An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

In this retrospective study, the authors aimed to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions, thereby avoiding unnecessary biopsies. The authors determined that the investigated automated 4D radiomics approach resulted in an accurate AI classifier that was able to distinguish between benign and malignant lesions, the application of which could have avoided unnecessary biopsies.

Key points

• Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features.
• An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset).
• Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

Article: An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

Authors: Nina Pötsch, Matthias Dietzel, Panagiotis Kapetas, Paola Clauser, Katja Pinker, Stephan Ellmann, Michael Uder, Thomas Helbich & Pascal A. T. Baltzer

Written by

European Radiology