REFERENCES
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424.
2. Sullivan R, Alatise OI, Anderson BO, et al. Global cancer surgery: delivering safe, affordable, and timely cancer surgery. Lancet Oncol 2015;16:1193-224.
3. Knight SR, Shaw CA, Pius R, et al. Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries. The Lancet 2021;397:387-97.
4. Jones RP, Poston GJ. Resection of liver metastases in colorectal cancer in the era of expanding systemic therapy. Annu Rev Med 2017;68:183-96.
5. Keller DS, Berho M, Perez RO, Wexner SD, Chand M. The multidisciplinary management of rectal cancer. Nat Rev Gastroenterol Hepatol 2020;17:414-29.
6. Prades J, Remue E, van Hoof E, Borras JM. Is it worth reorganising cancer services on the basis of multidisciplinary teams (MDTs)? Health Policy 2015;119:464-74.
7. Hansen MFC, Storkholm JH, Hansen CP. The results of pancreatic operations after the implementation of multidisciplinary team conference (MDT): a quality improvement study. Int J Surg 2020;77:105-10.
8. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018;268:70-6.
9. Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions. Nat Biomed Eng 2017;1:691-6.
10. Mitchell TM. Machine learning. 1st ed. USA: McGraw-Hill, Inc.; 1997.
11. Kernbach JM, Staartjes VE. Machine learning-based clinical prediction modeling - a practical guide for clinicians. arXiv 2020;11:37.
12. Willems JL, Abreu-Lima C, Arnaud P, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med 1991;325:1767-73.
13. Singh R, Kalra MK, Nitiwarangkul C, et al. Deep learning in chest radiography: detection of findings and presence of change. PLoS One 2018;13:e0204155.
14. Ahmad OF, Soares AS, Mazomenos E, et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol 2019;4:71-80.
15. Pereira SP, Oldfield L, Ney A, et al. Early detection of pancreatic cancer. The Lancet Gastroenterol & Hepatol 2020;5:698-710.
16. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019;110:12-22.
17. Jin P, Ji X, Kang W, et al. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 2020;146:2339-50.
18. Pantelis AG, Stravodimos GK, Lapatsanis DP. A scoping review of artificial intelligence and machine learning in bariatric and metabolic surgery: current status and future perspectives. Obes Surg 2021;31:4555-63.
19. Maier-Hein L, Eisenmann M, Sarikaya D, et al. Surgical data science - from concepts toward clinical translation. Med Image Anal 2022;76:102306.
20. Henn J, Buness A, Schmid M, Kalff JC, Matthaei H. Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review. Langenbecks Arch Surg 2022;407:51-61.
21. Garrow CR, Kowalewski KF, Li L, et al. Machine learning for surgical phase recognition: a systematic review. Ann Surg 2021;273:684-93.
22. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71.
23. Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.2; 2021. Available from: https://training.cochrane.org/handbook [Last accessed on 22 Sep 2022].
24. Kalkum E, Klotz R, Seide S, et al. Systematic reviews in surgery-recommendations from the study center of the german society of surgery. Langenbecks Arch Surg 2021;406:1723-31.
25. Maffulli N, Rodriguez HC, Stone IW, et al. Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol. J Orthop Surg Res 2020;15:478.
27. Poole D, Mackworth A, Goebel R. Computational intelligence: a logical approach. USA: Oxford University Press, Inc.; 1997.
28. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014;11:e1001744.
29. Stojadinovic A, Bilchik A, Smith D, et al. Clinical decision support and individualized prediction of survival in colon cancer: bayesian belief network model. Ann Surg Oncol 2013;20:161-74.
30. Ting W, Chang H, Chang C, Lu C. Developing a novel machine learning-based classification scheme for predicting SPCs in colorectal cancer survivors. Applied Sciences 2020;10:1355.
31. Velez-Serrano JF, Velez-Serrano D, Hernandez-Barrera V, et al. Prediction of in-hospital mortality after pancreatic resection in pancreatic cancer patients: a boosting approach via a population-based study using health administrative data. PLoS One 2017;12:e0178757.
32. Bhandari M, Nallabasannagari AR, Reddiboina M, et al. Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study. BJU Int 2020;126:350-8.
33. Mourad M, Moubayed S, Dezube A, et al. Machine learning and feature selection applied to SEER data to reliably assess thyroid cancer prognosis. Sci Rep 2020;10:5176.
34. Smith BJ, Mezhir JJ. An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients. J Am Med Inform Assoc 2014;21:e203-11.
35. Bolourani S, Tayebi MA, Diao L, et al. Using machine learning to predict early readmission following esophagectomy. J Thorac Cardiovasc Surg 2021;161:1926-1939.e8.
36. Schoenberg MB, Bucher JN, Koch D, et al. A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma. Ann Transl Med 2020;8:434.
37. Nilsaz-Dezfouli H, Abu-Bakar MR, Arasan J, Adam MB, Pourhoseingholi MA. Improving gastric cancer outcome prediction using single time-point artificial neural network models. Cancer Inform 2017;16:1176935116686062.
38. Xu Y, Ju L, Tong J, Zhou CM, Yang JJ. Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection. Sci Rep 2020;10:2519.
39. Rahman SA, Walker RC, Lloyd MA, et al. OCCAMS Consortium. Machine learning to predict early recurrence after oesophageal cancer surgery. Br J Surg 2020;107:1042-52.
40. Kudo SE, Ichimasa K, Villard B, et al. Artificial intelligence system to determine risk of T1 colorectal cancer metastasis to lymph node. Gastroenterology 2021;160:1075-1084.e2.
41. Li J, Gu J, Lu Y, Wang X, Si S, Xue F. Development and validation of a Super learner-based model for predicting survival in Chinese Han patients with resected colorectal cancer. Jpn J Clin Oncol 2020;50:1133-40.
42. van Soest J, Meldolesi E, van Stiphout R, et al. Prospective validation of pathologic complete response models in rectal cancer: transferability and reproducibility. Med Phys 2017;44:4961-7.
43. Adams K, Papagrigoriadis S. Creation of an effective colorectal anastomotic leak early detection tool using an artificial neural network. Int J Colorectal Dis 2014;29:437-43.
44. Ichimasa K, Kudo SE, Mori Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy 2018;50:230-40.
45. Liu C, Qi L, Feng QX, Sun SW, Zhang YD, Liu XS. Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection. Abdom Radiol (NY) 2019;44:3019-29.
46. Kang JS, Lee C, Song W, et al. Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning. Sci Rep 2020;10:20140.
47. Han IW, Cho K, Ryu Y, et al. Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence. World J Gastroenterol 2020;26:4453-64.
48. Zhou C, Wang Y, Ji MH, Tong J, Yang JJ, Xia H. Predicting peritoneal metastasis of gastric cancer patients based on machine learning. Cancer Control 2020;27:1073274820968900.
49. Lei L, Wang Y, Xue Q, Tong J, Zhou CM, Yang JJ. A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection. PeerJ 2020;8:e8583.
50. Jajroudi M, Baniasadi T, Kamkar L, Arbabi F, Sanei M, Ahmadzade M. Prediction of survival in thyroid cancer using data mining technique. Technol Cancer Res Treat 2014;13:353-9.
51. Merath K, Hyer JM, Mehta R, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg 2020;24:1843-51.
52. Tanaka T, Kurosaki M, Lilly LB, Izumi N, Sherman M. Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis. J Surg Oncol 2015;112:72-9.
53. Hermon R, Williams PAH. Big data in healthcare: What is it used for? Proceedings of the 3rd Australian EHealth Informatics and Security Conference; 2014.
54. Song Y, Gao S, Tan W, Qiu Z, Zhou H, Zhao Y. Multiple machine learnings revealed similar predictive accuracy for prognosis of pnets from the surveillance, epidemiology, and end result database. J Cancer 2018;9:3971-8.
55. Vasey B, Nagendran M, Campbell B, et al. DECIDE-AI expert group. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 2022;28:924-33.
56. Sala Elarre P, Oyaga-Iriarte E, Yu KH, et al. Use of machine-learning algorithms in intensified preoperative therapy of pancreatic cancer to predict individual risk of relapse. Cancers (Basel) 2019;11:606.
57. van Stiphout RG, Lammering G, Buijsen J, et al. Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging. Radiother Oncol 2011;98:126-33.
60. Tian Y, Liu X, Wang Z, et al. Concordance between watson for oncology and a multidisciplinary clinical decision-making team for gastric cancer and the prognostic implications: retrospective study. J Med Internet Res 2020;22:e14122.
61. Somashekhar SP, Sepúlveda MJ, Puglielli S, et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol 2018;29:418-23.
62. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020;368:m689.
63. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005;330:765.
64. Wu Y, Rao K, Liu J, et al. Machine learning algorithms for the prediction of central lymph node metastasis in patients with papillary thyroid cancer. Front Endocrinol (Lausanne) 2020;11:577537.
65. Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010;17:25-33.
66. Bouaud J, Blaszka-Jaulerry B, Zelek L, et al. Health information technology: use it well, or don’t! AMIA Annu Symp Proc 2014;2014:315-24.
