Morphological, functional, and molecular assessment of breast cancer bone metastases by experimental ultrasound techniques compared with magnetic resonance imaging and histological analysis.

BACKGROUND: The imaging of bone metastases, which is regularly performed by cross-sectional modalities, is clinically vital when characterizing and staging osseous lesions. In this paper, we aimed to establish a novel methodology using experimental ultrasound (US) techniques to assess the morphological, functional, and molecular features of breast cancer bone metastases in an animal model, compared with magnetic resonance imaging (MRI) and histological analysis. MATERIALS AND METHODS: Nude rats were implanted intra-arterially with MDA-MB-231 breast cancer cells to induce osteolytic metastasis in their right hind legs. Once tumors had developed, an experimental US technique using automatic 3D scanning and MRI were performed. For assessment of perfusion, functional imaging techniques included contrast-enhanced US (CEUS) and dynamic contrast-enhanced MRI (DCE-MRI). For molecular ultrasound, anti-VEGFR2 conjugated microbubbles were applied and correlated with immunostaining for VEGFR2 expression. RESULTS: 3D US enabled the automatic assessment of osteolytic lesions, including the largest tumor diameters along the x-, y- and z-axes as well as the segmented tumor volumes, without significant differences between US and MRI (p > 0.18). The CEUS and DCE-MRI of osseous lesions showed corresponding results for the parameters peak enhancement, wash-in area under the curve (both, r > 0.5) and wash-in perfusion index (r > 0.3) when differentiating between tumor, necrotic tissue and healthy muscle tissue (all, p < 0.01). Finally, molecular US allowed the non-invasive assessment of increased VEGFR2 expression in skeletal lesions compared with surrounding muscle tissue (p = 0.03), while a control antibody could not discriminate between these tissues (p = 0.44)-a factor which was confirmed by histological analysis. CONCLUSION: To the best of our knowledge, this is the first report on an imaging protocol for breast cancer bone metastasis using an experimental US scanner. Therefore, we present a novel methodology to characterize these osseous lesions on the morphological, functional, and molecular level in correlation with MRI and histological analysis.

[Artificial intelligence-based algorithms : Decision-making support for computed tomography of the chest]. / Algorithmen mit künstlicher Intelligenz : Entscheidungsunterstützung für Computertomographien des Thorax.

Artificial intelligence (AI) algorithms are increasingly used in radiology. The main areas of application are, for example, the detection of lung lesions and the diagnosis of chronic obstructive and interstitial lung diseases. The aim of our study was to train and evaluate a package of algorithms that analyze data from computed tomographic (CT) images of the chest and provide quantitative measurements to the radiologist. The following algorithms were trained: lung lesion detection and measurement, lung lobe segmentation, vessel segmentation and measurement, coronary calcium scoring, measurement and density analysis of vertebral bodies. AI-supported algorithms will become part of daily routine of the radiologist in the future. Tasks that do not require medical expertise can be performed by AI. However, our results show that, based on the current accuracy, verification by an experienced radiologist is necessary.

Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a Model-Averaged Neural Network.

Macrometastases in bone are preceded by bone marrow invasion of disseminated tumor cells. This study combined functional imaging parameters from FDG-PET/CT and MRI in a rat model of breast cancer bone metastases to a Model-averaged Neural Network (avNNet) for the detection of early metastatic disease and prediction of future macrometastases. Metastases were induced in 28 rats by injecting MDA-MB-231 breast cancer cells into the right superficial epigastric artery, resulting in the growth of osseous metastases in the right hind leg of the animals. All animals received FDG-PET/CT and MRI at days 0, 10, 20 and 30 after tumor cell injection. In total, 18/28 rats presented with metastases at days 20 or 30 (64.3%). None of the animals featured morphologic bone lesions during imaging at day 10, and the imaging parameters acquired at day 10 did not differ significantly between animals with metastases at or after day 20 and those without (all p > 0.3). The avNNet trained with the imaging parameters acquired at day 10, however, achieved an accuracy of 85.7% (95% CI 67.3-96.0%) in predicting future macrometastatic disease (ROCAUC 0.90; 95% CI 0.76-1.00), and significantly outperformed the predictive capacities of all single parameters (all p ≤ 0.02). The integration of functional FDG-PET/CT and MRI parameters into an avNNet can thus be used to predict macrometastatic disease with high accuracy, and their combination might serve as a surrogate marker for bone marrow invasion as an early metastatic process that is commonly missed during conventional staging examinations.