Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. Issue 2 (February 2019)

• Record Type: Journal Article
• Authors: Attiyeh, Marc A.; Chakraborty, Jayasree; Gazit, Lior; Langdon-Embry, Liana; Gonen, Mithat; Balachandran, Vinod P.; D'Angelica, Michael I.; DeMatteo, Ronald P.; Jarnagin, William R.; Kingham, T. Peter; Allen, Peter J.; Do, Richard K.; Simpson, Amber L.
• Is Part Of: HPB. Volume 21:Issue 2(2019)
• Page Start: 212
• Page End: 218
• Publication Date: 2019-02
• Subjects: Liver -- Diseases -- Periodicals; Biliary tract -- Diseases -- Periodicals; Pancreas -- Diseases -- Periodicals; 616.362005
• DOI: 10.1016/j.hpb.2018.07.016
• Languages: English
• ISSNs: 1365-182X

Abstract

Abstract: Background: Intraductal papillary mucinous neoplasms (IPMNs) are radiographically identifiable potential precursor lesions of pancreatic adenocarcinoma. While resection is recommended when main duct dilation is present, management of branch duct IPMN (BD-IPMN) remains controversial. This study sought to evaluate whether preoperative quantitative imaging features of BD-IPMNs could distinguish low-risk disease (low- and intermediate-grade dysplasia) from high-risk disease (high-grade dysplasia and invasive carcinoma). Methods: Patients who underwent resection between 2005 and 2015 with pathologically proven BD-IPMN and a preoperative CT scan were included in the study. Quantitative image features were extracted using texture analysis and a novel quantitative mural nodularity feature developed for the study. Significant features on univariate analysis were combined with clinical variables to build a multivariate prediction model. Results: Within the study group of 103 patients, 76 (74%) had low-risk disease and 27 (26%) had high-risk disease. Quantitative imaging features were prognostic of low-vs. high-risk disease. The model based only on clinical variables achieved an AUC of 0.67 and 0.79 with the addition of quantitative imaging features. Conclusion: Quantitative image analysis of BD-IPMNs is a novel method that may enable risk stratification. External validation may provide a reliable non-invasive prognostic tool for clinicians.