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.
Keywords: Bone metastases; Breast cancer; Disseminated tumor cells; Machine learning; Multiparametric imaging; Neural networks.
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