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Clin Cancer Res. 2018 Dec 1;24(23):5883-5894. doi: 10.1158/1078-0432.CCR-17-3668. Epub 2018 Aug 6.

A Visually Apparent and Quantifiable CT Imaging Feature Identifies Biophysical Subtypes of Pancreatic Ductal Adenocarcinoma.

Author information

1
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas. ekoay@mdanderson.org.
2
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
3
Center for Precision Biomedicine, The University of Texas Health Science Center, Houston, Texas.
4
Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas.
5
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
6
Department of Mathematics, University of California, Irvine, California.
7
Department of Biomedical Engineering, University of California, Irvine, California.
8
Chao Family Comprehensive Cancer Center, University of California, Irvine, California.
9
Center for Complex Biological Systems, University of California, Irvine, California.
10
Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
11
Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas.
12
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
13
Deparment of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, Texas.
14
Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
15
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
16
Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
17
Department of Medical Oncology, The University of Pennsylvania Abramson Cancer Center, Philadelphia, Pennsylvania.
18
Department of Internal Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania.
19
Department of Radiology, The University of Pennsylvania, Philadelphia, Pennsylvania.
20
Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
21
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
#
Contributed equally

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.

PMID:
30082477
PMCID:
PMC6279613
[Available on 2019-12-01]
DOI:
10.1158/1078-0432.CCR-17-3668

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