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


Authors: Pötsch, N.; Dietzel, M.; Kapetas, P.; Clauser, P.; Pinker, K.; Ellmann, S.; Uder, M.; Helbich, T.; Baltzer, P. A. T.
Article Title: An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: Potential to avoid unnecessary breast biopsies
Abstract: Objectives: Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies. Methods: This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network–derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity). Results: Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18–85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p <.001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8–89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2). Conclusion: The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application 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. © 2021, The Author(s).
Keywords: breast cancer; breast mri; neural network; principal component analysis; breast biopsies
Journal Title: European Radiology
Volume: 31
Issue: 8
ISSN: 0938-7994
Publisher: Springer  
Date Published: 2021-08-01
Start Page: 5866
End Page: 5876
Language: English
DOI: 10.1007/s00330-021-07787-z
PUBMED: 33744990
PROVIDER: scopus
PMCID: PMC8270804
DOI/URL:
Notes: Article -- Export Date: 2 August 2021 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors