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

Joao Santinha,Alain Sauvanet,David Tobaly,Jérôme Cros,Marco Dioguardi Burgio,Riccardo Sartoris,Valérie Vilgrain,Nikolaos Papanikolaou,Vinciane Rebours,Celso Matos,Maxime Ronot,Anne Couvelard

doi:10.3390/cancers12113089

Abstract

Simple SummaryThe 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.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.

Highlights

Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are mucin-producing tumors that originate from the pancreatic ducts
We have demonstrated the capabilities of radiomics models comprising low-grade dysplasia (LGD) versus high-grade dysplasia (HGD) versus invasive, LGD and HGD, HGD and invasive
The results showed in the training cohort an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.73, Se of 0.65, Spe of 0.69, and in the external validation cohort with an AUC of 0.55, Se of 0.36, Spe of 0.72

Summary

Introduction

Intraductal papillary mucinous neoplasms (IPMN) of the pancreas are mucin-producing tumors that originate from the pancreatic ducts. Their incidence keeps growing due to improvement and widespread use of cross-sectional imaging coupled with the population’s increasing age. An aggressive surgical resection approach was initially recommended for the prevention or early treatment of pancreatic cancer. This has led to overtreatment, as highlighted in a systematic review of 37 case series that showed that the rate of malignancy development during follow-up is low, with only 2.8% of patients developing invasive neoplasia [9]

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