Page 138 - Read Online
P. 138
De Robertis et al. Art Int Surg 2023;3:166-79 https://dx.doi.org/10.20517/ais.2023.18 Page 178
AISP (Italian Association for the Study of the Pancreas) registry. Am J Gastroenterol 2019;114:665-70. DOI
57. Chhoda A, Vodusek Z, Wattamwar K, et al. Late-stage pancreatic cancer detected during high-risk individual surveillance: a
systematic review and meta-analysis. Gastroenterology 2022;162:786-98. DOI
58. Chu LC, Park S, Kawamoto S, et al. Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from
normal pancreatic tissue. AJR Am J Roentgenol 2019;213:349-57. DOI
59. Qureshi TA, Gaddam S, Wachsman AM, et al. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of
pre-diagnostic computed tomography images. Cancer Biomark 2022;33:211-7. DOI PubMed PMC
60. Javed S, Qureshi TA, Gaddam S, et al. Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed
tomography images. Front Oncol 2022;12:1007990. DOI PubMed PMC
61. Mukherjee S, Patra A, Khasawneh H, et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic
computed tomography scans at a substantial lead time before clinical diagnosis. Gastroenterology 2022;163:1435-46.e3. DOI
62. Jeon SK, Kim JH, Yoo J, Kim JE, Park SJ, Han JK. Assessment of malignant potential in intraductal papillary mucinous neoplasms of
the pancreas using MR findings and texture analysis. Eur Radiol 2021;31:3394-404. DOI
63. Chakraborty J, Midya A, Gazit L, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas.
Med Phys 2018;45:5019-29. DOI PubMed PMC
64. Permuth JB, Choi J, Balarunathan Y, et al. Combining radiomic features with a miRNA classifier may improve prediction of malignant
pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget 2016;7:85785-97. DOI PubMed PMC
65. Zhang H, Meng Y, Li Q, et al. Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal
adenocarcinoma in patients with chronic pancreatitis. Eur Radiol 2022;32:6336-47. DOI
66. Wei R, Lin K, Yan W, et al. Computer-aided diagnosis of pancreas serous cystic neoplasms: a radiomics method on preoperative
MDCT images. Technol Cancer Res Treat 2019;18:1533033818824339. DOI PubMed PMC
67. Chang N, Cui L, Luo Y, Chang Z, Yu B, Liu Z. Development and multicenter validation of a CT-based radiomics signature for
discriminating histological grades of pancreatic ductal adenocarcinoma. Quant Imaging Med Surg 2020;10:692-702. DOI PubMed
PMC
68. Tikhonova VS, Karmazanovsky GG, Kondratyev EV, et al. Radiomics model-based algorithm for preoperative prediction of
pancreatic ductal adenocarcinoma grade. Eur Radiol 2023;33:1152-61. DOI PubMed
69. Gu D, Hu Y, Ding H, et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol
2019;29:6880-90. DOI
70. De Robertis R, Maris B, Cardobi N, et al. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine
tumors? Eur Radiol 2018;28:2582-91. DOI
71. Mori M, Palumbo D, Muffatti F, et al. Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms
(PanNENs) based on CT radiomic features. Eur Radiol 2023;33:4412-21. DOI
72. Salinas-Miranda E, Healy GM, Grünwald B, et al. Correlation of transcriptional subtypes with a validated CT radiomics score in
resectable pancreatic ductal adenocarcinoma. Eur Radiol 2022;32:6712-22. DOI
73. Bailey P, Chang DK, Nones K, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 2016;531:47-52.
DOI PubMed
74. Rigiroli F, Hoye J, Lerebours R, et al. CT radiomic features of superior mesenteric artery involvement in pancreatic ductal
adenocarcinoma: a pilot study. Radiology 2021;301:610-22. DOI PubMed PMC
75. Bian Y, Guo S, Jiang H, et al. Relationship between radiomics and risk of lymph node metastasis in pancreatic ductal adenocarcinoma.
Pancreas 2019;48:1195-203. DOI PubMed PMC
76. De Robertis R, Geraci L, Tomaiuolo L, et al. Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT
texture analysis. Radiol Med 2022;127:1079-84. DOI
77. Tang TY, Li X, Zhang Q, et al. Development of a novel multiparametric MRI radiomic nomogram for preoperative evaluation of early
recurrence in resectable pancreatic cancer. J Magn Reson Imaging 2020;52:231-45. DOI PubMed PMC
78. De Robertis R, Tomaiuolo L, Pasquazzo F, et al. Correlation between ADC histogram-derived metrics and the time to metastases in
resectable pancreatic adenocarcinoma. Cancers 2022;14:6050. DOI PubMed PMC
79. Kulkarni A, Carrion-Martinez I, Jiang NN, et al. Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of
resection margin status and high-risk features. Eur Radiol 2020;30:2853-60. DOI
80. De Robertis R, Beleù A, Cardobi N, et al. Correlation of MR features and histogram-derived parameters with aggressiveness and
outcomes after resection in pancreatic ductal adenocarcinoma. Abdom Radiol 2020;45:3809-18. DOI
81. Kim HS, Kim YJ, Kim KG, Park JS. Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer.
Sci Rep 2019;9:17389. DOI PubMed PMC
82. Xie T, Wang X, Li M, Tong T, Yu X, Zhou Z. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model
and TNM staging for survival estimation after curative resection. Eur Radiol 2020;30:2513-24. DOI
83. Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2019;29:1067-73.
DOI PubMed
84. Healy GM, Salinas-Miranda E, Jain R, et al. Pre-operative radiomics model for prognostication in resectable pancreatic
adenocarcinoma with external validation. Eur Radiol 2022;32:2492-505. DOI
85. Borhani AA, Dewan R, Furlan A, et al. Assessment of response to neoadjuvant therapy using CT texture analysis in patients with