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CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma.

ABSTRACT: PURPOSE:The aim of this study was to investigate the relationship between CT imaging phenotypes and genetic and biological characteristics in pancreatic ductal adenocarcinoma (PDAC). METHODS:In this retrospective study, consecutive patients between April 2015 and June 2016 who underwent PDAC resection were included if previously consented to a targeted sequencing protocol. Mutation status of known PDAC driver genes (KRAS, TP53, CDKN2A, and SMAD4) in the primary tumor was determined by targeted DNA sequencing and results were validated by immunohistochemistry (IHC). Radiomic features of the tumor were extracted from the preoperative CT scan and used to predict genotype and stromal content. RESULTS:The cohort for analysis consisted of 35 patients. Genomic and IHC analysis revealed alterations in KRAS in 34 (97%) patients, and changes in expression of CDKN2A in 29 (83%), SMAD4 in 16 (46%), and in TP53 in 29 (83%) patients. Models created from radiomic features demonstrated associations with SMAD4 status and the number of genes altered. The number of genes altered was the only significant predictor of overall survival (p?=?0.016). By linear regression analysis, a prediction model for stromal content achieved an R2 value of 0.731 with a root mean square error of 19.5. CONCLUSIONS:In this study, we demonstrate that in PDAC SMAD4 status and tumor stromal content can be predicted using radiomic analysis of preoperative CT imaging. These data show an association between resectable PDAC imaging features and underlying tumor biology and their potential for future precision medicine.

SUBMITTER: Attiyeh MA

PROVIDER: S-EPMC6692205 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma.

Attiyeh Marc A MA Chakraborty Jayasree J McIntyre Caitlin A CA Kappagantula Rajya R Chou Yuting Y Askan Gokce G Seier Kenneth K Gonen Mithat M Basturk Olca O Balachandran Vinod P VP Kingham T Peter TP D'Angelica Michael I MI Drebin Jeffrey A JA Jarnagin William R WR Allen Peter J PJ Iacobuzio-Donahue Christine A CA Simpson Amber L AL Do Richard K RK

Abdominal radiology (New York) 20190901 9

<h4>Purpose</h4>The aim of this study was to investigate the relationship between CT imaging phenotypes and genetic and biological characteristics in pancreatic ductal adenocarcinoma (PDAC).<h4>Methods</h4>In this retrospective study, consecutive patients between April 2015 and June 2016 who underwent PDAC resection were included if previously consented to a targeted sequencing protocol. Mutation status of known PDAC driver genes (KRAS, TP53, CDKN2A, and SMAD4) in the primary tumor was determine ...[more]

PMID: 31243486

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Project description:ObjectivesThe purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.Methods96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.ResultsA total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.ConclusionsThe CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.

Project description:Pancreatic ductal adenocarcinoma (PDA) is notorious for its poor prognosis. The current mainstay of treatment for PDA is surgical resection followed by adjuvant chemotherapy. However, it is difficult to predict the post-operative outcome because of the lack of reliable markers. The single-nucleotide polymorphism (SNP) of N-acetylgalactosaminyltransferase14 (GALNT14) has been proven to predict the progression-free survival (PFS), overall survival (OS) and response to chemotherapy in various types of gastrointestinal (GI) cancers. However, its role in PDA has not been studied. This study aims to investigate whether the GALNT14 SNP genotype can be a prognostic marker for PDA. A cohort of one hundred and three PDA patients having received surgical resection were retrospectively enrolled. GALNT14 genotypes and the clinicopathological parameters were correlated with postoperative prognosis. The genotype analysis revealed that 19.4%, 60.2% and 20.4% of patients had the GALNT14 "TT", "TG" and "GG" genotypes, respectively. The patients with the "GG" genotype had a mean OS time of 37.1 months (95% confidence interval [CI]: 18.2-56.1) and those with the "non-GG" genotype had a mean OS time of 16.1 months (95% CI: 13.1-19.2). Kaplan-Meier analysis showed that the "GG" genotype had a significantly better OS compared to the "non-GG" genotype (p = 0.005). However, there was no significant difference between the "GG" and "non-GG" genotypes in PFS (p = 0.172). The baseline characteristics between patients with the "GG" and "non-GG" genotypes were compared, and no significant difference was found. Univariate followed by multivariate Cox proportional hazard models demonstrated the GALNT14 "GG" genotype, negative resection margin, and locoregional disease as independent predictors for favorable OS (p = 0.003, p = 0.037, p = 0.021, respectively). Sensitivity analysis was performed in each subgroup to examine the relationship of GALNT14 with different clinicopathological variables and no heterogeneity was found. The GALNT14 "GG" genotype is associated with favorable survival outcome, especially OS, in patients with resected PDA and could serve as a prognostic marker.

Dataset Information

CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma.

Publications

CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma.

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