Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Table 2 Application of artificial intelligence in survival prediction for esophageal squamous cell carcinoma

Ref.	Sample size	Data sources	AI algorithms	Best result	Highlight
Zhang et al[98], 2023	Training: 1954 cases; testing: 487 cases	Clinicopathological and laboratory information	Rpart, Elastic Net, RF, GBM, GLMboost, and CoxPH	AUC (1-year overall survival): 0.725; AUC (3-year overall survival): 0.0.720; AUC (5-year overall survival): 0.752	Prognosis evaluation after ESCC resection. First study comparing different AI algorithms’ performance in predicting the survival prognosis of ESCC
Zhang et al[99], 2023	Training: 3612 cases; validation: 1204 cases testing: 1204 cases	Demographic, clinicopathological, and treatment information	DeepSurv	C-index: 0.732; AUC (3-year overall survival): 0.805; AUC (5-year overall survival): 0.825	Survival prediction of ESCC. Creating a web-based tool for individual survival outcomes prediction
Ling et al[100], 2023	Training: 4062 cases; testing: 1015 cases	Demographic, clinicopathological, and treatment information	WOA-XGBoost	AUC: 0.918; accuracy: 88.1%	Prognosis evaluation of stage IB-IVA ESCC after clinical treatment. The integration of IDPC and WOA-XGBoost realizes a more accurate prediction
Wang et al[101], 2021	Training: 122 cases; testing: 31 cases	CT and histopathological image	Regularized Cox, Weibull Cox, RSF, and DeepSurv	C-index: 0.694	Prognosis evaluation after ESCC resection. Successful survival analysis through combining multiple modal images together
Liu et al[102], 2020	Training: 79 cases; testing: 71 cases	Transcriptome profile	RF, SVM	Specificity: 98.1%; sensitivity: 79.0%	Prognosis evaluation of patients with ESCC. Identifying novel genetic markers reflecting the prognosis of ESCC
Yi et al[103], 2020	Training: 182 cases; testing: 179 cases	Tumor stemness index provided by one-class logistic regression	Combination of Lasso and Cox regression	AUC (1-year survival time): 0.947; AUC (3-year survival time): 0.760; AUC (5-year survival time): 0.770	Survival prediction of ESCC. First study predicting the prognosis using the tumor intrinsic stemness pattern

RF: Random forest; GBM: Gradient boosting machine; RSF: Random survival forest; SVM: Support vector machine; ESCC: Esophageal squamous cell carcinoma; CT: Computed tomography; AI: Artificial intelligence; AUC: Area under the curve.