Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms
| Autor: | Matthew P. Doepker, Sonia T. Orcutt, Jose G. Trevino, Domenico Coppola, Barbara A. Centeno, Sarah E. Hoffe, Kun Jiang, Yoganand Balarunathan, Robert J. Gillies, Qian Li, Lu Chen, Jennifer B. Permuth, Anthony M. Magliocco, Jongphil Kim, Kenneth L. Gage, Kujtim Latifi, Dung-Tsa Chen, Nipun B. Merchant, Geoffrey Zhang, Mokenge P. Malafa, Jung Choi |
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| Rok vydání: | 2016 |
| Předmět: |
Male
Pathology medicine.medical_specialty risk stratification Malignancy pre-malignant lesions 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Radiomics Surgical oncology Pancreatic cancer Radiation oncology Epidemiology of cancer Medicine Humans pancreas Aged miRNA Aged 80 and over business.industry Retrospective cohort study Middle Aged medicine.disease Adenocarcinoma Mucinous Carcinoma Papillary 3. Good health Pancreatic Neoplasms MicroRNAs Oncology radiomics 030220 oncology & carcinogenesis Risk stratification Female business Tomography X-Ray Computed Carcinoma Pancreatic Ductal Research Paper |
| Zdroj: | Oncotarget |
| ISSN: | 1949-2553 |
| Popis: | // Jennifer B. Permuth 1,2 , Jung Choi 3 , Yoganand Balarunathan 4 , Jongphil Kim 5 , Dung-Tsa Chen 5 , Lu Chen 5 , Sonia Orcutt 2 , Matthew P. Doepker 11 , Kenneth Gage 3 , Geoffrey Zhang 4,6 , Kujtim Latifi 4,6 , Sarah Hoffe 2,6 , Kun Jiang 7 , Domenico Coppola 7 , Barbara A. Centeno 7 , Anthony Magliocco 7 , Qian Li 4,8 , Jose Trevino 9 , Nipun Merchant 10 , Robert Gillies 4 and Mokenge Malafa 2 on behalf of the Florida Pancreas Collaborative 1 Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 2 Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 3 Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 4 Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 5 Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 6 Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 7 Anatomic Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA 8 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China 9 Department of Surgery, Division of General Surgery, University of Florida Health Sciences Center, Gainesville, Florida, USA 10 Department of Surgery, Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, Miami, Florida, USA 11 Department of Clinical Surgery/Surgical Oncology, Palmetto Health/USC School of Medicine, Columbia, South Carolina, USA Correspondence to: Jennifer B. Permuth, email: // Keywords : radiomics, miRNA, risk stratification, pre-malignant lesions, pancreas Received : July 06, 2016 Accepted : July 14, 2016 Published : August 31, 2016 Abstract 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 0.80 (0.87 (95% CI:0.84-0.89)). This proof-of-concept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than ‘worrisome’ radiologic features considered in consensus guidelines. |
| Databáze: | OpenAIRE |
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