Preoperative Prediction of Subtype and Artificial Intelligence-Driven Endomicroscopy Detection of Advanced Neoplasia in Intraductal Papillary Mucinous Neoplasms.
Non-gastric subtype of branch duct (BD)-intraductal papillary mucinous neoplasms (IPMNs) are associated with high-grade dysplasia/invasive adenocarcinoma (HGD/IC) and disease progression. We evaluated preoperative prediction of gastric vs. non-gastric BD-IPMN subtypes and assessed a needle-based confocal laser endomicroscopy-guided artificial intelligence algorithm (nCLE-AI) for detecting HGD/IC in pathologist-reclassified BD-IPMNs.
Participants with resected BD-IPMNs were enrolled from prospective studies (2015-2024). Phase 1: lesions were reclassified by subtype and dysplasia grade through blinded pathologist review, with discordant cases receiving MUC immunostaining and consensus review. Phase 2: using this reclassified pathology data, preoperative clinical and morphological features were analyzed to predict BD-IPMN subtypes. Phase 3: nCLE-AI performance in detecting HGD/IC within reclassified gastric and non-gastric BD-IPMNs was evaluated using preoperative endomicroscopy videos.
Among 63 resected BD-IPMNs (mean diameter=35.0±10.1 mm), 38% were classified as HGD/IC. Phase 1: The interobserver agreement among pathologists for subtype classification was moderate (k=0.52; 95%CI: 0.27-0.77). Phase 2: Multivariable analysis or preoperative variables revealed Kyoto high-risk stigmata (aOR=11.568, p=0.007), unifocal lesions (aOR=8.354, p=0.041), and lower BMI (aOR=1.37, p=0.04) predicted non-gastric subtype. Phase 3: The nCLE-AI algorithm using presurgical endomicroscopy imaging showed comparable sensitivity for detecting HGD/IC in non-gastric and gastric IPMN subtypes (83% vs. 82%, p=0.92), but significantly higher specificity (100% vs. 44%, p=0.06) and accuracy (87% vs. 53%, p<0.02) in the non-gastric subtype.
Moderate interobserver variability in BD-IPMN subtype classification among pathologists highlights the need for immunohistochemistry and consensus review in challenging cases. Preoperative clinical variables can predict non-gastric subtype which is associated with less favorable prognosis. nCLE-AI shows improved performance in detecting HGD/IC in non-gastric BD-IPMNs, where accurate risk stratification is particularly important due to higher risk of progression.
Participants with resected BD-IPMNs were enrolled from prospective studies (2015-2024). Phase 1: lesions were reclassified by subtype and dysplasia grade through blinded pathologist review, with discordant cases receiving MUC immunostaining and consensus review. Phase 2: using this reclassified pathology data, preoperative clinical and morphological features were analyzed to predict BD-IPMN subtypes. Phase 3: nCLE-AI performance in detecting HGD/IC within reclassified gastric and non-gastric BD-IPMNs was evaluated using preoperative endomicroscopy videos.
Among 63 resected BD-IPMNs (mean diameter=35.0±10.1 mm), 38% were classified as HGD/IC. Phase 1: The interobserver agreement among pathologists for subtype classification was moderate (k=0.52; 95%CI: 0.27-0.77). Phase 2: Multivariable analysis or preoperative variables revealed Kyoto high-risk stigmata (aOR=11.568, p=0.007), unifocal lesions (aOR=8.354, p=0.041), and lower BMI (aOR=1.37, p=0.04) predicted non-gastric subtype. Phase 3: The nCLE-AI algorithm using presurgical endomicroscopy imaging showed comparable sensitivity for detecting HGD/IC in non-gastric and gastric IPMN subtypes (83% vs. 82%, p=0.92), but significantly higher specificity (100% vs. 44%, p=0.06) and accuracy (87% vs. 53%, p<0.02) in the non-gastric subtype.
Moderate interobserver variability in BD-IPMN subtype classification among pathologists highlights the need for immunohistochemistry and consensus review in challenging cases. Preoperative clinical variables can predict non-gastric subtype which is associated with less favorable prognosis. nCLE-AI shows improved performance in detecting HGD/IC in non-gastric BD-IPMNs, where accurate risk stratification is particularly important due to higher risk of progression.
Authors
Koehler Koehler, Chen Chen, Esnakula Esnakula, Frankel Frankel, Abdelbaki Abdelbaki, Culp Culp, Li Li, Chao Chao, Hart Hart, Pawlik Pawlik, Shah Shah, Krishna Krishna
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