Prediction of lymph node metastasis in early esophageal cancer

Table 2 Models predicting lymph node metastasis in early esophageal squamous cell carcinoma

Number	Ref.	Year	Factors	Category	Training set		Validation set
					Cutoff value	C-index (95%CI)	Cutoff value	C-index (95%CI)
1	Jia et al[43]	2016	Depth of invasion and lymphovascular metastasis	Logistic regression model	NA	0.858 (0.757-0.959)	NA	NA
2	Zhou et al[42]	2019	Dumor size, tumor grade, depth of invasion, and angiolymphatic invasion	Logistic regression model	20%	0.80 (0.737-0.862)	20%	0.814 (0.724-0.905)
3				Nomogram	NA	0.80 (0.739-0.856)	NA	0.814 (0.725-0.900)
4	Zheng et al[40]	2018	Depth of tumor invasion, grade of differentiation, tumor size, and lymphovascular invasion	Nomogram	0.142	0.790 (0.717-0.864)	0.224	0.789 (0.709-0.869)
5	Chen et al [39]	2020	Tumor size, histologic grade, invasion depth, lymphovascular invasion, CT-results, and alcohol taking	Logistic regression model	3.735	0.857 (NA)	3.735	0.881 (NA)
6				artificial neural network	NA	0.904 (NA)	NA	0.938 (NA)

C-index: Harrell’s concordance index; CI: Confidence interval; NA: Not available; CT: Computed tomography.