Artificial intelligence in quantitative imaging of esophageal cancer: A review on radiomics, sarcopenia, and survival modeling

Table 1 Quantitative radiomic features used in esophageal cancer

Feature category	Primary parameters	Description	Clinical significance
First-order	Mean, median, skewness, kurtosis, entropy, energy	Statistical distribution of voxel intensities without spatial context	Reflects global tumor density, degree of necrosis, and metabolic activity
Second-order (texture)	GLCM, GLSZM, GLRLM, NGTDM	Spatial relationships and patterns between neighboring pixels	Quantifies intratumoral heterogeneity; predicts treatment resistance and aggressive phenotypes
Shape-based	Sphericity, surface-to-volume ratio, compactness, elongation	Three-dimensional geometric and morphological properties	Correlates with longitudinal infiltration and circumferential involvement for T-staging
Higher-order	Wavelet, LoG, square root	Mathematical filters applied to original images to enhance specific frequencies	Captures fine structural details and patterns linked to microvascular complexity and hypoxia
Deep features	CNN latent layers	Abstract patterns learned automatically by neural networks	High predictive power for survival, though often lacking direct biological interpretability

GLCM: Gray-level co-occurrence matrix; GLSZM: Gray-level size zone matrix; GLRLM: Gray-level run length matrix; NGTDM: Neighborhood gray-tone difference matrix; LoG: Laplacian-of-gaussian; CNN: Convolutional neural network.