Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder

•One of the pioneer approaches which attempted to classify 3D volumetric prostate cancer lesions into 5 grade groups from MRI images.•Achieved moderate success in classification of 4 grade groups.•The method uses stacked sparse autoencoders (SSAE) to transform low-level texture features extracted fr...

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Veröffentlicht in:Computerized medical imaging and graphics Jg. 69; S. 60 - 68
Hauptverfasser: Abraham, Bejoy, Nair, Madhu S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.11.2018
Elsevier Science Ltd
Schlagworte:
Biomarkers
CAI
Cancer
Computer assisted instruction
Deep learning
Diffusion coefficient
Feature extraction
Gleason grade
Image classification
Lesions
Magnetic resonance imaging
Medical imaging
Multiparametric MRI
NMR
Nuclear magnetic resonance
Prostate cancer
Stacked sparse autoencoder
Texture
Texture features
Training
Deep learning
Gleason grade
Stacked sparse autoencoder
Texture features
Prostate cancer
Multiparametric MRI
ISSN:0895-6111, 1879-0771, 1879-0771
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Zusammenfassung:•One of the pioneer approaches which attempted to classify 3D volumetric prostate cancer lesions into 5 grade groups from MRI images.•Achieved moderate success in classification of 4 grade groups.•The method uses stacked sparse autoencoders (SSAE) to transform low-level texture features extracted from GLCM, GLRLM, GLSZM and NGTDM into high-level features.•Softmax classifier is used for classification.•This method achieved first place in the PROSTATEx-2 challenge organized by the AAPM, SPIE and NCI for the determination of Gleason Grade Group in prostate cancer. A novel method to determine the Grade Group (GG) in prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mpMRI) biomarkers is investigated in this paper. In this method, high-level features are extracted from hand-crafted texture features using a deep network of stacked sparse autoencoders (SSAE) and classified them using a softmax classifier (SMC). Transaxial T2 Weighted (T2W), Apparent Diffusion Coefficient (ADC) and high B-Value Diffusion-Weighted (BVAL) images obtained from PROSTATEx-2 2017 challenge dataset are used in this technique. The method was evaluated on the challenge dataset composed of a training set of 112 lesions and a test set of 70 lesions. It achieved a quadratic-weighted Kappa score of 0.2772 on evaluation using test dataset of the challenge. It also reached a Positive Predictive Value (PPV) of 80% in predicting PCa with GG > 1. The method achieved first place in the challenge, winning over 43 methods submitted by 21 groups. A 3-fold cross-validation using training data of the challenge was further performed and the method achieved a quadratic-weighted kappa score of 0.2326 and Positive Predictive Value (PPV) of 80.26% in predicting PCa with GG > 1. Even though the training dataset is a highly imbalanced one, the method was able to achieve a fair kappa score. Being one of the pioneer methods which attempted to classify prostate cancer into 5 grade groups from MRI images, it could serve as a base method for further investigations and improvements.
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ISSN:0895-6111
1879-0771
1879-0771
DOI:10.1016/j.compmedimag.2018.08.006