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@article{Makowski:2021:10.1097/RLI.0000000000000788,
author = {Makowski, MR and Bressem, KK and Franz, L and Kader, A and Niehues, SM and Keller, S and Rueckert, D and Adams, LC},
doi = {10.1097/RLI.0000000000000788},
journal = {Invest Radiol},
pages = {661--668},
title = {De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer.},
url = {http://dx.doi.org/10.1097/RLI.0000000000000788},
volume = {56},
year = {2021}
}

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TY  - JOUR
AB  - OBJECTIVES: The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping. MATERIALS AND METHODS: Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8). RESULTS: Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (-0.16) and SGB (-0.25) and a higher generalization error. CONCLUSIONS: When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using
AU  - Makowski,MR
AU  - Bressem,KK
AU  - Franz,L
AU  - Kader,A
AU  - Niehues,SM
AU  - Keller,S
AU  - Rueckert,D
AU  - Adams,LC
DO  - 10.1097/RLI.0000000000000788
EP  - 668
PY  - 2021///
SP  - 661
TI  - De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer.
T2  - Invest Radiol
UR  - http://dx.doi.org/10.1097/RLI.0000000000000788
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34047538
VL  - 56
ER  -

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