Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges

Table 1 Applications of artificial intelligence based on medical imaging for intrahepatic cholangiocarcinoma diagnosis

Ref.	Sample size	Data source	Algorithms	Aim	Validation set or test set results
Ding et al[27]	3725 cases	CEUS	LSTM, MLP	ICC diagnosis	Accuracy (97%)
Ren et al[29]	226 cases	US	SVM	ICC vs HCC	AUC (0.936), sensitivity (90%), specificity (85.7%), accuracy (86.8%)
Chen et al[31]	465 cases	B-mode US	ResNet18	ICC vs HCC vs cHCC-CCA	AUC (0.9237), sensitivity (84.59%), specificity (92.65%), accuracy (86%)
Qian et al[32]	169 cases	2D US	LASSO, RFE, RF	ICC vs IBDS	AUC (0.988)
Gao et al[36]	723 cases	Multi-phase CECT	CNN, RNN	ICC vs HCC	AUC (0.944), accuracy (82.9%)
Wei et al[38]	4039 cases	Multi-phase CECT	ResNet50, SKD	ICC vs HCC vs metastatic liver cancer	AUC (0.956), accuracy (88.7%)
Midya et al[39]	814 cases	Portal venous phase CECT	Inception v3	ICC vs HCC vs CRLM vs liver benign tumors	Accuracy (96.27%)
Xue et al[40]	96 cases	Arterial-phase CECT	LASSO	IBDS with ICC vs IBDS with cholangitis	AUC (0.879)
Yang et al[42]	112 cases	CECT	LASSO	ICC vs EHA	AUC (0.868), sensitivity (94.4%), specificity (81.3%)
Xu et al[43]	129 cases	CECT	RF, LDA	ICC vs HL	AUC (0.997), accuracy (96.9%)
Liu et al[44]	177 cases	DCE-MRI	LASSO	ICC vs HCC	AUC (0.877)
Hu et al[46]	514 cases	Multi-phasic MRI	TPOT	ICC vs HCC	AUC (0.79), accuracy (75%), sensitivity (75%), specificity (79%)
Liu et al[47]	112 cases	T2-weighted MRI	SFFNet	MF-ICC vs HCC	AUC (0.968), accuracy (92.26%)
Zhou et al[48]	216 cases	DCE-MRI	LASSO	MF-ICC vs cHCC-CCA	AUC (0.897), sensitivity (79%), specificity (76.1%), accuracy (76.9%)
Xu et al[49]	133 cases	Multiparameter MRI	mRMR, LASSO	MF-ICC vs CRLM	AUC (0.94)
Starmans et al[50]	486 cases	T2-weighted MRI	WORC	ICC vs HCA vs FNH	AUC (0.78), sensitivity (84%), specificity (62%), accuracy (71%)
Cheng et al[51]	178 cases	CECT, MRI	ResNet50, LASSO	ICC diagnosis	AUC (0.937), sensitivity (80%), specificity (87.2%), accuracy (85.2%)
Jiang et al[52]	127 cases	18F-FDG PET/CT	SFFS, RF	ICC vs HCC	AUC (0.86), sensitivity (78%), specificity (88%), accuracy (82%)

CEUS: Contrast-enhanced ultrasound; US: Ultrasound; CECT: Contrast-enhanced computed tomography; DCE-MRI: Dynamic-contrast-enhanced magnetic resonance imaging; MRI: Magnetic resonance imaging; 18F-FDG PET/CT: 18F-fluorodeoxyglucose positron emission tomography/computed tomography; LSTM: Long short-term memory; MLP: Multilayer perceptron; SVM: Support vector machine; ResNet: Residual neural network; LASSO: Least absolute shrinkage and selection operator; RF: Random forest; RFE: Recursive feature elimination; CNN: Convolutional neural network; RNN: Recurrent neural network; SKD: Self-knowledge distillation module; LDA: Linear discriminant analysis; TPOT: Tree-based pipeline optimization tool; SFFNet: Strided feature fusion residual network; mRMR: Maximum relevance and minimum redundancy; WORC: Workflow for optimal radiomics classification; SFFS: Sequential forward floating selection; ICC: Intrahepatic cholangiocarcinoma; HCC: Hepatocellular carcinoma; cHCC-CCA: Combined hepatocellular-cholangiocarcinoma; IBDS: Intrahepatic bile duct stones; CRLM: Colorectal liver metastasis; EHA: Early hepatic abscess; HL: Hepatic lymphoma; MF-ICC: Mass-forming intrahepatic cholangiocarcinoma; FNH: Focal nodular hyperplasia; HCA: Hepatocellular adenoma; AUC: Area under the curve.

Table 2 Applications of artificial intelligence in predicting intrahepatic cholangiocarcinoma recurrence risk factors

Ref.	Sample size	Data source	Algorithms	Aim	Validation/training set results
Xu et al[65]	106 cases	T1-weighted contrast-enhanced MRI	mRMR, SVM	LNM prediction	AUC (0.87), sensitivity (89.47%), specificity (69.57%), accuracy (78.57%)
Xu et al[67]	116 cases	CECT	mRMR, LASSO	TLSs status prediction	AUC (0.85)
Mi et al[68]	271 cases	CEA, CA19-9, number, differentiation, and primary site of tumor	SVM	Prediction of LN status for non-dissected patients	AUC (0.754)
Gao et al[72]	519 cases	DCE-MRI	CNN	MVI prediction	AUC (0.895), sensitivity (73.9%), specificity (89.6%), accuracy (85.6%)
Ma et al[74]	160 cases	T1-weighted MRI, T2-weighted MRI, DWI	LASSO, LR, SVM	MVI prediction	AUC (0.867), sensitivity (64.3%), specificity (80%)
Jiang et al[52]	127 cases	18F-FDG PET/CT	SFFS, RF	MVI prediction	AUC (0.90), sensitivity (75%), specificity (80%), accuracy (77%)
Fiz et al[75]	74 cases	18F-FDG PET/CT	CART, RF	Prediction of MVI and ICC grading	AUC (0.87) for MVI, AUC (0.78) for grading
Liu et al[79]	243 cases	CECT	XGBoost	PNI prediction	AUC (0.831), sensitivity (76.2%), specificity (87.2%), accuracy (81.5%)
Qian et al[82]	178 cases	Gd-DTPA-enhanced MRI	LASSO, LDA	Ki67 prediction	AUC (0.815), sensitivity (75%), specificity (76.7%), accuracy (71.4%)
Peng et al[76]	128 cases	US	SVM, LASSO, LR, GBDT, bagging	Prediction of MVI, PNI, Ki67, VEGF, CK7, and differentiation	Sensitivity (75%), specificity (72.2%), accuracy (73.5%) for ICC differentiation

MRI: Magnetic resonance imaging; CECT: Contrast-enhanced computed tomography; CA19-9: Carbohydrate antigen 19-9; DCE-MRI: Dynamic-contrast-enhanced magnetic resonance imaging; DWI: Diffusion-weighted imaging; 18F-FDG PET/CT: 18F-fluorodeoxyglucose positron emission tomography/computed tomography; Gd-DTPA: Gadolinium-diethylenetriamine pentaacetic acid; US: Ultrasound; mRMR: Maximum relevance and minimum redundancy; SVM: Support vector machine; CNN: Convolutional neural network; LASSO: Least absolute shrinkage and selection operator; LR: Logistic regression; SFFS: Sequential forward floating selection; RF: Random forest; XGBoost: Extreme gradient boosting; LDA: Linear discriminant analysis; GBDT: Gradient boosted decision tree; LNM: Lymph node metastasis; TLSs: Tertiary lymphoid structures; LN: Lymph node; MVI: Microvascular invasion; PNI: Perineural invasion; VEGF: Vascular endothelial growth factor; CK7: Cytokeratin 7; AUC: Area under the curve; CART: Classification and regression tree; CEA: Carcinoma embryonic antigen.