Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset

Academic Radiology
Heather M WhitneyMaryellen L Giger

Abstract

The objective of this study was to demonstrate improvement in distinguishing between benign lesions and luminal A breast cancers in a large clinical breast magnetic resonance imaging database by using quantitative radiomics over maximum linear size alone. In this retrospective study, 264 benign lesions and 390 luminal A breast cancers were automatically segmented from dynamic contrast-enhanced breast magnetic resonance images. Thirty-eight radiomic features were extracted. Tenfold cross validation was performed to assess the ability to distinguish between lesions and cancers using maximum linear size alone and lesion signatures obtained with stepwise feature selection and a linear discriminant analysis classifier including and excluding size features. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit. For maximum linear size alone, AUC and 95% confidence interval was 0.684 (0.642, 0.724) compared to 0.728 (0.687, 0.766) (P = 0.005) and 0.729 (0.689, 0.767) (P = 0.005) for lesion signature feature selection protocols including and excluding size features, respectively. The features of irregularity and entropy were chosen in all folds when size features were included and excluded. AUC fo...Continue Reading

Citations

Nov 13, 2019·AJR. American Journal of Roentgenology·Cindy S LeeLinda Moy
Feb 27, 2020·The British Journal of Radiology·William RogersPhilippe Lambin
Aug 31, 2020·Journal of Magnetic Resonance Imaging : JMRI·Anke Meyer-BaseKatja Pinker
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Nov 8, 2018·BioMed Research International·Paola CrivelliMaurizio Conti
May 7, 2020·Seminars in Cancer Biology·Allegra ContiNicola Toschi
Mar 16, 2021·Frontiers in Oncology·Weijing TaoGuangming Lu
Jul 19, 2019·Breast Care·Giovanna Negrão de FigueiredoEva Maria Fallenberg
Dec 23, 2021·Journal of Magnetic Resonance Imaging : JMRI·R W Y GranzierM L Smidt

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