Abstract 970A: Using a radiogenomic approach to classify pancreatic cancer precursors
Authors: Permuth Jennifer B. , Choi Jung , Balarunathan Yoganand , Kim Jongphil , Chen Dung-Tsa , Jiang Kun , Orcutt Sonia , Chen Lu , Quinn Kimberly , Carvajal Rodrigo , Gonzalez-Calderon Guillermo , Fournier Michelle , Abdalla Mahmoud , Garcia Alberto , Bouton Amber , Yakoub Danny , Lechner Suzanne , Trevino Jose , Merchant Nipun , Gillies Robert , Malafa Mokenge 
Published: July 2016

Abstract
Background: Intraductal papillary mucinous neoplasms (IPMNs) are cystic pancreatic cancer precursors incidentally detected by imaging in more than 75,000 Americans each year. There is an unmet need to discover noninvasive approaches to differentiate ‘benign’ IPMNs that can be monitored from ‘malignant’ IPMNs that warrant surgery. We previously developed a blood-based ‘miRNA genomic classifier (MGC)’ that helps predict malignant IPMN pathology. The goal of this study was to evaluate whether novel radiomic features from preoperative computed tomography (CT) scans may improve prediction of IPMN pathology beyond that provided by standard radiologic features, either alone or in combination with the MGC.
Methods: Preoperative CT images were obtained for 37 surgically-resected, pathologically-confirmed IPMN cases with matched preoperative miRNA expression data. Images were reviewed for standard radiologic features characterized to be ‘high-risk’ or ‘worrisome’ for malignancy according to consensus guidelines. The region of interest within the pancreas was identified and segmented using a semi-automated algorithm. A total of 112 two-dimensional (2D) quantitative texture features (which measure tumor size, shape, and location) and non-texture features (which measure smoothness, coarseness, and regularity) were extracted. Logistic regression models were used to explore associations between non-redundant radiomic features and IPMN pathology. Principal component analysis was also performed to generate an index score (defined by the first principal component of the most promising radiomic features) that was evaluated for its association with malignant pathology. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the new radiomic features individually and in combination with the MGC were estimated and compared to values obtained for standard radiologic features.
Results: The MGC and standard ‘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. Analysis of 112 extracted preoperative radiomic CT features revealed 14 textural and non-textural features that differentiated malignant from benign IPMNs (P&lt;0.05). Collectively, the 14 radiomic features had an AUC = 0.77. A model that combined radiomic features and the MGC had an AUC = 0.92 and estimates of sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) that were superior to models not based on these novel data types.
Conclusions: Our preliminary findings suggest that clinical decision-making models that integrate novel quantitative radiomic features with a blood-based miRNA classifier may more accurately predict IPMN pathology than models that rely on standard clinical data or worrisome radiologic features alone. Larger, prospective, multi-center studies are planned to explore this topic further.
Citation Format: Jennifer B. Permuth, Jung Choi, Yoganand Balarunathan, Jongphil Kim, Dung-Tsa Chen, Kun Jiang, Sonia Orcutt, Lu Chen, Kimberly Quinn, Rodrigo Carvajal, Guillermo Gonzalez-Calderon, Michelle Fournier, Mahmoud Abdalla, Alberto Garcia, Amber Bouton, Danny Yakoub, Suzanne Lechner, Jose Trevino, Nipun Merchant, Robert Gillies, Mokenge Malafa. Using a radiogenomic approach to classify pancreatic cancer precursors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 970A.