CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.
Abstract
Background Current imaging methods for prediction of complete margin resection (R0)
in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose
To investigate whether tumor-related and perivascular CT radiomic features improve
preoperative assessment of arterial involvement in patients with surgically proven
PDAC. Materials and Methods This retrospective study included consecutive patients
with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional
segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery
(SMA) was performed on preoperative CT images with radiomic features extracted to
characterize morphology, intensity, texture, and task-based spatial information. The
reference standard was the pathologic SMA margin status of the surgical sample: SMA
involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm
from margin). The preoperative assessment of SMA involvement by a fellowship-trained
radiologist in multidisciplinary consensus was the comparison. High reproducibility
(intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used
to select features included in the logistic regression model. Results A total of 194
patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85
years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant
therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved
SMA. A total of 1695 CT radiomic features were extracted. The model with five features
(maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation,
minimum distance, square gray level co-occurrence matrix correlation) showed a better
performance compared with the radiologist assessment (model vs radiologist area under
the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77)
and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model
based on tumor-related and perivascular CT radiomic features improved the detection
of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma.
© RSNA, 2021 <i>Online supplemental material is available for this article.</i> See
also the editorial by Do and Kambadakone in this issue.
Type
Journal articlePermalink
https://hdl.handle.net/10161/23864Published Version (Please cite this version)
10.1148/radiol.2021210699Publication Info
Rigiroli, Francesca; Hoye, Jocelyn; Lerebours, Reginald; Lafata, Kyle J; Li, Cai;
Meyer, Mathias; ... Marin, Daniele (2021). CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal
Adenocarcinoma: A Pilot Study. Radiology. pp. 210699. 10.1148/radiol.2021210699. Retrieved from https://hdl.handle.net/10161/23864.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Kyle Jon Lafata
Assistant Professor of Radiation Oncology
Kyle Lafata is an Assistant Professor of Radiology, Radiation Oncology, and Electrical
& Computer Engineering at Duke University. As an imaging physicist and data scientist,
Dr. Lafata’s research interests are in image-based phenotyping and computational biomarkers.
His dissertation work focused on nature-inspired computational methods and soft-computing
paradigms, including the applied analysis of stochastic differential equations, self-organization,
and quantum machine learning (i
Reginald (Gino) Lerebours
Biostatistician II
Education: Masters Degree, Biostatistics. Harvard University. 2017Bachelors Degree,
Statistics. North Carolina State University. 2015Overview: Gino currently collaborates
with researchers, residents, and clinicians in the Departments of Surgery, Radiology
and Infectious Diseases. His main research interests and experience are in statistical
programming, data management, statistical modeling, statistical consulting and statistical
education.
Sheng Luo
Professor of Biostatistics & Bioinformatics
Daniele Marin
Associate Professor of Radiology
Liver Imaging Dual Energy CT CT Protocol Optimization Dose Reduction Strategies for
Abdominal CT Applications
Ehsan Samei
Reed and Martha Rice Distinguished Professor of Radiology
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American
medical physicist. He is a tenured Professor of Radiology, Medical Physics, Biomedical
Engineering, Physics, and Electrical and Computer Engineering at Duke University,
where he also serves as the Chief Imaging Physicist for Duke University Health System,
the director of the Carl E Ravin Advanced Imaging Laboratories, and the director of
Center for Virtual Imaging Trials. He is certi
Sabino Zani Jr.
Associate Professor of Surgery
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