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Cited by in CrossRef
For: Huang TF, Luo C, Guo LB, Liu HZ, Li JT, Lin QZ, Fan RL, Zhou WP, Li JD, Lin KC, Tang SC, Zeng YY. Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study. World J Gastroenterol 2025; 31(11): 100911 [PMID: 40124276 DOI: 10.3748/wjg.v31.i11.100911]
URL: https://www.wjgnet.com/1007-9327/full/v31/i11/100911.htm
Number Citing Articles
1
Arnulfo E Morales-Galicia, Mariana N Rincón-Sánchez, Mariana M Ramírez-Mejía, Nahum Méndez-Sánchez. Outcome prediction for cholangiocarcinoma prognosis: Embracing the machine learning eraWorld Journal of Gastroenterology 2025; 31(21): 106808 doi: 10.3748/wjg.v31.i21.106808
2
Lin Ding, Yanxin Hu, Wei Wang. Decoding serum C-reactive protein-associated molecular patterns in aging and secondhand smoke exposure chronic obstructive pulmonary disease male patients: evidence from cross-sectional, multi-omic and clinical studiesFrontiers in Medicine 2026; 13 doi: 10.3389/fmed.2026.1922619
3
Xusheng Li, Ahmad Nazrun Shuid, Mohd Fairudz Mohd Miswan, Xiao Zhang, Wenbo Gu, Donghui Cao, Jungang Wang, Ziyang Jiang, Haifeng Yuan. Integrating multi-omics and machine learning to explore the role of amino acid metabolism in intervertebral disk degenerationFrontiers in Neurology 2026; 17 doi: 10.3389/fneur.2026.1808282
4
Yizhao Liu, Xia Lei, Jinyong Hao, Jihua Yang, Hanzhou Bao, Qiao Wang, Xiaojun Huang. Prognostic models for intrahepatic cholangiocarcinoma after resection: A systematic review and meta-analysisClinics and Research in Hepatology and Gastroenterology 2026; 50(6) doi: 10.1016/j.clinre.2026.102844
5
Eyad Gadour, Mohammed S AlQahtani. Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinomaWorld Journal of Gastroenterology 2025; 31(17): 106592 doi: 10.3748/wjg.v31.i17.106592
6
Himanshu Agrawal, Nikhil Gupta, Himanshu Tanwar, Natasha Panesar. Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applicationsArtificial Intelligence in Gastroenterology 2025; 6(1): 108198 doi: 10.35712/aig.v6.i1.108198
7
Mikhail Efanov, Natalia Britskaia, Denis Fisenko, Pavel Tarakanov, Yuliya Kulezneva, Olga Melekhina, Anna Koroleva, Andrey Vankovich, Dmitry Kovalenko, Nikita Solovyev, Victor Tsvirkun, Igor Khatkov. Does calculating the textbook outcome based on its negative predictors enhance the transparency of intrahepatic cholangiocarcinoma surgery assessment?Annals of Hepato-Biliary-Pancreatic Surgery 2026; 30(2) doi: 10.14701/ahbps.25-234
8
Liang Qiao, Yu-Gang Luo, Qing-Ying Wang, Tian Yuan, Meng Xu, Guang-Bing Xiong, Feng Zhu. Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challengesWorld Journal of Gastrointestinal Oncology 2025; 17(10): 111367 doi: 10.4251/wjgo.v17.i10.111367
9
Xinming Li, Yaoqun Wang, Jiaqi Yang, Jiong Lu. Development of a preoperative prediction model for non-curative resection in intrahepatic cholangiocarcinoma involving the hepatic hilumEuropean Journal of Surgical Oncology 2026; 52(10) doi: 10.1016/j.ejso.2026.112061
10
Ali Ramouz, Ali Adeliansedehi, Behboud Moeini Chagervand, Nastaran Sabetkish, Benjamin Goeppert, Christoph Springfeld, Elias Khajeh, Arianeb Mehrabi, Ali Majlesara. Multiphasic Evidential Decision-Making Matrix (MedMax) for Intrahepatic Cholangiocarcinoma: A Single-Center Validation StudyCancers 2026; 18(9) doi: 10.3390/cancers18091365
11
Shan Li, Guoxia Jia, Fan Luo, Jiaxin Yin, Shaochong Deng, Jianfu Zhao, Huizhong Wang. Log odds of positive lymph nodes predict surgical prognosis in intrahepatic cholangiocarcinoma based on SEER cohort and nomogram modelDiscover Oncology 2026; 17(1) doi: 10.1007/s12672-026-04521-3
12
Wenhui Wang, Meizhen Sun. Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validationFrontiers in Neuroscience 2026; 20 doi: 10.3389/fnins.2026.1913179
13
Shu-Yen Chan, Patrick Twohig. Artificial intelligence in liver cancer surgery: Predicting success before the first incisionWorld Journal of Gastroenterology 2025; 31(16): 107221 doi: 10.3748/wjg.v31.i16.107221
14
Xiangli Meng, Jin Wang, Wei Shen, Shanshan Yang, Kelei Zhao, Xiaomei Liu, Kun Cheng, Lingling Tian, Hao Cheng, Xuelian Xu. A CRP-Albumin-Lymphocyte (CALLY) Index–Based Nomogram for Predicting Survival After Radical Surgery for Hypopharyngeal Squamous Cell CarcinomaJournal of Inflammation Research 2026;  doi: 10.2147/JIR.S599865
15
Si-qi Yang, Rui-qi Zou, Yu-shi Dai, Fei Liu, Hai-jie Hu, Fu-yu Li. Predictors and prognostic impact of textbook outcome in patients with biliary tract cancer after hepatectomy: a systematic review and meta-analysisInternational Journal of Surgery 2026; 112(3) doi: 10.1097/JS9.0000000000004149