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

Table 6 Summary of study quality across major evidence categories

Category	Typical study design	Sample size range	Key findings	Level of evidence	Risk of bias (summary)
AI segmentation/volumetry	Technical validation, retrospective imaging datasets	40-1200 scans	Dice 092-0.97 for liver segmentation	IV	High risk-internal validation only, curated datasets, protocol heterogeneity
ML prediction models for PHLF	Retrospective multicenter or single-center	300-25000 patients	AUC 0.82-0.94 for PHLF prediction	III-IV	Moderate risk -class imbalance, unblinded outcome measurement, overfitting risk
Comparative PVE/LVD/ALPPS studies	Retrospective cohorts, meta-analyses	60-1800	LVD > PVE hypertrophy; ALPPS fastest hypertrophy	II-III	Moderate risk -selection bias, inconsistent endpoints, non-standard hypertrophy intervals
Radiomics prognostic studies	Retrospective, mostly single-center	40-300	Predict recurrence, FLR dysfunction	IV	High-to-moderate risk -small samples, overfitting, rare external validation
Preclinical regenerative biology	Rodent and in vitro	n = 6-60 animals	Pathway-level mechanistic insights	V	Low-to-moderate risk-mechanistic but non-clinical

FLR: Future liver remnant; PVE: Portal vein embolization; LVD: Liver venous deprivation; ALPPS: Associating liver partition and portal vein ligation for staged hepatectomy; ML: Machine learning; AI: Artificial intelligence; AUC: Area under the curve; PHLF: Post-hepatectomy liver failure.