types were explored, and pathological verification using whole-mount slices from the second study was performed. Conclusions: The results of this paper show that the combination of mpMRI and machine learning is a powerful tool for quantitatively diagnosing prostate cancer.

Citation Download Citation

Henry R. Ehrenberg, Daniel Cornfeld, Cayce B. Nawaf, Preston C. Sprenkle, and James S. Duncan "Decision forests for learning prostate cancer probability maps from multiparametric MRI", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851J (24 March 2016); https://doi.org/10.1117/12.2216904

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