Artificial intelligence and cholangiocarcinoma: Updates and prospects

Table 2 Studies utilizing artificial intelligence in the diagnosis of cholangiocarcinoma

Ref.	Year of publication	Title of study	Diagnostic modality	AI model
Chu et al[44]	2021	Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma	CT	LR
Ibragimov et al[45]	2020	Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy	CT	CNN
Liu et al[46]	2021	Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?	MRI, CT	SVM
Logeswaran[35]	2009	Cholangiocarcinoma--an automated preliminary detection system using MLP	MRCP	ANN
Midya et al[47]	2018	Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma	CT	CNN
Nakai et al[29]	2021	Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study	CT, tumor markers	CNN
Negrini et al[22]	2020	Machine Learning Model Comparison in the Screening of Cholangiocarcinoma Using Plasma Bile Acids Profiles	Serum bile acids	ML
Pattanapairoj et al[23]	2015	Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach	Tumor markers	ANN
Peng et al[48]	2020	Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma	US	SVM
Peng et al[49]	2020	Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer	US	Radiomics
Ponnoprat et al[31]	2020	Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans	CT	CNN
Selvathi et al[50]	2013	Automatic segmentation and classification of liver tumor in CT images using adaptive hybrid technique and Contourlet based ELM classifier	CT	ELM
Sun et al[25]	2021	Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks	Histology	CNN
Urman et al[24]	2020	Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach	Bile acids, lipids	ANN
Uyumazturk et al[26]	2019	Deep learning for the digital pathologic diagnosis of cholangiocarcinoma and hepatocellular carcinoma: evaluating the impact of a web-based diagnostic assistant	Histology	DL
Wang et al[51]	2020	SCCNN: A Diagnosis Method for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Siamese Cross Contrast Neural Network	CT	ANN
Wang et al[52]	2019	Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features	MRI	DL
Xu et al[33]	2019	A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma	MRI	SVM
Xu et al[30]	2021	Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography	Contrast enhanced CT	ML
Yang et al[36]	2020	Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma	MRI	Radiomics
Yao et al[34]	2020	A 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 Model	MRI	SVM
Yasaka et al[53]	2018	Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study	CT	CNN
Zhang et al[32]	2020	Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning	CT	Radiomics
Zhao et al[28]	2020	CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence	Tissue biopsy	CNN
Zhou et al[54]	2021	Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study	Multiphasic CT	CNN

AI: Artificial intelligence; ANN: Artificial Neural Network; CCA: Cholangiocarcinoma; CNN: Convolutional neural network; CT: Computed tomography; DL: deep learning; ML: machine learning; ELM: Extreme learning machine; LR: Logistic regression; MRCP: Magnetic resonance cholangiopancreatography; MRI: Magnetic resonance imaging; SVM: Support-vector machine, US: Ultrasound.