Objectives: To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.
Materials and methods: Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE-MRI images (manual DCE) and using GMM with corresponding PET images (GMM-PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.
Results: No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM-PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM-PET for untreated and treated lesions. The mean Dice score for GMM-PET was 0.770 and 0.649 for untreated and treated lesions, respectively.
Discussion: Using PET/MRI, tumor area and mean ADC value estimated with a GMM-PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.
Keywords: Breast cancer; Diffusion imaging; Mixture modelling; PET/MRI; Segmentation.