Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features
Year: 2019
Type: article
Abstract: Multiparametric 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 18 F-fluorodeoxyglucose ( 18 F-FDG)-PET. The 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. In 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. 18 F-FDG-PET and morphologic features were less predictive. Our 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.
Source: European Radiology Experimental
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,609
Funders: Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung, Austrian Federal Ministry of Economy, Family and Youth, Austrian Science Fund, Oesterreichische Nationalbank, Medizinische Universität Wien, National Institutes of Health, Horizon 2020 Framework Programme, National Cancer Institute
Awards: Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders, G2665966410, MR Tumor Vessel Architecture Mapping as Novel Imaging Biomarker for the Assessment of Breast Tumors, G4102770861, G4266131079, G5271845376, G5870452336, G6403603385, G6517827690, G7125036476, G7768843244, G8858395165, G9000003249, G9000303076, Cancer Center Support Grant
OpenAlex ID: W2947596410