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Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study

Authors :
Terence M. Williams
Joseph M. Herman
Priya Bhosale
Mohamed Zaid
Anirban Maitra
Michael V. Knopp
Robert A. Wolff
Gauri R. Varadhachary
Eugene J. Koay
Mark W. Hurd
Lauren Widmann
Kevin Sun
Jun Zhao
Jie Zhang
Huamin Wang
Matthew H.G. Katz
Eric P. Tamm
A. Dai
Source :
Cancers, Vol 12, Iss 3656, p 3656 (2020), Cancers, Volume 12, Issue 12
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta<br />q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-na&iuml<br />ve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.

Details

Language :
English
ISSN :
20726694
Volume :
12
Issue :
3656
Database :
OpenAIRE
Journal :
Cancers
Accession number :
edsair.doi.dedup.....a52c032cc71e8513448791018b6c09f3