Leveraging artificial intelligence to differentiate benign from malignant biliary strictures: A step toward precision diagnosis

Table 1 Cross-sectional imaging for evaluation of biliary strictures

Imaging	Sensitivity range/specificity range, %	Notes
CT scan[15-17]	Sensitivity 75-80. Specificity 60-80	Study context: Prospective, blinded comparisons in patients with obstructive jaundice. Patient population: Mixed benign & malignant strictures (distal and hilar). Reference standard was histology or > 12 months[16,17]. Primary role: Excellent for staging, vascular assessment, and detecting metastases. Key limitation: Limited specificity for differentiating malignant from benign strictures, particularly in the absence of a discrete mass
MRCP[18]	Sensitivity 83-90. Specificity 94-98	Study context: Prospective observational study comparing MRCP directly to ERCP as a reference standard. Patient population: 60 patients with suspected CBD or pancreatic duct pathologies. Primary role: Non-invasive gold standard for evaluating biliary tree anatomy, stricture morphology, and level of obstruction. High accuracy for choledocholithiasis and ductal dilation. Key limitation: Provides anatomical, not histologic, diagnosis
18F-fluorodeoxyglucose positron emission tomography[19]	Sensitivity 85-92. Specificity 51-90	Study context: Pooled data from a meta-analysis. Patient population: 47 studies (n = 2125 patients). Primary role: Not for primary stricture characterization. High utility for staging (lymph node/distant metastasis). Changes management in approximately 15% of cases, primarily via upstaging. Key limitation: Very low specificity (51%) for diagnosing malignancy at the primary site due to false positives from inflammation (e.g., PSC, cholangitis). Cannot replace histopathological confirmation

CT: Computed tomography; MRCP: Magnetic resonance cholangiopancreatography; ERCP: Endoscopic retrograde cholangiopancreatography; CBD: Common bile duct; PSC: Primary sclerosing cholangitis.