BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Tsilimigras DI, Mehta R, Moris D, Sahara K, Bagante F, Paredes AZ, Moro A, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Soubrane O, Koerkamp BG, Endo I, Pawlik TM. A Machine-Based Approach to Preoperatively Identify Patients with the Most and Least Benefit Associated with Resection for Intrahepatic Cholangiocarcinoma: An International Multi-institutional Analysis of 1146 Patients. Ann Surg Oncol. 2020;27:1110-1119. [PMID: 31728792 DOI: 10.1245/s10434-019-08067-3] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Tsilimigras DI, Mehta R, Pawlik TM. ASO Author Reflections: Use of Machine Learning to Identify Patients with Intrahepatic Cholangiocarcinoma Who Could Benefit More from Neoadjuvant Therapies. Ann Surg Oncol 2020;27:1120-1. [DOI: 10.1245/s10434-019-08068-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
2 Shirono T, Niizeki T, Iwamoto H, Shimose S, Suzuki H, Kawaguchi T, Kamachi N, Noda Y, Okamura S, Nakano M, Kuromatu R, Koga H, Torimura T. Therapeutic Outcomes and Prognostic Factors of Unresectable Intrahepatic Cholangiocarcinoma: A Data Mining Analysis. J Clin Med 2021;10:987. [PMID: 33801202 DOI: 10.3390/jcm10050987] [Reference Citation Analysis]
3 Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27(37): 6191-6223 [PMID: 34712027 DOI: 10.3748/wjg.v27.i37.6191] [Reference Citation Analysis]
4 Ahsan R, Ebrahimi F, Ebrahimi M. Classification of imbalanced protein sequences with deep-learning approaches; application on influenza A imbalanced virus classes. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100860] [Reference Citation Analysis]
5 Ntanasis-Stathopoulos I, Tsilimigras DI, Gavriatopoulou M, Schizas D, Pawlik TM. Cholangiocarcinoma: investigations into pathway-targeted therapies. Expert Rev Anticancer Ther 2020;20:765-73. [PMID: 32757962 DOI: 10.1080/14737140.2020.1807333] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
6 Chen Y, Weng S. Reappraisal of the T Category for Solitary Intrahepatic Cholangiocarcinoma by Tumor Size in 611 Early-Stage (T1-2N0M0) Patients After Hepatectomy: a Surveillance, Epidemiology, and End Results (SEER) Analysis. J Gastrointest Surg 2021;25:1989-99. [PMID: 33140321 DOI: 10.1007/s11605-020-04833-x] [Reference Citation Analysis]
7 Lai Q, Spoletini G, Mennini G, Larghi Laureiro Z, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26(42): 6679-6688 [PMID: 33268955 DOI: 10.3748/wjg.v26.i42.6679] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
8 Tsilimigras DI, Paredes AZ, Pawlik TM. ASO Author Reflections: Identification of Intrahepatic Cholangiocarcinoma Clusters Using Machine Learning Techniques: Should Patients be Treated Differently? Ann Surg Oncol 2020;27:5233-4. [PMID: 32591955 DOI: 10.1245/s10434-020-08697-y] [Reference Citation Analysis]
9 Tan L, Tivey D, Kopunic H, Babidge W, Langley S, Maddern G. Part 1: Artificial intelligence technology in surgery. ANZ J Surg 2020;90:2409-14. [PMID: 33000556 DOI: 10.1111/ans.16343] [Reference Citation Analysis]
10 Akateh C, Ejaz AM, Pawlik TM, Cloyd JM. Neoadjuvant treatment strategies for intrahepatic cholangiocarcinoma. World J Hepatol 2020; 12(10): 693-708 [PMID: 33200010 DOI: 10.4254/wjh.v12.i10.693] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
11 Bertsimas D, Wiberg H. Machine Learning in Oncology: Methods, Applications, and Challenges. JCO Clin Cancer Inform 2020;4:885-94. [PMID: 33058693 DOI: 10.1200/CCI.20.00072] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Beetz O, Weigle CA, Cammann S, Vondran FWR, Timrott K, Kulik U, Bektas H, Klempnauer J, Kleine M, Oldhafer F. Preoperative leukocytosis and the resection severity index are independent risk factors for survival in patients with intrahepatic cholangiocarcinoma. Langenbecks Arch Surg 2020;405:977-88. [PMID: 32815017 DOI: 10.1007/s00423-020-01962-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
13 Tsilimigras DI, Moris D, Mehta R, Paredes AZ, Sahara K, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Soubrane O, Koerkamp BG, Endo I, Pawlik TM. The systemic immune-inflammation index predicts prognosis in intrahepatic cholangiocarcinoma: an international multi-institutional analysis. HPB (Oxford). 2020;22:1667-1674. [PMID: 32265108 DOI: 10.1016/j.hpb.2020.03.011] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
14 Henn J, Buness A, Schmid M, Kalff JC, Matthaei H. Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review. Langenbecks Arch Surg 2021. [PMID: 34716472 DOI: 10.1007/s00423-021-02348-w] [Reference Citation Analysis]
15 Tsilimigras DI, Sahara K, Wu L, Moris D, Bagante F, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Soubrane O, Koerkamp BG, Moro A, Sasaki K, Aucejo F, Zhang XF, Matsuyama R, Endo I, Pawlik TM. Very Early Recurrence After Liver Resection for Intrahepatic Cholangiocarcinoma: Considering Alternative Treatment Approaches. JAMA Surg 2020;155:823-31. [PMID: 32639548 DOI: 10.1001/jamasurg.2020.1973] [Cited by in Crossref: 22] [Cited by in F6Publishing: 21] [Article Influence: 22.0] [Reference Citation Analysis]
16 Li Q, Chen C, Zhang J, Wu H, Qiu Y, Song T, Mao X, He Y, Cheng Z, Zhai W, Li J, Zhang D, Geng Z, Tang Z. Prediction Efficacy of Prognostic Nutritional Index and Albumin-Bilirubin Grade in Patients With Intrahepatic Cholangiocarcinoma After Radical Resection: A Multi-Institutional Analysis of 535 Patients. Front Oncol 2021;11:769696. [PMID: 34956888 DOI: 10.3389/fonc.2021.769696] [Reference Citation Analysis]
17 Wang S, Liu X, Zhao J, Liu Y, Liu S, Liu Y, Zhao J. Computer auxiliary diagnosis technique of detecting cholangiocarcinoma based on medical imaging: A review. Comput Methods Programs Biomed 2021;208:106265. [PMID: 34311415 DOI: 10.1016/j.cmpb.2021.106265] [Reference Citation Analysis]
18 Acher AW, Paro A, Elfadaly A, Tsilimigras D, Pawlik TM. Intrahepatic Cholangiocarcinoma: A Summative Review of Biomarkers and Targeted Therapies. Cancers (Basel) 2021;13:5169. [PMID: 34680318 DOI: 10.3390/cancers13205169] [Reference Citation Analysis]
19 Ivanics T, Patel MS, Erdman L, Sapisochin G. Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology). Curr Opin Organ Transplant 2020;25:426-34. [PMID: 32487887 DOI: 10.1097/MOT.0000000000000773] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Ji G, Jiao C, Xu Z, Li X, Wang K, Wang X. Development and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma. BMC Cancer 2022;22. [DOI: 10.1186/s12885-022-09352-3] [Reference Citation Analysis]
21 Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. Medicina (Kaunas) 2022;58:459. [PMID: 35454298 DOI: 10.3390/medicina58040459] [Reference Citation Analysis]
22 Tsilimigras DI, Pawlik TM. ASO Author Reflections: Resection for Hepatocellular Carcinoma Beyond the BCLC Guidelines-How Can Machine Learning Techniques Help? Ann Surg Oncol 2020;27:875-6. [PMID: 31686343 DOI: 10.1245/s10434-019-08036-w] [Cited by in F6Publishing: 1] [Reference Citation Analysis]