Abstract
BackgroundMultiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET.MethodsThe CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used.ResultsIn 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive.ConclusionOur CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.
Highlights
Multiparametric positron emission tomography/magnetic resonance imaging shows clinical potential for detection and classification of breast lesions
Computer-aided detection (CADe) systems assist radiologists in localising suspicious regions in medical images, whereas computer-aided diagnosis (CADx) systems support the radiologist in the diagnosis of suspicious regions by providing and analysing information extracted from these regions [7]
These systems show potential to be advantageous in the current clinical scenario [7] where despite guidelines for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), such as the Breast Imaging-Reporting and Data System (BI-RADS®) MRI lexicon [8], inter- and intra-reader variability remains an issue and the human analysis of complex relationships observed in images and the underlying disease remains limited [9]
Summary
Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Multimodal, multiparametric imaging (mpI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), and positron emission tomography (PET) has been investigated for an improved differentiation of benign and malignant breast lesions [3]. Computer-aided detection (CADe) systems assist radiologists in localising suspicious regions in medical images, whereas computer-aided diagnosis (CADx) systems support the radiologist in the diagnosis of suspicious regions by providing and analysing information extracted from these regions [7] These systems show potential to be advantageous in the current clinical scenario [7] where despite guidelines for DCE-MRI, such as the Breast Imaging-Reporting and Data System (BI-RADS®) MRI lexicon [8], inter- and intra-reader variability remains an issue and the human analysis of complex relationships observed in images and the underlying disease remains limited [9]
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