Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma

Table 2 Predictive performance and receiver operating characteristic curve analyses of the five radiomics features with the highest area under the curve values and the best performing artificial intelligence-based machine-learning model for pathological complete response

	AUC	Sensitivity	Specificity	Accuracy	Cut off	95%CI	P value
ADC skewness	0.77	0.67	0.82	0.79	0.37	0.61-0.88	0.005
GLCM entropy (b = 1000 second/mm²)	0.76	0.87	0.62	0.67	9.28	0.64-0.85	0.002
GLCM autocorrelation (b = 0 second/mm²)	0.76	0.53	0.93	0.84	5276.1	0.60-0.87	0.009
Skewness (b = 0 second/mm²)	0.73	0.87	0.42	0.64	0.72	0.61-0.84	0.006
Kurtosis (b = 0 second/mm²)	0.72	0.73	0.64	0.66	5.05	0.55-0.82	0.005
Machine learning radiomics model	0.85	0.80	0.85	0.81	NA	0.73-0.93	< 0.001

AUC: Area under the curve; CI: Confidence interval; ADC: Apparent diffusion coefficient; GLCM: Gray-level co-occurrence matrix; NA: Not applicable.