PanIN or IPMN? Redefining Lesion Size in 3 Dimensions

Author:

Kiemen Ashley L.12,Dequiedt Lucie3,Shen Yu3,Zhu Yutong3,Matos-Romero Valentina3,Forjaz André3,Campbell Kurtis1,Dhana Will3,Cornish Toby3,Braxton Alicia M.3,Wu Pei-Hsun3,Fishman Elliot K.3,Wood Laura D.12,Wirtz Denis123,Hruban Ralph H.12

Affiliation:

1. Pathology

2. Oncology

3. Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC

Abstract

Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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