Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI

  title={Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI},
  author={Jin Jin and Lin Zhang and Ethan Leng and Gregory J. Metzger and Joseph S. Koopmeiners},
While current research has shown the importance of Multi-parametric MRI (mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed for how to incorporate the specific structures of the mpMRI data, such as the regional heterogeneity and between-voxel correlation within a subject. This paper proposes a machine learning-based method for improved voxel-wise PCa classification by taking into account the unique structures of the data. We propose a multi-resolution modeling approach… 

Figures and Tables from this paper



Bayesian Spatial Models for Voxel-wise Prostate Cancer Classification Using Multi-parametric MRI Data

This paper proposes novel voxel-wise Bayesian classifiers for prostate cancer that account for the spatial correlation and between-patient heterogeneity in mpMRI, and considers three computationally efficient approaches using Nearest Neighbor Gaussian Process (NNGP), knot-based reduced-rank approximation, and a conditional autoregressive (CAR) model.

Detection of prostate cancer with multiparametric MRI utilizing the anatomic structure of the prostate

This paper proposes a novel voxel‐wise classifier of prostate cancer that accounts for the anatomic structure of the prostate by Bayesian hierarchical modeling, which can be combined with post hoc spatial Gaussian kernel smoothing to account for residual spatial correlation.

Bayesian spatial models for voxel‐wise prostate cancer classification using multi‐parametric magnetic resonance imaging data

This article proposes novel Bayesian approaches for voxel‐wise PCa classification that accounts for spatial correlation and between‐patient heterogeneity in the mpMRI data and proposes three scalable approaches based on Nearest Neighbor Gaussian process, reduced‐rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance.

Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Combination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent.

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level.

Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

A preliminary comparison showed that the optimal CADx scheme mimicked, in terms of AUC, the human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.

A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI

A DCE-MRI CAD system, which calculates the likelihood of malignancy in a given area of the prostate by combining model-based and model-free parameters, and preliminary results show that the system is accurate in detecting areas of the gland that are involved by tumor.

Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class

This paper develops a new method to estimate the parameters defining the Markovian distribution of the measured data, while performing the data clustering simultaneously, using fuzzy Markov random fields (fuzzy MRFs) for the segmentation of multispectral MR prostate images.