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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

(101) (110) (0) (0) (0) (7) (1) (2) (3521) (13)

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 era. World Journal of Gastroenterology 2025; 31(21): 106808 doi: 10.3748/wjg.v31.i21.106808 Abstract(393) \| Core Tip(385) \| Full Article(HTML)(1530) \| Full Article (PDF)-1743K(148) \| Full Article with Cover (PDF)-3261K(149) \| Full Article (XML)-81K(79) \| Audio-355K(17) \| Peer-Review Report-461K(70) \| Answering Reviewers-379K(78) \| Times Cited (1) \| Total Visits (2539) \| Open
2	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 challenges. World Journal of Gastrointestinal Oncology 2025; 17(10): 111367 doi: 10.4251/wjgo.v17.i10.111367 Abstract(95) \| Core Tip(85) \| Full Article(HTML)(397) \| Full Article (PDF)-1950K(61) \| Full Article with Cover (PDF)-2134K(76) \| Full Article (XML)-291K(44) \| Audio-1564K(16) \| Peer-Review Report-206K(37) \| Answering Reviewers-69K(38) \| Times Cited (1) \| Total Visits (706) \| Open
3	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 Study. Cancers 2026; 18(9) doi: 10.3390/cancers18091365
4	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 model. Discover Oncology 2026; 17(1) doi: 10.1007/s12672-026-04521-3
5	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 degeneration. Frontiers in Neurology 2026; 17 doi: 10.3389/fneur.2026.1808282
6	Shu-Yen Chan, Patrick Twohig. Artificial intelligence in liver cancer surgery: Predicting success before the first incision. World Journal of Gastroenterology 2025; 31(16): 107221 doi: 10.3748/wjg.v31.i16.107221 Abstract(405) \| Core Tip(361) \| Full Article(HTML)(1621) \| Full Article (PDF)-351K(136) \| Full Article with Cover (PDF)-630K(108) \| Full Article (XML)-42K(88) \| Audio-463K(12) \| Peer-Review Report-183K(84) \| Answering Reviewers-216K(79) \| Times Cited (2) \| Total Visits (1953) \| Open
7	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 Carcinoma. Journal of Inflammation Research 2026; doi: 10.2147/JIR.S599865
8	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-analysis. Clinics and Research in Hepatology and Gastroenterology 2026; 50(6) doi: 10.1016/j.clinre.2026.102844
9	Eyad Gadour, Mohammed S AlQahtani. Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma. World Journal of Gastroenterology 2025; 31(17): 106592 doi: 10.3748/wjg.v31.i17.106592 Abstract(381) \| Core Tip(377) \| Full Article(HTML)(1279) \| Full Article (PDF)-369K(124) \| Full Article with Cover (PDF)-1834K(122) \| Full Article (XML)-50K(98) \| Audio-1935K(24) \| Peer-Review Report-179K(86) \| Answering Reviewers-61K(91) \| Times Cited (1) \| Total Visits (1623) \| Open
10	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-analysis. International Journal of Surgery 2026; 112(3) doi: 10.1097/JS9.0000000000004149
11	Himanshu Agrawal, Nikhil Gupta, Himanshu Tanwar, Natasha Panesar. Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications. Artificial Intelligence in Gastroenterology 2025; 6(1): 108198 doi: 10.35712/aig.v6.i1.108198 Abstract(399) \| Core Tip(388) \| Full Article(HTML)(1079) \| Full Article (PDF)-713K(157) \| Full Article with Cover (PDF)-923K(197) \| Full Article (XML)-390K(81) \| Audio-2093K(24) \| Peer-Review Report-201K(100) \| Answering Reviewers-248K(83) \| Times Cited (1) \| Total Visits (2162) \| Open
12	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

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