Copyright ©The Author(s) 2022.
World J Clin Oncol. Feb 24, 2022; 13(2): 125-134
Published online Feb 24, 2022. doi: 10.5306/wjco.v13.i2.125
Table 1 Advantages and disadvantages of artificial intelligence models used for cholangiocarcinoma diagnosis in radiology
AI technology
Imaging modalities used in
Logistic regressionUS/CTInterpretable Low precision
Support-vector machineUS/CT/MRIAvoids overlearning and dimension disaster problemsProne to missing data
Extreme learning machineCTDoes not need high amount of data for trainingSlow processing speed
Artificial neural networkCT/MRIHigh generalization powerNeeds long training time
Convolutional neural networkUS/CT/MRIHigher efficacy and speed as there is no need to compute features as first stepNeeds large training data
Table 2 Studies utilizing artificial intelligence in the diagnosis of cholangiocarcinoma
Year of publication
Title of study
Diagnostic modality
AI model
Chu et al[44]2021Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinomaCTLR
Ibragimov et al[45]2020Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapyCTCNN
Liu et al[46]2021Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?MRI, CTSVM
Logeswaran[35]2009Cholangiocarcinoma--an automated preliminary detection system using MLPMRCPANN
Midya et al[47]2018Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinomaCTCNN
Nakai et al[29]2021Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot studyCT, tumor markersCNN
Negrini et al[22]2020Machine Learning Model Comparison in the Screening of Cholangiocarcinoma Using Plasma Bile Acids ProfilesSerum bile acidsML
Pattanapairoj et al[23]2015Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approachTumor markersANN
Peng et al[48]2020Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic CholangiocarcinomaUSSVM
Peng et al[49]2020Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver CancerUSRadiomics
Ponnoprat et al[31]2020Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scansCTCNN
Selvathi et al[50]2013Automatic segmentation and classification of liver tumor in CT images using adaptive hybrid technique and Contourlet based ELM classifierCTELM
Sun et al[25]2021Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networksHistologyCNN
Urman et al[24]2020Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning ApproachBile acids, lipidsANN
Uyumazturk et al[26]2019Deep learning for the digital pathologic diagnosis of cholangiocarcinoma and hepatocellular carcinoma: evaluating the impact of a web-based diagnostic assistantHistologyDL
Wang et al[51]2020SCCNN: A Diagnosis Method for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Siamese Cross Contrast Neural NetworkCTANN
Wang et al[52]2019Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging featuresMRIDL
Xu et al[33]2019A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinomaMRISVM
Xu et al[30]2021Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer TomographyContrast enhanced CTML
Yang et al[36]2020Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinomaMRIRadiomics
Yao et al[34]2020A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine ModelMRISVM
Yasaka et al[53]2018Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary StudyCTCNN
Zhang et al[32]2020Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learningCTRadiomics
Zhao et al[28]2020CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligenceTissue biopsyCNN
Zhou et al[54]2021Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary StudyMultiphasic CTCNN
Table 3 Studies utilizing artificial intelligence in the treatment and prognostication of cholangiocarcinoma
Year of publication
Title of study
AI variables
AI model
Jeong et al[39]2020Latent Risk Intrahepatic Cholangiocarcinoma Susceptible to Adjuvant Treatment After Resection: A Clinical Deep Learning ApproachCT, albumin, platelets, Diabetes, CA 19-9ML
Ji et al[55]2019Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival OutcomesCT reported LN featuresANN
Li et al[41]2020A Novel Prognostic Scoring System of Intrahepatic Cholangiocarcinoma With Machine Learning Basing on Real-World DataCEA, CA 19-9, tumor stageML
Muller et al[42]2021Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof-of-Concept Study Using Artificial Intelligence for Risk AssessmentTumor size, tumor boundary, serologyANN
Shao et al[43]2018Artificial Neural Networking Model for the Prediction of Early Occlusion of Bilateral Plastic Stent Placement for Inoperable Hilar CholangiocarcinomaTumor size, nodal involvementANN
Tang et al[40]2021The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinomaTumor size, cirrhosis in CTRadiomics
Tsilimigras et al[37]2020A Novel Classification of Intrahepatic Cholangiocarcinoma Phenotypes Using Machine Learning Techniques: An International Multi-Institutional AnalysisTumor size, nodal involvement, serologyML