BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning

Abstract

BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and MethodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A–5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

Multiparametric MRI

“…Hagiwara et al 135 applied CNN to T1, T2, and proton density maps of the brain to extract features common to multiple sclerosis and neuromyelitis optica spectrum disorder, another demyelinating disease, and used this information to differentiate these 2 disorders, achieving an accuracy of 80%. Some studies showed the superiority of the diagnostic ability of CNN over radiomics based on multiparametric MRI 155–158 . For example, Truhn et al 155 developed both CNN and radiomic models to differentiate benign from malignant breast lesions using multiparametric MRI data of T2-weighted images, as well as precontrast and 4 postcontrast DCE T1-weighted images, with the CNN showing a significantly higher AUC than the radiomic model.…”

“…Multiparametric MRI has also been used for the classification or diagnosis of diseases using deep learning. 135,[150][151][152][153][154][155][156][157][158] Hagiwara et al 135 applied CNN to T1, T2, and proton density maps of the brain to extract features common to multiple sclerosis and neuromyelitis optica spectrum disorder, another demyelinating disease, and used this information to differentiate these 2 disorders, achieving an accuracy of 80%. Some studies showed the superiority of the diagnostic ability of CNN over radiomics based on multiparametric MRI.…”

“…Some studies showed the superiority of the diagnostic ability of CNN over radiomics based on multiparametric MRI. [155][156][157][158] For example, Truhn et al 155 developed both CNN and radiomic models to differentiate benign from malignant breast lesions using multiparametric MRI data Results from 2-sample t test, which identify the most critical areas of the brain that distinguish the 2 groups (top row: secondary progressive multiple sclerosis > relapsing-remitting multiple sclerosis; bottom row: relapsing-remitting multiple sclerosis > secondary progressive multiple sclerosis). Shown are the t values (plus 95% confidence interval) that demonstrate significance in voxel-wise statistics.…”

Management of Non-Mass Enhancement at Breast Magnetic Resonance in Screening Settings Referred for Magnetic Resonance-Guided Biopsy

“…17 The NME lesion is challenging since it is unique for the MRI lexicon, and its management and follow-up lack standardization. ( Figure 1 ) 18 , 19 …”

“…17 The NME lesion is challenging since it is unique for the MRI lexicon, and its management and follow-up lack standardization. (Figure 1) 18,19 This study aims to report our experience with NME lesions diagnosed by screening MRI referred for MRI-guided biopsies and discuss the management and follow-up of these lesions.…”