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

Table 4 Artificial intelligence based radiomics models for non-invasive diagnosis and risk stratification of biliary strictures

Study year	AI model	Data source	Key performance	Clinical impact
2025, meta nalysis[56]	Various ML, LR, RF, SVM, DL	CT (13 studies), EUS (5 studies), MRI (3 studies), PET-CT (3 studies), 24 case-control studies; 14406 patients (7635 PDAC)	Sensitivity 0.92 (95%CI: 0.91-0.94); Specificity 0.90 (95%CI: 0.85-0.94); AUC 0.94 (95%CI: 0.74-0.99); DOR 110 (95%CI: 62-194)	Non-invasive PDAC screening; reduces EUS-FNA need; CT best for initial triage, limited by moderate radiomics quality score and case-control design
Multi-institutional radiomics study[52]	ML classifiers	CT imaging of PDACs < 2 cm	Sensitivity > 90% for small PDAC detection; cross-population generalizability demonstrated	First to characterize PDAC-specific radiomic signature (decreased intensity, increased NGTDM busyness) linking imaging features to desmoplastic heterogeneity; validated across multiple populations
Differentiation AIP vs PDAC[53].	Radiomics-based ML	Thin-slice venous-phase CT	Accuracy 95.2%; AUC 0.975; sensitivity 89.7%; specificity 100%	Demonstrated superiority of thin-slice venous-phase imaging; proved AI can discriminate malignancy from complex benign inflammatory conditions (previously a major limitation)
PET/CT radiogenomics[54,55]	Radiomic analysis	FDG PET/CT with genomic correlation	Metabolic texture features correlate with KRAS and SMAD4 mutations	First demonstration that radiomic phenotypes reflect underlying tumor genotype; provides non-invasive window into tumor biology beyond structural imaging
2023, narrative review[57]	Radiomics review (various ML)	CT/MRI across HPB studies	PDAC detection/differentiation: AUC 0.71-0.99. Tumor grading prediction: AUC 0.73-0.90. IPMN high-grade dysplasia prediction: AUC up to 0.84	Supports early PDAC screening, cyst risk stratification (e.g., IPMN high-grade dysplasia AUC 0.84), and resectability assessment in pancreatic/HCC/ICC

AIP: Autoimmune pancreatitis; ML: Machine learning; LR: Logistic regression; HPB: Hepatopancreatobiliary; RF: Random forest; SVM: Support vector machine; DL: Deep learning; CT: Computed tomography; EUS: Endoscopic ultrasound; MRI: Magnetic resonance imaging; PET: Positron emission tomography; PDAC: Pancreatic ductal adenocarcinomas; CI: Confidence interval; AUC: Area under the curve; DOR: Diagnostic odds ratio; FNA: Fine needle aspiration; NGTDM: Neighboring grey-tone difference matrix; AI: Artificial intelligence; IPMN: Intraductal papillary mucinous neoplasm; HCC: Hepatocellular carcinoma; ICC: Intrahepatic cholangiocarcinoma; FDG: Fluorodeoxyglucose.