CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas

SIMPLE SUMMARY: The management of intraductal papillary mucinous neoplasms of the pancreas (IPMN) remains controversial due to the relatively high rate of unnecessary surgery for low grade dysplasia (LGD) despite the last international recommendations. The aim of our retrospective study was to assess the performance of radiomic analysis on CT in differentiating benign from malignant IPMN. We confirmed in a training cohort (296 patients) and a validation cohort (112 patients) that a total of 85 radiomics features provided valuable additional and independent information for discriminating benign from malignant tumors in the training cohort with an area under the ROC curve (AUC) of 0.84 and an external validation with an AUC of 0.71 with higher performance when implementing clinical variables leading to the indication to surgery. We have demonstrated the capabilities of radiomics models comprising LGD versus high-grade dysplasia (HGD) versus invasive, LGD and HGD, HGD and invasive. ABSTRACT: To assess the performance of CT-based radiomics analysis in differentiating benign from malignant intraductal papillary mucinous neoplasms of the pancreas (IPMN), preoperative scans of 408 resected patients with IPMN were retrospectively analyzed. IPMNs were classified as benign (low-grade dysplasia, n = 181), or malignant (high grade, n = 128, and invasive, n = 99). Clinicobiological data were reported. Patients were divided into a training cohort (TC) of 296 patients and an external validation cohort (EVC) of 112 patients. After semi-automatic tumor segmentation, PyRadiomics was used to extract radiomics features. A multivariate model was developed using a logistic regression approach. In the training cohort, 85/107 radiomics features were significantly different between patients with benign and malignant IPMNs. Unsupervised clustering analysis revealed four distinct clusters of patients with similar radiomics features patterns with malignancy as the most significant association. The multivariate model differentiated benign from malignant tumors in TC with an area under the ROC curve (AUC) of 0.84, sensitivity (Se) of 0.82, specificity (Spe) of 0.74, and in EVC with an AUC of 0.71, Se of 0.69, Spe of 0.57. This large study confirms the high diagnostic performance of preoperative CT-based radiomics analysis to differentiate between benign from malignant IPMNs.