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Endo et al. Art Int Surg 2024;4:59-67  https://dx.doi.org/10.20517/ais.2024.09                                                                Page 67

                   slides. Hepatology 2020;72:2000-13.  DOI  PubMed
               44.      Endo Y, Alaimo L, Lima HA, et al. A novel online calculator to predict risk of microvascular invasion in the preoperative setting for
                   hepatocellular carcinoma patients undergoing curative-intent surgery. Ann Surg Oncol 2023;30:725-33.  DOI  PubMed
               45.      Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: a state-of-the-art review. World J Gastrointest Oncol
                   2021;13:1599-615.  DOI  PubMed  PMC
               46.      Lewis S, Hectors S, Taouli B. Radiomics of hepatocellular carcinoma. Abdom Radiol 2021;46:111-23.  DOI  PubMed
               47.      Ma X, Wei J, Gu D, et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using
                   contrast-enhanced CT. Eur Radiol 2019;29:3595-605.  DOI  PubMed
               48.      Zhu YJ, Feng B, Wang S, et al. Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a
                   potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Oncol Lett 2019;18:720-32.  DOI
                   PubMed  PMC
               49.      Jiang YQ, Cao SE, Cao S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and
                   deep learning. J Cancer Res Clin Oncol 2021;147:821-33.  DOI  PubMed  PMC
               50.      American Cancer Society. Key statistics for bile duct cancer. Available from: https://www.cancer.org/cancer/types//bile-duct-cancer/
                   about/key-statistics.html. [Last accessed on 24 May 2024].
               51.      Alaimo L, Lima HA, Moazzam Z, et al. Development and validation of a machine-learning model to predict early recurrence of
                   intrahepatic cholangiocarcinoma. Ann Surg Oncol 2023;30:5406-15.  DOI  PubMed
               52.      Sasaki K, Morioka D, Conci S, et al. The tumor burden score: a new “metro-ticket” prognostic tool for colorectal liver metastases
                   based on tumor size and number of tumors. Ann Surg 2018;267:132-41.  DOI  PubMed
               53.      Cotter G, Beal EW, Poultsides GA, et al. Using machine learning to preoperatively stratify prognosis among patients with gallbladder
                   cancer: a multi-institutional analysis. HPB 2022;24:1980-8.  DOI  PubMed
               54.      Tsilimigras DI, Hyer JM, Paredes AZ, et al. A novel classification of intrahepatic cholangiocarcinoma phenotypes using machine
                   learning techniques: an international multi-institutional analysis. Ann Surg Oncol 2020;27:5224-32.  DOI  PubMed
               55.      Chen B, Mao Y, Li J, et al. Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using
                   machine learning radiomics based on CECT: a multi-institutional study. Comput Biol Med 2023;167:107612.  DOI  PubMed
               56.      Endo  Y,  Moazzam  Z,  Lima  HA,  et  al.  The  impact  of  tumor  location  on  the  value  of  lymphadenectomy  for  intrahepatic
                   cholangiocarcinoma. HPB 2023;25:650-8.  DOI  PubMed
               57.      Mueller M, Breuer E, Mizuno T, et al. Perihilar cholangiocarcinoma - novel benchmark values for surgical and oncological outcomes
                   from 24 expert centers. Ann Surg 2021;274:780-8.  DOI  PubMed
               58.      van Keulen AM, Buettner S, Erdmann JI, et al; perihilar cholangiocarcinoma collaboration group. Multivariable prediction model for
                   both 90-day mortality and long-term survival for individual patients with perihilar cholangiocarcinoma: does the predicted survival
                   justify the surgical risk? Br J Surg 2023;110:599-605.  DOI  PubMed  PMC
               59.      Ratti F, Marino R, Olthof PB, et al; Perihilar Cholangiocarcinoma Collaboration Group. Predicting futility of upfront surgery in
                   perihilar cholangiocarcinoma: Machine learning analytics model to optimize treatment allocation. Hepatology 2024;79:341-54.  DOI
                   PubMed
               60.      Alaimo L, Moazzam Z, Endo Y, et al. The application of artificial intelligence to investigate long-term outcomes and assess optimal
                   margin width in hepatectomy for intrahepatic cholangiocarcinoma. Ann Surg Oncol 2023;30:4292-301.  DOI  PubMed
               61.      Laplante S, Namazi B, Kiani P, et al. Validation of an artificial intelligence platform for the guidance of safe laparoscopic
                   cholecystectomy. Surg Endosc 2023;37:2260-8.  DOI  PubMed
               62.      Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify
                   surgical anatomy during laparoscopic cholecystectomy. Ann Surg 2022;276:363-9.  DOI  PubMed  PMC
               63.      Alaimo L, Endo Y, Lima HA, et al. A comprehensive preoperative predictive score for post-hepatectomy liver failure after
                   hepatocellular carcinoma resection based on patient comorbidities, tumor burden, and liver function: the CTF score. J Gastrointest
                   Surg 2022;26:2486-95.  DOI  PubMed
               64.      Winkel DJ, Weikert TJ, Breit HC, et al. Validation of a fully automated liver segmentation algorithm using multi-scale deep
                   reinforcement learning and comparison versus manual segmentation. Eur J Radiol 2020;126:108918.  DOI  PubMed
               65.      Ruzzenente A, Alaimo L, D’Onofrio M, et al. Perihilar cholangiocarcinoma: three-dimensional modelling algorithm to estimate
                   tumour extension and bile duct resection margins. Br J Surg 2024;111:znad428.  DOI  PubMed
               66.      Tomiyama K, Ghazi A, Hernandez-Alejandro R. Looking beyond the horizon: patient-specific rehearsals for complex liver surgeries
                   with 3D printed model. Ann Surg 2021;273:e28-30.  DOI  PubMed
               67.      Ntourakis D, Memeo R, Soler L, Marescaux J, Mutter D, Pessaux P. Augmented reality guidance for the resection of missing
                   colorectal liver metastases: an initial experience. World J Surg 2016;40:419-26.  DOI  PubMed
               68.      Pessaux P, Diana M, Soler L, Piardi T, Mutter D, Marescaux J. Towards cybernetic surgery: robotic and augmented reality-assisted
                   liver segmentectomy. Langenbecks Arch Surg 2015;400:381-5.  DOI  PubMed
               69.      Ossa L, Lorenzini G, Milford SR, Shaw D, Elger BS, Rost M. Integrating ethics in AI development: a qualitative study. BMC Med
                   Ethics 2024;25:10.  DOI  PubMed  PMC
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