Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

Abstract

Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.

AI-enhanced Synchronized Multiparametric 18F-FDG PET/MRI for Accurate Breast Cancer Diagnosis

“…They found that the model based on radiomics features from T2-w, DKI and quantitative DCE pharmacokinetic parameter maps had the best discriminatory ability for benign and malignant breast lesions (AUC = 0.921) [33]. Radiomics coupled to machine learning analysis applied to DCE-MRI, including both radiomics features and clinical data, also proved to be accurate in the characterization of < 1 cm breast lesions in ninety-six high-risk BRCA mutation carriers, with a diagnostic accuracy of 81.5%, signi cantly higher than qualitative morphological assessment with BI-RADS classi cation (AUC 53.4%) [37]. The usefulness of a multiparametric MRI approach was explored in a recent study by Tsarouchi et al [38].…”

“…Several studies have been published on the use of AI applied to MRI for breast cancer diagnosis, mainly aiming at increasing its relatively low speci city, compared to the high sensitivity, with accuracy values ranging from 0.728 to 0.920 [33][34][35][36][37]. Similar to our work, the group of Zhang et al also explored the possibility to improve the accuracy of the ML classi er combining radiomics features extracted from both morphological and functional contrast-enhanced and diffusion kurtosis MRI images of 207 histologically proven breast lesions.…”

AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis

“…Several studies have been published on the use of AI applied to MRI for breast cancer diagnosis, mainly aiming at increasing its relatively low specificity compared to its high sensitivity, with accuracy values ranging from 72.8 to 92.0% [40][41][42][43][44]. Similar to our work, Zhang et al also explored the possibility to improve the accuracy of the ML classifier combining radiomics features extracted from both morphological and functional DCE and diffusion kurtosis (DK) images of 207 histologically proven breast lesions.…”

Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

“…Gibbs et al [ 44 ] evaluated the utility of radiomics analysis for breast cancer diagnosis in small breast lesions (BI-RADS 4/5) using radiomics DCE-based parameter maps and achieved an AUC of 0.78. Lo Gullo et al [ 45 ] focused on the characterization of subcentimeter breast masses (BI-RADS 3/4) in high-risk patients. Radiomics analysis coupled with machine learning showed a diagnostic accuracy of 81.5%, improving lesion characterization compared with radiologists’ BI-RADS classification.…”