Corpus ID: 20151331

MRI prostate cancer ra- diomics

  title={MRI prostate cancer ra- diomics},
  author={P. Chatzoudis},


Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery
To identify and evaluate textural quantitative imaging signatures (QISes) for tumors occurring within the central gland (CG) and peripheral zone (PZ) of the prostate, respectively, as seen on in vivoExpand
Prostate cancer characterization on MR images using fractal features.
PURPOSE Computerized detection of prostate cancer on T2-weighted MR images. METHODS The authors combined fractal and multifractal features to perform textural analysis of the images. The fractalExpand
MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection
The preliminary experimental results indicated that the proposed radiomics-driven feature model outperformed each of its constituent feature groups as well as a comparable conventional mpMRI feature model. Expand
A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI
The results show that the proposed data-driven features outperform the traditional pharmacokinetic parameters with an area under ROC of 0.86 for LASSO-isolated PCA parameters, compared to 0.78 for pharmacokinetics parameters, shows that the novel approach to the analysis of DCE data has the potential to improve the multiparametric MRI protocol for prostate cancer detection. Expand
Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging.
The novel CAD system is able to detect the discriminative texture features for cancer detection and localization and is a promising tool for improving the quality and efficiency of prostate cancer diagnosis. Expand
Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models
Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI and outperformed the conventional model with regard to cancer detection accuracy. Expand
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
Machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns are presented. Expand
Computer-Aided Detection of Prostate Cancer in MRI
A fully automated computer-aided detection system which consists of two stages that detects initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection and has potential in a first-reader setting. Expand
Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS
The results suggest that following further rigorous validation, SeSMiK-GE could be developed into a powerful diagnostic and prognostic tool for detection and grading of CaP in vivo and in helping to determine the appropriate treatment option. Expand
Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI.
A simple T2 estimation method has a diagnostic performance such that it complements a DCE T1-w-based CADx system in discriminating malignant lesions from normal and benign regions and is beneficial to visual inspection due to the removed coil profile and fixed window and level settings. Expand