TY - JOUR
T1 - Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms
AU - Florida Pancreas Collaborative
AU - Permuth, Jennifer B.
AU - Choi, Jung
AU - Balarunathan, Yoganand
AU - Kim, Jongphil
AU - Chen, Dung Tsa
AU - Chen, Lu
AU - Orcutt, Sonia
AU - Doepker, Matthew P.
AU - Gage, Kenneth
AU - Zhang, Geoffrey
AU - Latifi, Kujtim
AU - Hoffe, Sarah
AU - Jiang, Kun
AU - Coppola, Domenico
AU - Centeno, Barbara A.
AU - Magliocco, Anthony
AU - Li, Qian
AU - Trevino, Jose
AU - Merchant, Nipun
AU - Gillies, Robert
AU - Malafa, Mokenge
N1 - Funding Information:
This study was supported in part by: Institutional Research Grant number 93-032-16 from the American Cancer Society (PI: J. Permuth), the State of Florida and the Florida Academic Cancer Center Alliance (FACCA) (Co-PIs: J. Permuth, M. Malafa, N. Merchant, J. Trevino), and the Total Cancer Care? Protocol and the Collaborative Data Services, Tissue, Molecular Genomics, Biostatistics, and Cancer Informatics Core Facilities at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292).
PY - 2016
Y1 - 2016
N2 - Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors incidentally discovered by cross-sectional imaging. Consensus guidelines for IPMN management rely on standard radiologic features to predict pathology, but they lack accuracy. Using a retrospective cohort of 38 surgically-resected, pathologically-confirmed IPMNs (20 benign; 18 malignant) with preoperative computed tomography (CT) images and matched plasma-based 'miRNA genomic classifier (MGC)' data, we determined whether quantitative 'radiomic' CT features (+/- the MGC) can more accurately predict IPMN pathology than standard radiologic features 'high-risk' or 'worrisome' for malignancy. Logistic regression, principal component analyses, and cross-validation were used to examine associations. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated. The MGC, 'high-risk,' and 'worrisome' radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Fourteen radiomic features differentiated malignant from benign IPMNs (p < 0.05) and collectively had an AUC=0.77. Combining radiomic features with the MGC revealed an AUC=0.92 and superior sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) than other models. Evaluation of uncertainty by 10-fold cross-validation retained an AUC > 0.80 (0.87 (95% CI:0.84-0.89)). This proof-ofconcept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than 'worrisome' radiologic features considered in consensus guidelines.
AB - Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors incidentally discovered by cross-sectional imaging. Consensus guidelines for IPMN management rely on standard radiologic features to predict pathology, but they lack accuracy. Using a retrospective cohort of 38 surgically-resected, pathologically-confirmed IPMNs (20 benign; 18 malignant) with preoperative computed tomography (CT) images and matched plasma-based 'miRNA genomic classifier (MGC)' data, we determined whether quantitative 'radiomic' CT features (+/- the MGC) can more accurately predict IPMN pathology than standard radiologic features 'high-risk' or 'worrisome' for malignancy. Logistic regression, principal component analyses, and cross-validation were used to examine associations. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated. The MGC, 'high-risk,' and 'worrisome' radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Fourteen radiomic features differentiated malignant from benign IPMNs (p < 0.05) and collectively had an AUC=0.77. Combining radiomic features with the MGC revealed an AUC=0.92 and superior sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) than other models. Evaluation of uncertainty by 10-fold cross-validation retained an AUC > 0.80 (0.87 (95% CI:0.84-0.89)). This proof-ofconcept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than 'worrisome' radiologic features considered in consensus guidelines.
KW - MiRNA
KW - Pancreas
KW - Pre-malignant lesions
KW - Radiomics
KW - Risk stratification
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U2 - 10.18632/oncotarget.11768
DO - 10.18632/oncotarget.11768
M3 - Article
C2 - 27589689
AN - SCOPUS:85007494966
VL - 7
SP - 85785
EP - 85797
JO - Oncotarget
JF - Oncotarget
SN - 1949-2553
IS - 52
ER -