De Novo Radiomics Approach Using Image Augmentation and Features from T1 Mapping to Predict Gleason Scores in Prostate Cancer
Makowski MR, Bressem KK, Franz L, Kader A, Niehues SM, Keller S, Rueckert D, Adams LC (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 56
Pages Range: 661-668
Journal Issue: 10
DOI: 10.1097/RLI.0000000000000788
Abstract
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 radiomics.
Involved external institutions
Germany (DE)How to cite
APA:
Makowski, M.R., Bressem, K.K., Franz, L., Kader, A., Niehues, S.M., Keller, S.,... Adams, L.C. (2021). De Novo Radiomics Approach Using Image Augmentation and Features from T1 Mapping to Predict Gleason Scores in Prostate Cancer. Investigative Radiology, 56(10), 661-668. https://doi.org/10.1097/RLI.0000000000000788
MLA:
Makowski, Marcus R., et al. "De Novo Radiomics Approach Using Image Augmentation and Features from T1 Mapping to Predict Gleason Scores in Prostate Cancer." Investigative Radiology 56.10 (2021): 661-668.
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