Abstract Background 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 18F-fluorodeoxyglucose (18F-FDG)-PET.Methods 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 Outdoor Games - Disc Golf 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.Results 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.18F-FDG-PET and morphologic features were less predictive.
Conclusion Our CAD COWHIDE - RUGS - 100% Hair on Hide Floor Rug 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.