TY - JOUR
T1 - A visually apparent and quantifiable CT imaging feature identifies biophysical subtypes of pancreatic ductal adenocarcinoma
AU - Koay, Eugene J.
AU - Lee, Yeonju
AU - Cristini, Vittorio
AU - Lowengrub, John S.
AU - Kang, Ya'an
AU - Anthony San Lucas, F.
AU - Hobbs, Brian P.
AU - Ye, Rong
AU - Elganainy, Dalia
AU - Almahariq, Muayad
AU - Amer, Ahmed M.
AU - Chatterjee, Deyali
AU - Yan, Huaming
AU - Park, Peter C.
AU - Rios Perez, Mayrim V.
AU - Li, Dali
AU - Garg, Naveen
AU - Reiss, Kim A.
AU - Yu, Shun
AU - Chauhan, Anil
AU - Zaid, Mohamed
AU - Nikzad, Newsha
AU - Wolff, Robert A.
AU - Javle, Milind
AU - Varadhachary, Gauri R.
AU - Shroff, Rachna T.
AU - Das, Prajnan
AU - Lee, Jeffrey E.
AU - Ferrari, Mauro
AU - Maitra, Anirban
AU - Taniguchi, Cullen M.
AU - Kim, Michael P.
AU - Crane, Christopher H.
AU - Katz, Matthew H.
AU - Wang, Huamin
AU - Bhosale, Priya
AU - Tamm, Eric P.
AU - Fleming, Jason B.
N1 - Publisher Copyright:
© 2018 American Association for Cancer Research.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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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U2 - 10.1158/1078-0432.CCR-17-3668
DO - 10.1158/1078-0432.CCR-17-3668
M3 - Article
C2 - 30082477
AN - SCOPUS:85057847033
SN - 1078-0432
VL - 24
SP - 5883
EP - 5894
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 23
ER -