Deboever N, Jones CM, Yamashita K, Ajani JA, Hofstetter WL (2024) Advances in diagnosis and management of cancer of the esophagus. BMJ 385:e074962. https://doi.org/10.1136/bmj-2023-074962
Article CAS PubMed Google Scholar
Sun S et al (2023) Different gastric tubes in esophageal reconstruction during esophagectomy. Esophagus 20(4):595–604. https://doi.org/10.1007/s10388-023-01021-z
Article PubMed PubMed Central Google Scholar
Chevallay M, Jung M, Chon S-H, Takeda FR, Akiyama J, Mönig S (2020) Esophageal cancer surgery: review of complications and their management. Ann N Y Acad Sci 1482(1):146–162. https://doi.org/10.1111/nyas.14492
Kalata S et al (2023) Epidemiology of postoperative complications after esophagectomy: implications for management. Ann Thorac Surg 116(6):1168–1175. https://doi.org/10.1016/j.athoracsur.2023.09.004
Patel P, Patel M, Ebrahim MA, Loganathan P, Adler DG (2024) Endoscopic management of lower gastrointestinal tract anastomosis strictures: a meta-analysis and systematic review of the literature. Dig Dis Sci 69(10):3882–3893. https://doi.org/10.1007/s10620-024-08627-y
Zhong Y et al (2024) Risk factors for esophageal anastomotic stricture after esophagectomy: a meta-analysis. BMC Cancer 24(1):872. https://doi.org/10.1186/s12885-024-12625-8
Article PubMed PubMed Central Google Scholar
Mendelson AH, Small AJ, Agarwalla A, Scott FI, Kochman ML (2015) Esophageal anastomotic strictures: outcomes of endoscopic dilation, risk of recurrence and refractory stenosis, and effect of foreign body removal. Clin Gastroenterol Hepatol 13(2):263–271.e1. https://doi.org/10.1016/j.cgh.2014.07.010
Wang X, Liu X, Gu Z, Li X, Shu Y (2023) Experiences and requirements in nutritional management of patients with esophageal cancer: a systematic review and qualitative meta-synthesis. Support Care Cancer 31(12):633. https://doi.org/10.1007/s00520-023-08100-y
Na B, Kang CH, Na KJ, Park S, Park IK, Kim YT (2023) Risk factors of anastomosis stricture after esophagectomy and the impact of anastomosis technique. Ann Thorac Surg 115(5):1257–1264. https://doi.org/10.1016/j.athoracsur.2023.01.026
Hagi T et al (2019) Dysphagia score as a predictor of adverse events due to triplet chemotherapy and oncological outcomes in 434 consecutive patients with esophageal cancer. Ann Surg Oncol 26(13):4754–4764. https://doi.org/10.1245/s10434-019-07744-7
Mellow MH, Pinkas H (1985) Endoscopic laser therapy for malignancies affecting the esophagus and gastroesophageal junction. Analysis of technical and functional efficacy. Arch Intern Med 145(8):1443–1446
Article CAS PubMed Google Scholar
Alhudhaif A (2021) A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach. PeerJ Comput Sci 7:e523. https://doi.org/10.7717/peerj-cs.523
Article PubMed PubMed Central Google Scholar
Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
Low DE et al (2019) Benchmarking complications associated with esophagectomy. Ann Surg 269(2):291–298. https://doi.org/10.1097/SLA.0000000000002611
Herron R, Abbas G (2021) Techniques of esophageal anastomoses for esophagectomy. Surg Clin North Am 101(3):511–524. https://doi.org/10.1016/j.suc.2021.03.012
Carr RA, Molena D (2021) Minimally invasive esophagectomy: anastomotic techniques. Ann Esophagus 4:19. https://doi.org/10.21037/aoe-20-40
Ge F et al (2022) Evaluation of clinical and safety outcomes of neoadjuvant immunotherapy combined with chemotherapy for patients with resectable esophageal cancer: a systematic review and meta-analysis. JAMA Netw Open 5(11):e2239778. https://doi.org/10.1001/jamanetworkopen.2022.39778
Article PubMed PubMed Central Google Scholar
Hong Z et al (2023) Additional neoadjuvant immunotherapy does not increase the risk of anastomotic leakage after esophagectomy for esophageal squamous cell carcinoma: a multicenter retrospective cohort study. Int J Surg Lond Engl 109(8):2168–2178. https://doi.org/10.1097/JS9.0000000000000487
Sugimoto A et al (2021) Preoperative C-reactive protein to albumin ratio predicts anastomotic leakage after esophagectomy for thoracic esophageal cancer: a single-center retrospective cohort study. BMC Surg 21(1):348. https://doi.org/10.1186/s12893-021-01344-7
Article CAS PubMed PubMed Central Google Scholar
Zhang C et al (2021) Predictive value of postoperative C-reactive protein-to-albumin ratio in anastomotic leakage after esophagectomy. J Cardiothorac Surg 16(1):133. https://doi.org/10.1186/s13019-021-01515-w
Article PubMed PubMed Central Google Scholar
Jiang H et al (2021) Risk factors for anastomotic complications after radical mckeown esophagectomy. Ann Thorac Surg 112(3):944–951. https://doi.org/10.1016/j.athoracsur.2020.09.019
Wang X et al (2019) Predictive value of anastomotic blood supply for anastomotic stricture after esophagectomy in esophageal cancer. Dig Dis Sci 64(11):3307–3313. https://doi.org/10.1007/s10620-018-5451-3
Article CAS PubMed Google Scholar
Kong C, Zhu Y, Xie X, Wu J, Qian M (2023) Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study. Front Immunol 14:1184700. https://doi.org/10.3389/fimmu.2023.1184700
Article CAS PubMed PubMed Central Google Scholar
Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N (2018) A survey on addressing high-class imbalance in big data. J Big Data 5(1):42. https://doi.org/10.1186/s40537-018-0151-6
TalebiMoghaddam M et al (2024) Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm. BMC Med Res Methodol 24(1):220. https://doi.org/10.1186/s12874-024-02341-z
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451
Rodríguez-Tomàs E et al (2022) Gradient boosting machine identified predictive variables for breast cancer patients pre- and post-radiotherapy: preliminary results of an 8-year follow-up study. Antioxid Basel Switz. https://doi.org/10.3390/antiox11122394
Boldini D, Grisoni F, Kuhn D, Friedrich L, Sieber SA (2023) Practical guidelines for the use of gradient boosting for molecular property prediction. J Cheminform 15(1):73. https://doi.org/10.1186/s13321-023-00743-7
Article PubMed PubMed Central Google Scholar
Jiang X, Wang J, Meng Q, Saada M, Cai H (2023) An adaptive multi-class imbalanced classification framework based on ensemble methods and deep network. Neural Comput Appl 35(15):11141–11159. https://doi.org/10.1007/s00521-023-08290-w
Wang K et al (2021) Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP. Comput Biol Med 137:104813. https://doi.org/10.1016/j.compbiomed.2021.104813
Jia L et al (2022) Development of interactive biological web applications with R/Shiny. Brief Bioinform 23(1):bbab415. https://doi.org/10.1093/bib/bbab415
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