Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer

• Verfasst von: Bickelhaupt, Sebastian [VerfasserIn]
• Verfasst von: Jaeger, Paul Ferdinand [VerfasserIn]
• Verfasst von: Laun, Frederik B. [VerfasserIn]
• Verfasst von: Lederer, Wolfgang [VerfasserIn]
• Verfasst von: Daniel, Heidi [VerfasserIn]
• Verfasst von: Kuder, Tristan Anselm [VerfasserIn]
• Verfasst von: Wuesthof, Lorenz [VerfasserIn]
• Verfasst von: Paech, Daniel [VerfasserIn]
• Verfasst von: Bonekamp, David [VerfasserIn]
• Verfasst von: Radbruch, Alexander [VerfasserIn]
• Verfasst von: Delorme, Stefan [VerfasserIn]
• Verfasst von: Schlemmer, Heinz-Peter [VerfasserIn]
• Verfasst von: Maier-Hein, Klaus H. [VerfasserIn]
• Titel: Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer
• Verf.angabe: 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
• E-Jahr: 2018
• Jahr: February 20, 2018
• Umfang: 10 S.
• Fussnoten: Ahead of print ; Gesehen am 21.02.2018
• Titel Quelle: Enthalten in: Radiology
• Ort Quelle: Oak Brook, Ill. : Soc., 1923
• Jahr Quelle: 2018
• Band/Heft Quelle: 287(2018), 3, Seite 761-770
• ISSN Quelle: 1527-1315
• DOI: doi:10.1148/radiol.2017170273
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1570040680

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.