Magnetic resonance imaging radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas
Schilsky, Juliana Brooke
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BACKGROUND: Pancreatic cancer is one of the most lethal cancers. Despite enhanced understanding of the disease, the 5-year survival rate remains 8% due to the late stage of diagnosis and a lack of effective treatment options. Early detection of precancerous lesions, such as intraductal papillary mucinous neoplasms (IPMNs), is a strategy to prevent pancreas cancer related death. Standard qualitative imaging assessment cannot reliably distinguish between benign and malignant branch duct intraductal papillary mucinous neoplasms (BD-IPMNs). A more consistent risk prediction method is needed to inform clinical decision making such that patients with benign cysts may be spared from unnecessary surgical resection. OBJECTIVE: To assess whether a BD-IPMN malignancy risk prediction model which demonstrated strong potential on preoperative computed tomography (CT) images would show similar results on magnetic resonance imaging (MRI). METHODS: 19 pathologically proven BD-IPMN patients with preoperative contrast-enhanced CT and MRI and were included in the study. Five radiomics features were extracted from the portal-venous phase CT and MR images of the largest cyst. Associations between radiomics features extracted from CT and MR were assessed using Pearson correlations. RESULTS: Of the five radiomics features, average-weighted eccentricity (AWE) was most strongly correlated between imaging modalities in all patients (n=19, r=0.46, 95% CI=0.001-0.75, p=0.05), low-risk patients (r=0.63, 95% CI=0.09-0.88, p=0.028), and patients with a solid component or mural nodule (r=0.66, 95% CI=-0.32-0.96, p=0.15). However, when two outliers within the dataset were removed from analysis, AWE no longer correlated between MR and CT. None of the other radiomics features displayed significant correlations between the modalities. CONCLUSIONS: The CT-based risk prediction model cannot be applied to MR data suggesting that a new model should be created from MRI data alone.