1.
Rahib, L, Smith, BD, Aizenberg, R, Rosenzweig, AB, Fleshman, JM, Matrisian, LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913–2921.
Google Scholar |
Crossref |
Medline2.
DiMagno, EP, Reber, HA, Tempero, MA. AGA technical review on the epidemiology, diagnosis, and treatment of pancreatic ductal adenocarcinoma. American Gastroenterological Association. Gastroenterology. 1999;117(6):1464–1484.
Google Scholar |
Crossref |
Medline3.
Fogel, EL, Shahda, S, Sandrasegaran, K, et al. A multidisciplinary approach to pancreas cancer in 2016: a review. Am J Gastroenterol. 2017;112(4):537–554.
Google Scholar |
Crossref |
Medline4.
Ansari, D, Friess, H, Bauden, M, Samnegård, J, Andersson, R. Pancreatic cancer: disease dynamics, tumor biology and the role of the microenvironment. Oncotarget. 2018;9(5):6644–6651.
Google Scholar |
Crossref |
Medline5.
Keek, SA, Leijenaar, RT, Jochems, A, Woodruff, HC. A review on radiomics and the future of theranostics for patient selection in precision medicine. Br J Radiol. 2018;91(1091):20170926.
Google Scholar |
Crossref |
Medline6.
Cheng, S-H, Cheng, Y-J, Jin, Z-Y, Xue, HD. Unresectable pancreatic ductal adenocarcinoma: role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy. Eur J Radiol. 2019;113:188–197.
Google Scholar |
Crossref |
Medline7.
Eilaghi, A, Baig, S, Zhang, Y, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma—a quantitative analysis. BMC Med Imaging. 2017;17(1):38.
Google Scholar |
Crossref |
Medline8.
Kim, BR, Kim, JH, Ahn, SJ, et al. CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis. Eur Radiol. 2019;29(1):362–372.
Google Scholar |
Crossref |
Medline9.
Bian, Y, Jiang, H, Ma, C, et al. Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer. Abdom Radiol. 2020;45(3):759–773.
Google Scholar |
Crossref |
Medline10.
Leijenaar, RTH, Carvalho, S, Hoebers, FJP, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol (Madr). 2015;54(9):1423–1429.
Google Scholar |
Crossref |
Medline11.
Papadoniou, N, Kosmas, C, Gennatas, K, et al. Prognostic factors in patients with locally advanced (unresectable) or metastatic pancreatic adenocarcinoma: a retrospective analysis. Anticancer Res. 2008;28(1B):543–549.
Google Scholar |
Medline12.
Bittoni, A, Pellei, C, Lanese, A, et al. Prognostic factors in advanced pancreatic cancer patients receiving second-line chemotherapy: a single institution experience. Transl Cancer Res. 2018;7(3):1190–1198.
Google Scholar |
Crossref13.
Yun, G, Kim, YH, Lee, YJ, et al. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep. 2018;8(1):7226.
Google Scholar |
Crossref |
Medline14.
Chakraborty, J, Langdon-Embry, L, Cunanan, KM, et al. Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients. PLoS One. 2017;12(12):e0188022.
Google Scholar |
Crossref |
Medline15.
Cassinotto, C, Chong, J, Zogopoulos, G, et al. Resectable pancreatic adenocarcinoma: role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol. 2017;90:152–158.
Google Scholar |
Crossref |
Medline16.
Sandrasegaran, K, Lin, Y, Asare-Sawiri, M, Taiyini, T, Tann, M. CT texture analysis of pancreatic cancer. Eur Radiol. 2019;29(3):1067–1073.
Google Scholar |
Crossref |
Medline17.
Khalvati, F, Zhang, Y, Baig, S, et al. Prognostic value of CT radiomic features in resectable pancreatic ductal adenocarcinoma. Sci Rep. 2019;9(1):5449.
Google Scholar |
Crossref |
Medline18.
Notta, F, Chan-Seng-Yue, M, Lemire, M, et al. A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns. Nature. 2016;538(7625):378–382.
Google Scholar |
Crossref |
Medline19.
Sohal, DP, Walsh, RM, Ramanathan, RK, Khorana, AA. Pancreatic adenocarcinoma: treating a systemic disease with systemic therapy. JNCI J Natl Cancer Inst. 2014;106(3):dju011–dju011.
Google Scholar |
Crossref20.
Qi, Q, Zhuang, L, Shen, Y, et al. A novel Systemic Inflammation Response Index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 2016;122(14):2158–2167.
Google Scholar |
Crossref |
Medline21.
Kamisawa, T, Wood, LD, Itoi, T, Takaori, K. Pancreatic cancer. Lancet (London, England). 2016;388(10039):73–85.
Google Scholar |
Crossref |
Medline22.
Makohon-Moore, AP, Zhang, M, Reiter, JG, et al. Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer. Nat Genet. 2017;49(3):358–366.
Google Scholar |
Crossref |
Medline23.
Zhao, B, Tan, Y, Tsai, W-Y, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428.
Google Scholar |
Crossref |
Medline24.
Yamashita, R, Perrin, T, Chakraborty, J, et al. Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol. 2020;30(1):195–205.
Google Scholar |
Crossref |
Medline25.
Griethuysen, JV, Fedorov, A, Aucoin, N, et al. Excluded Radiomic Features—Pyradiomics 2.2.0 Documentation. 2016;2.
Google Scholar26.
Berenguer, R, Pastor-Juan, MR, Canales-Vázquez, J, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology. 2018;288(2):407–415.
Google Scholar |
Crossref |
Medline27.
Lee, S-H, Cho, H, Lee, HY, et al. Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer. Cancer Imaging. 2019;19(1):54.
Google Scholar |
Crossref |
Medline28.
Attiyeh, MA, Chakraborty, J, Doussot, A, et al. Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis. Ann Surg Oncol. 2018;25(4):1034–1042.
Google Scholar |
Crossref |
Medline29.
Guo, C, Zhuge, X, Wang, Q, et al. The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging. 2018;18(1):37.
Google Scholar |
Crossref |
Medline30.
Cozzi, L, Comito, T, Fogliata, A, et al. Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. PLoS One. 2019;14(1):e0210758.
Google Scholar |
Crossref |
Medline31.
Boland, GW, O’Malley, ME, Saez, M, et al. Pancreatic-phase versus portal vein-phase helical CT of the pancreas: optimal temporal window for evaluation of pancreatic adenocarcinoma. AJR Am J Roentgenol. 1999;172(3):605–608.
Google Scholar |
Crossref |
Medline32.
Lu, DS, Vedantham, S, Krasny, RM, et al. Two-phase helical CT for pancreatic tumors: pancreatic versus hepatic phase enhancement of tumor, pancreas, and vascular structures. Radiology. 1996;199(3):697–701.
Google Scholar |
Crossref |
Medline33.
Graf, O, Boland, GW, Warshaw, AL, et al. Arterial versus portal venous helical CT for revealing pancreatic adenocarcinoma: conspicuity of tumor and critical vascular anatomy. AJR Am J Roentgenol. 1997;169(1):119–123.
Google Scholar |
Crossref |
Medline34.
Foley, WD . Special focus session. RadioGraphics 2002;22(3):701–719.
Google Scholar |
Crossref |
Medline35.
Aung, KL, Fischer, SE, Denroche, RE, et al. Genomics-driven precision medicine for advanced pancreatic cancer: early results from the COMPASS trial. Clin Cancer Res. 2018;24(6):1344–1354.
Google Scholar |
Crossref |
Medline36.
Da-ano, R, Masson, I, Lucia, F, et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep. 2020;10(1):10248.
Google Scholar |
Crossref |
Medline
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