Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma

Table 1 Deep learning feature biological correlation

DL feature class	MRI sequence	Radiological interpretation
Border features	T1-weighted (T1WI)	Correlates with margin sharpness and lesion boundary irregularity
Texture features	T2-weighted (T2WI)	Reflects intratumoral heterogeneity and signal intensity variations
Internal features	T2-weighted (T2WI)	Associated with the presence of internal septations or lobulations

DL: Deep learning; MRI: Magnetic resonance imaging.

Table 2 Magnetic resonance imaging technical standardization and processing workflow

Stage	Process applied	Purpose and clinical benefit
Normalization	Resampling to 1 mm isotropic voxels	Ensures all images have a consistent spatial resolution regardless of the original scanner settings
Intensity alignment	Nyul standardization	Harmonizes signal intensities across different T1 and T2 sequences to reduce “brightness” variations
Clustering	Simple-linear-iterative-clustering superpixels and fuzzy c-means clustering	Unsupervised algorithms that automatically group voxels to isolate hyperintense tumor regions
Validation	Manual radiologist review	Experts verified the automatic contours, achieving a high mean dice coefficient of 0.906
Heterogeneity management	Multi-vendor, multi-field (1.5T/3.0T)	Accounts for real-world variation in scanner hardware and field strengths
Batch correction	ComBat harmonization	Removes “center effects” or scanner-specific noise to ensure the DLBR score is stable across different hospitals

DLBR: Deep learning-based radiomics.