Interpretable multimodal artificial intelligence model for predicting advanced neoplasia in pancreatic cystic lesions
World Journal of Gastrointestinal Oncology, cilt.18, sa.7, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 7
- Basım Tarihi: 2026
- Doi Numarası: 10.4251/wjgo.v18.i7.119847
- Dergi Adı: World Journal of Gastrointestinal Oncology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE
- Anahtar Kelimeler: Pancreatic cystic lesions, Artificial intelligence, Endoscopic ultrasound, Cyst fluid biomarkers, Intraductal papillary mucinous neoplasm, Risk stratification
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
BACKGROUND International guidelines for pancreatic cystic lesions (PCLs) rely primarily on morphology-based criteria and demonstrate limited discrimination for advanced neoplasia. The integration of clinical, endoscopic ultrasound (EUS), and cyst fluid biomarkers into interpretable risk models may improve preoperative risk stratification. AIM To develop and internally validate an interpretable multimodal model for predicting advanced neoplasia in PCLs. METHODS This retrospective single-center cohort study included 187 adults who underwent surgical resection for PCLs between 2012 and 2025. Clinical variables, cross-sectional imaging findings, EUS features, and cyst fluid biomarkers (carcinoembryonic antigen and glucose) were analyzed. Using least absolute shrinkage and selection operator-penalized logistic regression, a core clinical-EUS model and an integrated multimodal model were developed (training set, n = 131) and internally validated (n = 56). Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Calibration and decision curve analysis were also performed. Model performance was compared with American Gastroenterological Association 2015, Fukuoka 2017, European 2018, and Kyoto 2024 criteria. RESULTS Advanced neoplasia was identified in 58 of 187 patients (31.0%). In the validation cohort (17 advanced; 39 non-advanced), the integrated multimodal model achieved an AUC of 0.91 (95%CI: 0.82-0.97), with a sensitivity of 82.4% and specificity of 89.7%, significantly outperforming international guideline-based criteria (AUC range: 0.70-0.79; all P < 0.01). Performance remained stable in the intraductal papillary mucinous neoplasm subset (AUC 0.90) and in cysts without mural nodules (AUC 0.88). Decision curve analysis demonstrated a superior net benefit across clinically relevant thresholds. CONCLUSION An interpretable multimodal model integrating clinical, EUS, and cyst fluid data improves the discrimination of advanced neoplasia in surgically resected PCLs and may support preoperative risk stratification. Prospective external validation is required before clinical implementation.