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.

AI-enhanced breast imaging: Where are we and where are we heading?

“…Thus, the application of AI in multiparametric breast MRI may improve its specificity and thereby reduce the number of unnecessary breast biopsies. A similar conclusion was achieved with a DCE-MRI radiomics AI 4D classifier which could avoid up to 36.2% of unnecessary biopsies [56]. This is particularly important in the setting of challenging lesions such as sub-centimeter lesions, non-mass like lesions, or those pertaining to high-risk patient groups.…”

Radiomics and deep learning methods in expanding the use of screening breast MRI

“…Improving the positive predictive value is of course desirable as it will result in fewer benign biopsies and fewer follow-up imaging recommendations. The study by Pötsch et al in this issue of European Radiology [8] seeks to increase the positive predictive value of MRI using a neural network classifier of radiomic features.…”

“…Radiomics studies in general follow a methodology of (1) image acquisition, (2) image segmentation, (3) feature extraction, (4) feature selection, and (5) predictive modeling [9]. In the study by Pötsch et al [8], the authors extracted 86 radiomic features including the enhancement characteristics of the entire volume of a breast lesion over several post-contrast time-points. The authors then trained an artificial intelligence (AI) classifier to predict benignity or malignancy based on the radiomic features.…”

“…The radiomics features selected by Pötsch et al [8] included volumetric characteristics, such that the spatial distribution of the parameters was analyzed in addition to the parameters themselves. The features that were found to be most predictive of malignancy were descriptors of the volumetric distribution of vascular permeability and the extracellular compartment, as well as changes in signal intensity over time related to wash-in and wash-out of contrast within the lesion [8]. Note that these time-resolved features of dynamic contrast enhancement were evaluated over 7 min, which may be too long given the trend toward abbreviated MRI.…”

“…Ultimately, the goal is to develop AI tools that improve the performance of radiologists and increase quality of care. The study by Pötsch et al [8] demonstrates that radiomics and AI tools could potentially be used to decrease benign biopsy rates. In this rapidly evolving field, future studies evaluating abbreviated MR methods and including a generalized screening patient population will be required.…”