CT-Derived Radiomic Signature of MUC6 Expression Improves Guideline-Based Risk Stratification in Intraductal Papillary Mucinous Neoplasms.
Accurate pre-operative identification of high-risk intraductal papillary mucinous neoplasms (IPMNs) remains a major clinical challenge, particularly for branch-duct (BD) lesions where guideline-based criteria incompletely capture biologic aggressiveness. We investigated whether tumoral mucin expression identifies high-risk IPMN pathology (i.e., high-grade dysplasia or invasive carcinoma) and whether computed tomography (CT)-derived radiomic features can serve as non-invasive biomarkers to enhance pre-operative risk assessment beyond international consensus guidelines (ICG) criteria.
Multiplex immunofluorescence quantified MUC1, MUC2, MUC5AC, and MUC6 expression in tissue microarrays from 101 surgically resected IPMNs classified as low-risk (low-grade dysplasia) or high-risk (high-grade dysplasia or invasive carcinoma). Associations were evaluated using Wilcoxon rank-sum tests, and their discriminatory capability evaluated using receiver operating characteristic curves. For mucins predictive of high-risk pathology, a CT-based 'radiomic' signature was developed. Incremental value beyond ICG criteria was evaluated using discrimination metrics and decision curve analysis.
Reduced MUC6 expression was significantly associated with high-risk pathology (p = 0.001) and had the highest discriminatory performance (AUC = 0.72). A CT-derived radiomic signature predictive of low MUC6 expression achieved an AUC of 0.75 and, when integrated with ICG high-risk stigmata (HRS), demonstrated improved discrimination and favorable decision-curve characteristics compared with HRS alone, including among BD-IPMNs.
Loss of tumoral MUC6 expression is associated with high-risk IPMN pathology and may be approximated using CT-derived radiomic features, supporting the feasibility of non-invasive molecular phenotyping. These findings suggest that integration of molecular and imaging biomarkers with guideline-based criteria may enhance pre-operative IPMN risk stratification; however, prospective external validation in broader surveillance populations and multi-institutional cohorts is warranted prior to clinical implementation.
Multiplex immunofluorescence quantified MUC1, MUC2, MUC5AC, and MUC6 expression in tissue microarrays from 101 surgically resected IPMNs classified as low-risk (low-grade dysplasia) or high-risk (high-grade dysplasia or invasive carcinoma). Associations were evaluated using Wilcoxon rank-sum tests, and their discriminatory capability evaluated using receiver operating characteristic curves. For mucins predictive of high-risk pathology, a CT-based 'radiomic' signature was developed. Incremental value beyond ICG criteria was evaluated using discrimination metrics and decision curve analysis.
Reduced MUC6 expression was significantly associated with high-risk pathology (p = 0.001) and had the highest discriminatory performance (AUC = 0.72). A CT-derived radiomic signature predictive of low MUC6 expression achieved an AUC of 0.75 and, when integrated with ICG high-risk stigmata (HRS), demonstrated improved discrimination and favorable decision-curve characteristics compared with HRS alone, including among BD-IPMNs.
Loss of tumoral MUC6 expression is associated with high-risk IPMN pathology and may be approximated using CT-derived radiomic features, supporting the feasibility of non-invasive molecular phenotyping. These findings suggest that integration of molecular and imaging biomarkers with guideline-based criteria may enhance pre-operative IPMN risk stratification; however, prospective external validation in broader surveillance populations and multi-institutional cohorts is warranted prior to clinical implementation.
Authors
Davis Davis, Park Park, Basinski Basinski, Alhassan Alhassan, Gomez Gomez, Genilo-Delgado Genilo-Delgado, Sinnamon Sinnamon, Hodul Hodul, Karolak Karolak, Sayegh Sayegh, Nguyen Nguyen, Rummens Rummens, Li Li, Tripathi Tripathi, Parker Parker, Pimiento Pimiento, Rasool Rasool, Tassielli Tassielli, Chen Chen, Centeno Centeno, Jiang Jiang, Jeong Jeong, Permuth Permuth
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