REFERENCES
2. 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;74:2913-21.
3. National cancer institute. SEER cancer statistics review (CSR) 1975-2015. Available from: https://seer.cancer.gov/archive/csr/1975_2015/ [Last accessed on 17 Mar 2023].
5. Poruk KE, Firpo MA, Adler DG, Mulvihill SJ. Screening for pancreatic cancer: why, how, and who? Ann Surg 2013;257:17-26.
6. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020;295:328-38.
7. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Available from: https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf [Last accessed on 17 Mar 2023].
8. Meyers PH, Nice CM Jr. Automated computer analysis of radiographic images. Arch Environ Health 1964;8:774-5.
9. Giger ML, Doi K, MacMahon H. Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys 1988;15:158-66.
10. Gross GW, Boone JM, Greco-Hunt V, Greenberg B. Neural networks in radiologic diagnosis. II. Interpretation of neonatal chest radiographs. Invest Radiol 1990;25:1017-23.
11. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-6.
12. Katzen J, Dodelzon K. A review of computer aided detection in mammography. Clin Imaging 2018;52:305-9.
13. O'Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017;14:169-86.
14. National cancer institute. Cancer stat facts: pancreatic cancer. Available from: https://seer.cancer.gov/statfacts/html/pancreas.html [Last accessed on 17 Mar 2023].
15. Dbouk M, Katona BW, Brand RE, et al. The multicenter cancer of pancreas screening study: impact on stage and survival. J Clin Oncol 2022;40:3257-66.
16. Canto MI, Kerdsirichairat T, Yeo CJ, et al. Surgical outcomes after pancreatic resection of screening-detected lesions in individuals at high risk for developing pancreatic cancer. J Gastrointest Surg 2020;24:1101-10.
17. U.S. Census Bureau. Available from: https://www.census.gov/quickfacts/fact/table/US/PST045221 [Last accessed on 17 Mar 2023].
18. Owens DK, Davidson KW, Krist AH, et al. Screening for pancreatic cancer: us preventive services task force reaffirmation recommendation statement. JAMA 2019;322:438-44.
19. Srivastava S, Koay EJ, Borowsky AD, et al. Cancer overdiagnosis: a biological challenge and clinical dilemma. Nat Rev Cancer 2019;19:349-58.
20. Canto MI, Almario JA, Schulick RD, et al. Risk of neoplastic progression in individuals at high risk for pancreatic cancer undergoing long-term surveillance. Gastroenterology 2018;155:740-751.e2.
21. Reni M, Cereda S, Balzano G, et al. Carbohydrate antigen 19-9 change during chemotherapy for advanced pancreatic adenocarcinoma. Cancer 2009;115:2630-9.
22. Goonetilleke KS, Siriwardena AK. Systematic review of carbohydrate antigen (CA 19-9) as a biochemical marker in the diagnosis of pancreatic cancer. Eur J Surg Oncol 2007;33:266-70.
23. Kim G, Bahl M. Assessing risk of breast cancer: a review of risk prediction models. J Breast Imaging 2021;3:144-55.
24. Alhazmi A, Alhazmi Y, Makrami A, et al. Application of artificial intelligence and machine learning for prediction of oral cancer risk. J Oral Pathol Med 2021;50:444-50.
25. Yeh MC, Wang YH, Yang HC, Bai KJ, Wang HH, Li YJ. Artificial intelligence-based prediction of lung cancer risk using nonimaging electronic medical records: deep learning approach. J Med Internet Res 2021;23:e26256.
26. Kakileti ST, Madhu HJ, Manjunath G, Wee L, Dekker A, Sampangi S. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics. Artif Intell Med 2020;105:101854.
27. Yin H, Zhang F, Yang X, et al. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022;12:973999.
28. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015;349:255-60.
30. Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Available from: https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html [Last accessed on 17 Mar 2023].
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.
33. Chari ST. Detecting early pancreatic cancer: problems and prospects. Semin Oncol 2007;34:284-94.
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.
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.
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.
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.
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.
40. Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell 2019;2:2.
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.
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.
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.
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.
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.
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.
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.
48. Hanania AN, Bantis LE, Feng Z, et al. Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget 2016;7:85776-84.
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.
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.
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.
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.
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.
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.
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.
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.
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.
58. Kang JD, Clarke SE, Costa AF. Factors associated with missed and misinterpreted cases of pancreatic ductal adenocarcinoma. Eur Radiol 2021;31:2422-32.
59. Laffan TA, Horton KM, Klein AP, et al. Prevalence of unsuspected pancreatic cysts on MDCT. AJR Am J Roentgenol 2008;191:802-7.
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.
62. Chen J, Yang R, Lu Y, Xia Y, Zhou H. Diagnostic accuracy of endoscopic ultrasound-guided fine-needle aspiration for solid pancreatic lesion: a systematic review. J Cancer Res Clin Oncol 2012;138:1433-41.
63. Hayashi T, Ishiwatari H, Yoshida M, et al. Rapid on-site evaluation by endosonographer during endoscopic ultrasound-guided fine needle aspiration for pancreatic solid masses. J Gastroenterol Hepatol 2013;28:656-63.
64. Alston E, Bae S, Eltoum IA. Atypical cytologic diagnostic category in EUS-FNA of the pancreas: follow-up, outcomes, and predictive models. Cancer Cytopathol 2014;122:428-34.
65. Savoy AD, Raimondo M, Woodward TA, et al. Can endosonographers evaluate on-site cytologic adequacy? A comparison with cytotechnologists. Gastrointest Endosc 2007;65:953-7.
66. Del Castillo E, Meier R, Chung M, et al. The microbiomes of pancreatic and duodenum tissue overlap and are highly subject specific but differ between pancreatic cancer and noncancer subjects. Cancer Epidemiol Biomarkers Prev 2019;28:370-83.
67. Sheller MJ, Edwards B, Reina GA, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 2020;10:12598.
68. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med 2020;3:119.
69. Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated Electronic Health Records. Int J Med Inform 2018;112:59-67.
70. Qayyum A, Ahmad K, Ahtazaz Ahsan M, Al-Fuqaha A, Qadir J. Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge. IEEE Open J Comput Soc 2022;3:172-84.
71. Maitra A. A clinical validation center for early detection of pancreatic cancer. Available from: https://grantome.com/grant/NIH/U01-CA200468-02 [Last accessed on 17 Mar 2023].
72. Koay EJ, Lee Y, Cristini V, et al. A visually apparent and quantifiable ct imaging feature identifies biophysical subtypes of pancreatic ductal adenocarcinoma. Clin Cancer Res 2018;24:5883-94.
74. Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics 2020;46:205-11.
75. van de Sande D, Van Genderen ME, Smit JM, et al. Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform 2022;29:e100495.
76. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405-10.
