Advanced imaging techniques at the crossroads of cholangiocarcinoma and liver transplantation: Can we bridge the gap?

Table 1 Emerging imaging strategies for cholangiocarcinoma assessment in liver transplant candidates

Imaging strategy	Key features	Strengths	Limitations	Ref.
Adapted LI-RADS for CCA	Targetoid appearance, rim enhancement, ADC thresholds	Standardization; improves interobserver agreement	Requires validation; originally developed for HCC	Hong et al[12]; Majeed et al[18]; Jiang et al[19]; Pankaj Jain et al[20]
AI-driven radiomics	Texture analysis; deep learning for microvascular invasion, recurrence risk	High predictive accuracy; individualized risk stratification	Limited generalizability; needs multicenter training	Ahmadzadeh et al[3]; Zerunian et al[22]; Barcena-Varela et al[23]; Lindner et al[24]; Ji et al[25]
Contrast-enhanced ultrasound	Rim enhancement, early washout, intratumorally vein	Portable; low cost; high specificity in cirrhosis	Operator-dependent; limited spatial resolution	O’Brien et al[16]; Wu et al[27]; Smajerova et al[28]

LI-RADS: Liver Imaging Reporting and Data System; CCA: Cholangiocarcinoma; AI: Artificial intelligence; ADC: Apparent diffusion coefficient; HCC: Hepatocellular carcinoma.