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Published on: February 1, 2020
CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study
Francesca Rigiroli1, Jocelyn Hoye1, Reginald Lerebours1
1From the Departments of Radiology (F.R., K.J.L., M.M., P.L., Y.D., F.R.S., E.S., D.M.) and Radiation Oncology (K.J.L.), Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 27710; Multi-Dimensional Image Processing Laboratory, Duke Radiology, Duke University School of Medicine, Durham, NC (F.R., M.M., P.L., Y.D., F.R.S., D.M.); progettoDiventerò, Bracco Foundation, Milan, Italy (F.R.); Carl E. Ravin Advanced Imaging Laboratories (J.H., E.S.), Department of Biostatistics and Bioinformatics (R.L., S.L.), and Duke Electrical and Computer Engineering (K.J.L.), Duke University, Durham, NC; Department of Biostatistics, Yale University, New Haven, Conn (C.L.); Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany (M.M.); Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China (P.L.); Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (Y.D.); Duke Cancer Center, Duke Health, Durham, NC (N.B.M., S.Z.); and Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (D.E.M.).
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.
