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

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.