Artificial intelligence in expanding hepatic resection boundaries: Integrating portal flow modulation and regenerative strategies

Table 4 Radiomics features predicting surgical outcomes

Outcome	Imaging modality	Key radiomics features	Best classifier	Performance (AUC/accuracy)
Recurrence (CRLM)	EOB-MRI	VIBE_FA10 textural metrics	KNN	AUC 0.91, Acc. 93%
Tumor front growth (exp. vs inf.)	CT	7 textural features (GLCM, etc.)	KNN	Acc. 97%, Sens. 90%, Spec. 100%
Tumor budding	Contrast MRI	11 textural features (arterial phase)	KNN	Acc. 95%, Sens. 84%, Spec. 99%
Early recurrence	CT	Tumor quality and quantity model	ML ensemble	AUC 0.83
Macrovesicular steatosis (donor)	CT	7 selected radiomic features	Logistic regression	AUC 0.87

CRLM: Colorectal liver metastases; EOB-MRI: Gadoxetic acid-enhanced magnetic resonance imaging; GLCM: Gray-level co-occurrence matrix; KNN: K-nearest neighbors; AUC: Area under the curve; CT: Computed tomography; ML: Machine learning; Acc: Accuracy; Sens: Sensitivity; Spec: Specificity; MRI: Magnetic resonance imaging.