Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI
Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 290
Pages Range: 290-297
Journal Issue: 3
DOI: 10.1148/radiol.2018181352
Abstract
Purpose: To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of contrast agent–enhancing lesions as benign or malignant at multiparametric breast MRI. Materials and Methods: Between August 2011 and August 2015, 447 patients with 1294 enhancing lesions (787 malignant, 507 benign; median size, 15 mm 6 20) were evaluated. Lesions were manually segmented by one breast radiologist. RA was performed by using L1 regularization and principal component analysis. CNN used a deep residual neural network with 34 layers. All algorithms were also retrained on half the number of lesions (n = 647). Machine interpretations were compared with prospective interpretations by three breast radiologists. Standard of reference was histologic analysis or follow-up. Areas under the receiver operating curve (AUCs) were used to compare diagnostic performance. Results: CNN trained on the full cohort was superior to training on the half-size cohort (AUC, 0.88 vs 0.83, respectively; P = .01), but there was no difference for RA and L1 regularization (AUC, 0.81 vs 0.80, respectively; P = .76) or RA and principal component analysis (AUC, 0.78 vs 0.78, respectively; P = .93). By using the full cohort, CNN performance (AUC, 0.88; 95% confidence interval: 0.86, 0.89) was better than RA and L1 regularization (AUC, 0.81; 95% confidence interval: 0.79, 0.83; P , .001) and RA and principal component analysis (AUC, 0.78; 95% confidence interval: 0.76, 0.80; P , .001). However, CNN was inferior to breast radiologist interpretation (AUC, 0.98; 95% confidence interval: 0.96, 0.99; P , .001). Conclusion: A convolutional neural network was superior to radiomic analysis for classification of enhancing lesions as benign or malignant at multiparametric breast MRI. Both approaches were inferior to radiologists’ performance; however, more training data will further improve performance of convolutional neural network, but not that of radiomics algorithms.
Involved external institutions
Germany (DE)How to cite
APA:
Truhn, D., Schrading, S., Haarburger, C., Schneider, H., Merhof, D., & Kuhl, C. (2019). Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Radiology, 290(3), 290-297. https://doi.org/10.1148/radiol.2018181352
MLA:
Truhn, Daniel, et al. "Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI." Radiology 290.3 (2019): 290-297.
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