Title |
Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer
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Published in |
Radiology, February 2018
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DOI | 10.1148/radiol.2017170273 |
Pubmed ID | |
Authors |
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 |
Abstract |
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.©RSNA, 2018 Online supplemental material is available for this article. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 4 | 29% |
United Arab Emirates | 1 | 7% |
France | 1 | 7% |
Mexico | 1 | 7% |
Germany | 1 | 7% |
Netherlands | 1 | 7% |
Unknown | 5 | 36% |
Demographic breakdown
Type | Count | As % |
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Scientists | 5 | 36% |
Members of the public | 4 | 29% |
Practitioners (doctors, other healthcare professionals) | 3 | 21% |
Science communicators (journalists, bloggers, editors) | 2 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 100 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 18 | 18% |
Researcher | 15 | 15% |
Other | 6 | 6% |
Student > Postgraduate | 6 | 6% |
Student > Bachelor | 6 | 6% |
Other | 20 | 20% |
Unknown | 29 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 35 | 35% |
Engineering | 12 | 12% |
Computer Science | 5 | 5% |
Neuroscience | 2 | 2% |
Physics and Astronomy | 2 | 2% |
Other | 6 | 6% |
Unknown | 38 | 38% |