CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.
Date
2021-09-07
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Citation Stats
Attention Stats
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 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.
Type
Department
Description
Provenance
Subjects
Citation
Permalink
Published Version (Please cite this version)
Publication Info
Rigiroli, Francesca, Jocelyn Hoye, Reginald Lerebours, Kyle J Lafata, Cai Li, Mathias Meyer, Peijie Lyu, Yuqin Ding, et al. (2021). CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study. Radiology. p. 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.
Collections
Scholars@Duke
Reginald (Gino) Lerebours
Education: Masters Degree, Biostatistics. Harvard University. 2017
Bachelors Degree, Statistics. North Carolina State University. 2015
Overview: 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.
Kyle Jon Lafata
Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University with faculty appointments in Radiation Oncology, Radiology, Pathology, Medical Physics, and Electrical & Computer Engineering. He joined the faculty at Duke in 2020 following postdoctoral training at the US Department of Veterans Affairs. His dissertation work focused on the applied analysis of stochastic partial differential equations and high-dimensional image phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems).
Prof. Lafata has worked in various areas of computational medicine and biology, resulting in over 55 academic papers, 20 invited talks, and more than 60 national conference presentations. At Duke, the Lafata Laboratory focuses on the theory, development, and application of computational oncology. The lab interrogates disease at different length-scales of its biological organization via high-performance computing, multiscale modeling, advanced imaging technology, and the applied analysis of stochastic partial differential equations. Current research interests include tumor topology, cellular dynamics, tumor immune microenvironment, drivers of radiation resistance and immune dysregulation, molecular insight into tissue heterogeneity, and biologically-guided adaptative treatment strategies.
Sabino Zani
Sheng Luo
Ehsan Samei
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American medical physicist. He is the Reed and Martha Rice Distinguished Professor of Radiology, and Professor of Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University. He serves as the Chief Imaging Physicist for Duke University Health System, the Director of the Carl E Ravin Advanced Imaging Laboratories and the Center for Virtual Imaging Trials (CVIT), and co-PI of one the five Centers of Excellence in Regulatory Science and Innovation (CERSI), Triangle CERSI. He is certified by the American Board of Radiology, recognized as a Distinguished Investigator by the Academy of Radiology Research, and awarded Fellow by five professional organization. He founded/co-founded the Duke Medical Physics Program, the Duke Imaging Physics Residency Program, the Duke Clinical Imaging Physics Group, the Center for Virtual Imaging Trials, and the Society of Directors of Academic Medical Physics Programs (SDAMPP). He has held senior leadership positions in the AAPM, SPIE, SDAMPP, and RSNA, including election to the presidency of the SEAAPM (2010-2011), SDAMPP (2011), and AAPM (2023).
Dr. Samei's scientific expertise include x-ray imaging, theoretical imaging models, simulation methods, and experimental techniques in medical image formation, quantification, and perception. His research aims to bridge the gap between scientific scholarship and clinical practice, in the meaningful realization of translational research, and in clinical processes that are informed by scientific evidence. He has advanced image quality and safety metrics and radiometrics that are clinically relevant and that can be used to design, optimize, and monitor interpretive and quantitative performance of imaging techniques. These have been implemented in advanced imaging performance characterization, procedural optimization, and clinical dose and quality analytics. His most recent research interests have been virtual clinical trial across a broad spectrum of oncologic, pulmonary, cardiac, and vascular diseases, and developing methodological advances that provide smart fusions of principle-informed and AI-based, data-informed approaches to scientific inquiry.
Dr. Samei has mentored over 140 trainees (graduate and postgraduate). He has more than 1400 scientific publications including more than 360 referred journal articles, 600 conference presentations, and 4 books. Citations to his work is reflected in an h-index of 74 and a Weighted Relative Citation Ratio of 613. His laboratory of over 20 researchers has been supported continuously over two decades by 44 extramural grants, culminating in a NIH Program Project grant in 2021 to establish the national Center for Virtual Imaging Trials (CVIT), joining a small number of prominent Biomedical Technology Research Centers across the nation. In 2023, he, along with 3 other PIs, was awarded to lead one of five national Centers of Excellence in Regulatory Science and Innovation (Triangle CERSI) by the FDA.
Daniele Marin
Liver Imaging
Dual Energy CT
CT Protocol Optimization
Dose Reduction Strategies for Abdominal CT Applications
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.