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| 001 | 135948 | ||
| 005 | 20240229105047.0 | ||
| 024 | 7 | _ | |a 10.1148/radiol.2017170273 |2 doi |
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| 024 | 7 | _ | |a 0033-8419 |2 ISSN |
| 024 | 7 | _ | |a 1527-1315 |2 ISSN |
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| 037 | _ | _ | |a DKFZ-2018-00685 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Bickelhaupt, Sebastian |0 P:(DE-He78)d2d971750bce6217eb90fff9b01e61f9 |b 0 |e First author |
| 245 | _ | _ | |a Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer. |
| 260 | _ | _ | |a Oak Brook, Ill. |c 2018 |b Soc. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1661341175_30405 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 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. © RSNA, 2018 Online supplemental material is available for this article. |
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| 700 | 1 | _ | |a Jaeger, Paul Ferdinand |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Laun, Frederik |0 P:(DE-He78)b709e6df1ec6b63e5ffad4c8131f6f4d |b 2 |
| 700 | 1 | _ | |a Lederer, Wolfgang |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Daniel, Heidi |b 4 |
| 700 | 1 | _ | |a Kuder, Tristan Anselm |0 P:(DE-He78)59dfdd0ee0a7f0db81535f0781a3a6d6 |b 5 |
| 700 | 1 | _ | |a Wuesthof, Lorenz |b 6 |
| 700 | 1 | _ | |a Paech, Daniel |0 P:(DE-He78)c6e31fb8f19e185e254174554a0cccfc |b 7 |
| 700 | 1 | _ | |a Bonekamp, David |0 P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93 |b 8 |
| 700 | 1 | _ | |a Radbruch, Alexander |0 P:(DE-He78)77588f5b9413339755a66e739d316c7d |b 9 |
| 700 | 1 | _ | |a Delorme, Stefan |0 P:(DE-He78)3e76653311420a51a5faeb80363bd73e |b 10 |
| 700 | 1 | _ | |a Schlemmer, Heinz-Peter |0 P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec |b 11 |
| 700 | 1 | _ | |a Steudle, Franziska Hildegard |b 12 |
| 700 | 1 | _ | |a Maier-Hein, Klaus |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 13 |e Last author |
| 773 | _ | _ | |a 10.1148/radiol.2017170273 |g Vol. 287, no. 3, p. 761 - 770 |0 PERI:(DE-600)2010588-5 |n 3 |p 761 - 770 |t Radiology |v 287 |y 2018 |x 1527-1315 |
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