Artificial intelligence and cholangiocarcinoma: Updates and prospects

Table 3 Studies utilizing artificial intelligence in the treatment and prognostication of cholangiocarcinoma

Ref.	Year of publication	Title of study	AI variables	AI model
Jeong et al[39]	2020	Latent Risk Intrahepatic Cholangiocarcinoma Susceptible to Adjuvant Treatment After Resection: A Clinical Deep Learning Approach	CT, albumin, platelets, Diabetes, CA 19-9	ML
Ji et al[55]	2019	Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes	CT reported LN features	ANN
Li et al[41]	2020	A Novel Prognostic Scoring System of Intrahepatic Cholangiocarcinoma With Machine Learning Basing on Real-World Data	CEA, CA 19-9, tumor stage	ML
Muller et al[42]	2021	Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof-of-Concept Study Using Artificial Intelligence for Risk Assessment	Tumor size, tumor boundary, serology	ANN
Shao et al[43]	2018	Artificial Neural Networking Model for the Prediction of Early Occlusion of Bilateral Plastic Stent Placement for Inoperable Hilar Cholangiocarcinoma	Tumor size, nodal involvement	ANN
Tang et al[40]	2021	The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma	Tumor size, cirrhosis in CT	Radiomics
Tsilimigras et al[37]	2020	A Novel Classification of Intrahepatic Cholangiocarcinoma Phenotypes Using Machine Learning Techniques: An International Multi-Institutional Analysis	Tumor size, nodal involvement, serology	ML

AI: Artificial intelligence; ANN: Artificial Neural Network; CA 19-9: Carbohydrate antigen 19-9; CCA: Cholangiocarcinoma; CEA: Carcinoembryonic antigen; CT: Computed tomography; ML: Machine learning.