WANG Pei-pei, WU Bin*
|  Siegel RL, Miller KD, Jemal A.Cancer statistics, 2020[J].2020,70: 7-30.
 Chen W, Sun K, Zheng R, et al. Cancer incidence and mortality in China, 2014[J].Chin J Cancer Res,2018, 30: 1-12.
 Peek N, Combi C, Marin R, et al. Thirty years of artificial intelligence in medicine (AIME) conferences: a review of research themes[J]. Arti Intell Med,2015,65: 61-73.
 de Grey AD. artificial intelligence and medical research: time to aim higher?[J].Reju Res, 2016,19: 105-106.
 Trestini I, Carbognin L, Monteverdi S, et al. Clinical implication of changes in body composition and weight in patients with early-stage and metastatic breast cancer[J].Crit Rev Onco/hema,2018,129: 54-66.
 Al-Haidari G, Skovlund E, Undseth C, et al. Re-irradiation for recurrent rectal cancer- a single-center experience[J].Acta Onco (Stockholm, Sweden),2020: 1-7.
 Balyasnikova S, Brown G. Optimal imaging strategies for rectal cancer staging and ongoing management[J]. Cur Treat Opti Onco,2016,17: 32-43.
 Castaneda C, Nalley K, Mannion C, et al. Cclinical decision support systems for improving diagnostic accuracy and achieving precision medicine[J]. J Clin Bio, 2015,5: 4-20.
 Tanaka S, Sano Y. Sim to unify the narrow band imaging magnifying classification for colorectal tumors: current status in Japan from a summary of the consensus symposium in the 79th annual meeting of the japan gastroenterological endoscopy society[J]. Dige Endo, 2011,23 Suppl 1: 131-139.
 Sano Y, Tanaka S, Kudo SE, et al. Narrow-band imaging magnifying endoscopic classification of colorectal tumors proposed by the Japan nbi expert team[J]. Dige Endo,2016: 28: 526-533.
 Chen PJ, Lin MC, Lai MJ, et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis[J]. Gastroenterology,2018,154: 568-575.
 Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience[J]. Gastroenterology,2018,154: 2027-2029.
 Bang S, Yoo D, Kim SJ, et al. Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data[J]. Sci Rep,2019, 9: 1018-1019.
 Kennedy ED, Simunovic M, Jhaveri K, et al. Safety and feasibility of using magnetic resonance imaging criteria to identify patients with “good prognosis” rectal cancer eligible for primary surgery: the phase 2 nonrandomized quicksilver clinical trial[J]. JAMA Onco, 2019, 5: 961-966.
 Ogawa S, Hida J, Ike H, et al. Selection of lymph node-positive cases based on perirectal and lateral pelvic lymph nodes using magnetic resonance imaging: study of the japanese society for cancer of the colon and rectum[J]. Ann Surg Onco,2016, 23: 1187-1194.
 Ren S, He K, Girshick R, et al.Faster r-cnn: towards real-time object detection with region proposal networks[J]. IEEE Trans Patt Anal and Mach Intell, 2017,39: 1137-1149.
 周云朋, 李硕, 卢云,等.基于深度神经网络的高分辨MRI直肠淋巴结辅助诊断系统的临床应用价值研究[J] 中华外科杂志, 2019,57: 108-113.
 Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology-promises and pitfalls[J]. Am J Gastroenter, 2019, 114: 422-428.
 Varghese AM, Cardin DB, Hersch J, et al. Phase i study of trifluridine/tipiracil plus irinotecan and bevacizumab in advanced gastrointestinal tumors[J]. Clin Cancer Res, 2020,26:1555-1562.
 Oyaga-Iriarte E, Insausti A, Sayar O, et al. Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters[J]. J Pharm Sci,2019,140: 20-25.
 Midura EF, Hanseman D, Davis BR, et al. Risk factors and consequences of anastomotic leak after colectomy: a national analysis[J]. Dis Colon Rectum, 2015; 58: 333-338.
 Sammour T, Hayes IP, Jones IT, et al. Impact of anastomotic leak on recurrence and survival after colorectal cancer surgery: a biogrid australia analysis[J]. ANZ J Surg,2018, 88: E6-E10.
 Sammour T, Cohen L, Karunatillake AI, et al. Validation of an online risk calculator for the prediction of anastomotic leak after colon cancer surgery and preli-minary exploration of artificial intelligence-based analytics[J]. Tech Colo, 2017,21: 869-877.
 Dimitriou N, Arandjelovic O, Caie PD. Deep learning for whole slide image analysis: an overview[J]. Fron Med, 2019, 6: 264-271.
 LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015,521: 436-444.
 Bychkov D, Linder N, Turkki R, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer[J]. Sci Rep,2018, 8: 3395-3406.
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