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CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.

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Date
2021-09-07
Authors
Rigiroli, Francesca
Hoye, Jocelyn
Lerebours, Reginald
Lafata, Kyle J
Li, Cai
Meyer, Mathias
Lyu, Peijie
Ding, Yuqin
Schwartz, Fides R
Mettu, Niharika B
Zani, Sabino
Luo, Sheng
Morgan, Desiree E
Samei, Ehsan
Marin, Daniele
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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.
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Journal article
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https://hdl.handle.net/10161/23864
Published Version (Please cite this version)
10.1148/radiol.2021210699
Publication 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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Scholars@Duke

Lafata

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
Lerebours

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.
Luo

Sheng Luo

Professor of Biostatistics & Bioinformatics
Marin

Daniele Marin

Associate Professor of Radiology
Liver Imaging Dual Energy CT CT Protocol Optimization Dose Reduction Strategies for Abdominal CT Applications
Samei

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
Zani

Sabino Zani Jr.

Associate Professor of Surgery
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