Page 67 - Read Online
P. 67
Page 25 Tovar et al. Art Int Surg 2023;3:14-26 https://dx.doi.org/10.20517/ais.2022.38
32. Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future
directions. J Big Data 2021;8:53. DOI PubMed PMC
33. Chari ST. Detecting early pancreatic cancer: problems and prospects. Semin Oncol 2007;34:284-94. DOI PubMed PMC
34. Petersen GM. Familial pancreatic cancer. Semin Oncol 2016;43:548-53. DOI PubMed PMC
35. Becker AE, Hernandez YG, Frucht H, Lucas AL. Pancreatic ductal adenocarcinoma: risk factors, screening, and early detection. World
J Gastroenterol 2014;20:11182-98. DOI PubMed PMC
36. Chari ST, Maitra A, Matrisian LM, et al. Early detection initiative: a randomized controlled trial of algorithm-based screening in
patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma. Contemp Clin Trials
2022;113:106659. DOI PubMed PMC
37. Permuth JB, Dezsi KB, Vyas S, et al. The Florida pancreas collaborative next-generation biobank: infrastructure to reduce disparities
and improve survival for a diverse cohort of patients with pancreatic cancer. Cancers 2021;13:809. DOI PubMed PMC
38. Boursi B, Finkelman B, Giantonio BJ, et al. A clinical prediction model to assess risk for pancreatic cancer among patients with new-
onset diabetes. Gastroenterology 2017;152:840-850.e3. DOI PubMed PMC
39. Boursi B, Finkelman B, Giantonio BJ, et al. A clinical prediction model to assess risk for pancreatic cancer among patients with
prediabetes. Eur J Gastroenterol Hepatol 2022;34:33-8. DOI PubMed PMC
40. Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell
2019;2:2. DOI
41. 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
42. Chen W, Butler RK, Zhou Y, Parker RA, Jeon CY, Wu BU. Prediction of pancreatic cancer based on imaging features in patients with
duct abnormalities. Pancreas 2020;49:413-9. DOI PubMed PMC
43. 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;163:1435-1446.e3. DOI
PubMed
44. 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
45. Polk SL, Choi JW, McGettigan MJ, et al. Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous
neoplasms to predict malignancy. World J Gastroenterol 2020;26:3458-71. DOI PubMed PMC
46. Tobaly D, Santinha J, Sartoris R, et al. CT-based radiomics analysis to predict malignancy in patients with intraductal papillary
mucinous neoplasm (IPMN) of the pancreas. Cancers 2020;12:3089. DOI PubMed PMC
47. Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary
mucinous neoplasms of the pancreas. Clin Transl Gastroenterol 2019;10:1-8. DOI PubMed PMC
48. Hanania AN, Bantis LE, Feng Z, et al. Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget 2016;7:85776-84.
DOI PubMed PMC
49. Momeni-Boroujeni A, Yousefi E, Somma J. Computer-assisted cytologic diagnosis in pancreatic FNA: an application of neural
networks to image analysis. Cancer Cytopathol 2017;125:926-33. DOI PubMed
50. Chen PT, Wu T, Wang P, et al. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study.
Radiology 2023;306:172-82. DOI PubMed
51. Zhang S, Zhou Y, Tang D, et al. A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of
pancreatic masses: a retrospective, multicenter, diagnostic study. EBioMedicine 2022;80:104022. DOI PubMed PMC
52. Kartal E, Schmidt TSB, Molina-Montes E, et al. A faecal microbiota signature with high specificity for pancreatic cancer. Gut
2022;71:1359-72. DOI PubMed PMC
53. Zaid M, Elganainy D, Dogra P, et al. Imaging-based subtypes of pancreatic ductal adenocarcinoma exhibit differential growth and
metabolic patterns in the pre-diagnostic period: implications for early detection. Front Oncol 2020;10:596931. DOI PubMed PMC
54. Pannala R, Leirness JB, Bamlet WR, Basu A, Petersen GM, Chari ST. Prevalence and clinical profile of pancreatic cancer-associated
diabetes mellitus. Gastroenterology 2008;134:981-7. DOI PubMed PMC
55. Sharma A, Smyrk TC, Levy MJ, Topazian MA, Chari ST. Fasting blood glucose levels provide estimate of duration and progression of
pancreatic cancer before diagnosis. Gastroenterology 2018;155:490-500.e2. DOI PubMed PMC
56. Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-“how-to” guide and critical
reflection. Insights Imaging 2020;11:91. DOI PubMed PMC
57. Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and artificial intelligence for biomarker
and prediction model development in oncology. Comput Struct Biotechnol J 2019;17:995-1008. DOI PubMed PMC
58. Kang JD, Clarke SE, Costa AF. Factors associated with missed and misinterpreted cases of pancreatic ductal adenocarcinoma. Eur
Radiol 2021;31:2422-32. DOI PubMed
59. Laffan TA, Horton KM, Klein AP, et al. Prevalence of unsuspected pancreatic cysts on MDCT. AJR Am J Roentgenol 2008;191:802-7.
DOI PubMed PMC
60. Sharib JM, Fonseca AL, Swords DS, et al. Surgical overtreatment of pancreatic intraductal papillary mucinous neoplasms: do the 2017
International Consensus Guidelines improve clinical decision making? Surgery 2018;164:1178-84. DOI PubMed
61. Hines OJ, Reber HA. Pancreatic surgery. Curr Opin Gastroenterol 2005;21:568-72. DOI PubMed