Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Attiyeh, MA; Chakraborty, J; Gazit, L; Langdon-Embry, L; Gonen, M; Balachandran, VP; D'Angelica, MI; DeMatteo, RP; Jarnagin, WR; Kingham, TP; Allen, PJ; Do, RK; Simpson, AL

Published Date

  • February 2019

Published In

Volume / Issue

  • 21 / 2

Start / End Page

  • 212 - 218

PubMed ID

  • 30097414

Pubmed Central ID

  • PMC6367060

Electronic International Standard Serial Number (EISSN)

  • 1477-2574

Digital Object Identifier (DOI)

  • 10.1016/j.hpb.2018.07.016


  • eng

Conference Location

  • England