A visually apparent and quantifiable CT imaging feature identifies biophysical subtypes of pancreatic ductal adenocarcinoma

Eugene J Koay, Yeonju Lee, Vittorio Cristini, John S Lowengrub, Ya'an Kang, F Anthony San Lucas, Brian P Hobbs, Rong Ye, Dalia Elganainy, Muayad Almahariq, Ahmed M Amer, Shun Yu, Deyali Chatterjee, Huaming Yan, Peter C Park, Mayrim V Rios Perez, Dali Li, Naveen Garg, Kim Reiss, Anil ChauhanMohamed M Zaid, Newsha Nikzad, Robert A Wolff, Milind Javle, Gauri R Varadhachary, Rachna T Shroff, Prajnan Das, Jeffrey E Lee, Mauro Ferrari, Anirban Maitra, Cullen M Taniguchi, Michael P Kim, Christopher H Crane, Matthew H G Katz, Huamin Wang, Priya Bhosale, Eric P Tamm, Jason B Fleming

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology. Experimental Design: We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology. Results: In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth. Conclusions: At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease.

Original languageEnglish (US)
Pages (from-to)5883-5894
Number of pages12
JournalClinical Cancer Research
Volume24
Issue number23
Early online dateAug 6 2018
DOIs
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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