Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer
Purpose: To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue–optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and M...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
February 20, 2018
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| In: |
Radiology
Year: 2018, Volume: 287, Issue: 3, Pages: 761-770 |
| ISSN: | 1527-1315 |
| DOI: | 10.1148/radiol.2017170273 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1148/radiol.2017170273 Verlag, kostenfrei, Volltext: http://pubs.rsna.org/doi/abs/10.1148/radiol.2017170273 Verlag, kostenfrei, Volltext: http://pubs.rsna.org/doi/pdf/10.1148/radiol.2017170273 
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| Author Notes: | Sebastian Bickelhaupt, Paul Ferdinand Jaeger, Frederik Bernd Laun, Wolfgang Lederer, Heidi Daniel, Tristan Anselm Kuder, Lorenz Wuesthof, Daniel Paech, David Bonekamp, Alexander Radbruch, Stefan Delorme, Heinz-Peter Schlemmer, Franziska Hildegard Steudle, Klaus Hermann Maier-Hein |
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| 245 | 1 | 0 | |a Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer |c Sebastian Bickelhaupt, Paul Ferdinand Jaeger, Frederik Bernd Laun, Wolfgang Lederer, Heidi Daniel, Tristan Anselm Kuder, Lorenz Wuesthof, Daniel Paech, David Bonekamp, Alexander Radbruch, Stefan Delorme, Heinz-Peter Schlemmer, Franziska Hildegard Steudle, Klaus Hermann Maier-Hein |
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| 520 | |a Purpose: To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue–optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods: This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0–1500 sec/mm2) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results: The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material–enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001). Conclusion: A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set. | ||
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