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

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.

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

“…Numerous studies have applied the emerging radiomics technique to improve diagnostic, identification, prognostic, and predictive accuracy of cancer research (11)(12)(13)(14). Some scholars also try to apply radiomics in pancreatic tumor studies, such as malignancy prediction (15), histopathologic characteristics discrimination (16), vascular invasion prediction (17), prognosis prediction (18), and radiogenomics for genetic status prediction (19). However, to the best of our knowledge, there is no literature that has determined whether a radiomics signature derived from CT images would enable superior prediction of invasive behavior in patients with pSPN.…”

“…Considering the radiomics feature could be extracted from the single cross section (two dimensional, 2D) or multi-slices (three dimensional, 3D) of the tumor in CT images, the reported radiomics-based pancreatic cancer studies have either applied 2D segmentation (20) or 3D whole-tumor segmentation (14,(21)(22)(23)(24)(25). However, whether to select 2D regions of interest (ROIs) or 3D ROIs still remains unclear for invasive behavior prediction in pSPN.…”

Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

“…Hanania et al [ 13 ] identified 14 imaging biomarkers within GLCM features that predicted the histopathological grade within cyst contours. Tobaly et al [ 35 ] developed a radiomic model mostly based on high order CT radiomic features, which showed high diagnostic performance in differentiating benign from malignant IPMNs. Our results showed that most of the selected features were high-order features, which was consistent with the results presented by previous studies.…”

Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning

“…Thin section, high-resolution, contrast-enhanced CT was found to provide enough details regarding the structure of PCLs to make a diagnosis [ 40 ] and Lee, J. et al recently declared MRI and CT to be interchangeable for assessment and follow-up of patients with PCLs [ 41 ]. For patients refusing an MRI, pancreatic CT is the recommended alternative modality according to the societies ICG, ACG and ESG [ 32 ], and offers a comparable accuracy to MRCP in terms of PCL characterization [ 42 ]. However, we recognize the high diagnostic value of other modalities such as multi-parametric MRI, MRCP, and endoscopic ultrasound (EUS) [ 4 , 32 , 43 , 44 ].…”