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Cited by 110Pancreatic and Hepatic Oncology ResearchRadiomics and Machine Learning in Medical ImagingMathematical Biology Tumor Growth

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

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Koay, Eugene J., Lee, Yeonju, Cristini, Vittorio, Lowengrub, John S., Kang, Ya'an, Lucas, F. Anthony San, Hobbs, Brian P., Ye, Rong +30 more·2018·Clinical Cancer Research
DOI 10.1158/1078-0432.ccr-17-3668PMC PMC6279613

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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.

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  • Eugene Koay has previously characterized subtypes of PDAC on CT scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology, a higher rate of common pathway mutations, and poorer clinical outcomes compared with inconspicuous (low delta) tumors.

    “Eugene Koay from The University of Texas MD Anderson Cancer Center (MDACC) has previously characterized subtypes of PDAC on CT scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology, a higher rate of common pathway mutations, and poorer clinical outcomes compared with inconspicuous (low delta) tumors.161”

    From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [161]

  • Radiomics and quantitative imaging studies have shown that enhancement and morphology of primary pancreatic tumours have biological underpinnings and clinical relevance.

    “Other applications of radiomics and quantitative imaging approaches have shown that the enhancement and morphology of the primary tumours have biological underpinnings and clinical relevance,113–115”

    From Early detection of pancreatic cancer (2020) · cites this paper as [114]

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Authors
Koay, E. J., Lee, Y., Cristini, V., Lowengrub, J. S., Kang, Y., Lucas, F. A. S., Hobbs, B. P., Ye, R., Elganainy, D., Almahariq, M., Amer, A. M., Chatterjee, D., Yan, H., Park, P. C., Rios Perez, M. V., Li, D., Garg, N., Reiss, K. A., Yu, S., Chauhan, A., Zaid, M., Nikzad, N., Wolff, R. A., Javle, M., Varadhachary, G. R., Shroff, R. T., Das, P., Lee, J. E., Ferrari, M., Maitra, A., Taniguchi, C. M., Kim, M. P., Crane, C. H., Katz, M. H., Wang, H., Bhosale, P., Tamm, E. P., & Fleming, J. B.
Affiliations
  • The University of Texas MD Anderson Cancer Center (US)
  • 1Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • 2Center for Precision Biomedicine, The University of Texas Health Science Center, Houston, Texas.
  • University of California, Irvine (US)
  • The University of Texas Medical Branch at Galveston (US)
  • 9Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Abramson Cancer Center (US)
  • University of Pennsylvania (US)
  • 3Department of Nanomedicine, Houston Methodist Research Institute, Houston, Texas.
  • Memorial Sloan Kettering Cancer Center (US)
Corresponding author
Koay, Eugene J.
ORCID
Venue
Clinical Cancer Research
Publisher
American Association for Cancer Research (AACR)
Published
2018-12-01
Volume
24
Issue
23
Pages
5883-5894
ISSN
  • 1078-0432 (ISSN-L)
  • 1557-3265
DOI
10.1158/1078-0432.ccr-17-3668
PMC
PMC6279613
OpenAlex
W2886778091
Cited by
110
References
5
Citations by year
2026: 5 · 2025: 14 · 2024: 16 · 2023: 11 · 2022: 17 · 2021: 17 · 2020: 22 · 2019: 7 · 2018: 1
FWCI
4.42
Type
journal article
Language
en
Open access
Locations
  • Clinical Cancer Research (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
NIH (U54CA210181-01, U54CA143837, and U01CA196403, NSF DMS- 1716737, NIH 1U01CA196403, 1U01CA213759, 1R01CA226537, 1R01CA222007, 1U54CA217378-01A1, P50GM76516, and P30CA062203)
Topics
  • Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
Keywords
pancreatic ductal adenocarcinoma, tumor heterogeneity, CT imaging features, tumor stroma, mesenchymal subtype, tumor growth modeling, metastasis, survival, molecular subtypes
MeSH terms
Exome Sequencing, Adenocarcinoma, Algorithms, Biopsy, Combined Modality Therapy, DNA Mutational Analysis, Humans, Image Processing, Computer-Assisted, Immunohistochemistry, Models, Theoretical, Neoplasm Metastasis, Neoplasm Staging, Tomography, X-Ray Computed, Carcinoma, Pancreatic Ductal, Cell Line, Tumor, Tumor Burden, Neoplasm Grading

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