Predictive modeling for voxel-based quantification of imaging-based subtypes of pancreatic ductal adenocarcinoma (Pdac): A multi-institutional study

Mohamed Zaid, Lauren Widmann, Annie Dai, Kevin Sun, Jie Zhang, Jun Zhao, Mark W. Hurd, Gauri R. Varadhachary, Robert A. Wolff, Anirban Maitra, Matthew H.G. Katz, Joseph M. Herman, Huamin Wang, Michael V. Knopp, Terence M. Williams, Priya Bhosale, Eric P. Tamm, Eugene J. Koay

Research output: Contribution to journalArticlepeer-review

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; 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-naïve 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.

Original languageEnglish (US)
Article number3656
Pages (from-to)1-15
Number of pages15
JournalCancers
Volume12
Issue number12
DOIs
StatePublished - Dec 5 2020

Keywords

  • Computed tomography
  • Imaging biomarker
  • Machine learning
  • Pancreatic cancer
  • Radiomics

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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