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PURPOSE The therapeutic management of pancreatic neuroendocrine tumors (PanNETs) is based on pathological tumor grade assessment. A noninvasive imaging method to grade tumors would facilitate treatment selection. This study evaluated the ability of quantitative image analysis derived from computed tomography (CT) images to predict PanNET grade. METHODS Institutional database was queried for resected PanNET (2000-2017) with a preoperative arterial phase CT scan. Radiomic features were extracted from the primary tumor on the CT scan using quantitative image analysis, and qualitative radiographic descriptors were assessed by two radiologists. Significant features were identified by univariable analysis and used to build multivariable models to predict PanNET grade. RESULTS Overall, 150 patients were included. The performance of models based on qualitative radiographic descriptors varied between the two radiologists (reader 1: sensitivity, 33%; specificity, 66%; negative predictive value [NPV], 63%; and positive predictive value [PPV], 37%; reader 2: sensitivity, 45%; specificity, 70%; NPV, 72%; and PPV, 47%). The model based on radiomics had a better performance predicting the tumor grade with a sensitivity of 54%, a specificity of 80%, an NPV of 81%, and a PPV of 54%. The inclusion of radiomics in the radiographic descriptor models improved both the radiologists' performance. CONCLUSION CT quantitative image analysis of PanNETs helps predict tumor grade from routinely acquired scans and should be investigated in future prospective studies.

Liver metastasis (LM) after pancreatic ductal adenocarcinoma (PDAC) resection is common but difficult to predict and has grave prognosis. We combined preoperative clinicopathological variables and quantitative analysis of computed tomography (CT) imaging to predict early LM.We retrospectively evaluated patients with PDAC submitted to resection between 2005 and 2014 and identified clinicopathological variables associated with early LM. We performed liver radiomic analysis on preoperative contrast-enhanced CT scans and developed a logistic regression classifier to predict early LM (6 months).In 688 resected PDAC patients, there were 516 recurrences (75%). The cumulative incidence of LM at 5 years was 41%, and patients who developed LM first (n = 194) had the lowest 1-year overall survival (OS) (34%), compared with 322 patients who developed extrahepatic recurrence first (61%). Independent predictors of time to LM included poor tumor differentiation (hazard ratio (HR) = 2.30; P0.001), large tumor size (HR = 1.17 per 2-cm increase; P = 0.048), lymphovascular invasion (HR = 1.50; P = 0.015), and liver Fibrosis-4 score (HR = 0.89 per 1-unit increase; P = 0.029) on multivariate analysis. A model using radiomic variables that reflect hepatic parenchymal heterogeneity identified patients at risk for early LM with an area under the receiver operating characteristic curve (AUC) of 0.71; the performance of the model was improved by incorporating preoperative clinicopathological variables (tumor size and differentiation status; AUC = 0.74, negative predictive value (NPV) = 0.86).We confirm the adverse survival impact of early LM after resection of PDAC. We further show that a model using radiomic data from preoperative imaging combined with tumor-related variables has great potential for identifying patients at high risk for LM and may help guide treatment selection.

This study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans. In this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs. Among pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively. Variations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions. • For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC

Background: Prognostic and/or predictive biomarkers for patients with pancreatic adenocarcinoma are limited. Radiomics is a non-invasive method to quantitatively analyze tumors with imaging. Previous data have demonstrated that CT radiomics is predictive of overall survival in PDAC. Based on evidence that enhanced intratumoral adaptive immunity is associated with improved survival in PDAC, we sought to build an immune infiltration prediction model with CT radiomics. Methods: A tissue microarray (TMA) was constructed with tumor arranged in triplicate from short-term and long-term survivors of PDAC who underwent resection between 2005 and 2010. Multiplexed immunohistochemistry (IHC) was performed for intratumoral mature dendritic cells (DC-LAMP+), regulatory T cells (CD3+FoxP3+), CD8+ T cells, (CD3+CD8+), activated cytolytic CD8+ T cells (CD3+CD8+GrB+), and macrophages (CD68+). The pancreatic tumor in the portal venous phase on preoperative CT was manually delineated, and 255 radiomic features were extracted and analyzed for significance using univariate linear regression. A multivariate linear regression analysis was then performed to correlate radiomic features with immune infiltrate for each of the immune phenotypes. Results: There were 22 patients included in the analysis, 7 of whom were short-term survivors (median survival of 9 months) and 15 were long-term survivors (median survival of 74 months). Feature selection resulted in 17 significant radiomic features for mature dendritic cells, 7 for regulatory T cells, 11 for CD8+ T cells, 16 for cytolytic CD8+ T cells and 12 features for macrophages. Five regression models were then constructed using the significant radiomic features to predict intratumoral immune infiltrate. There was a strong association between radiomic features and mature dendritic cells (R2=0.83, 95% CI 0.761-0.906, p Conclusion: A strong association was found between imaging phenotypes and both dendritic cells and activated cytolytic T cells, demonstrating that non-invasive imaging techniques may help identify tumors with active adaptive immunity. Further analysis is underway in a larger cohort of patients to validate these findings. Citation Format: Caitlin A. McIntyre, Jayasree Chakraborty, Maura Koszalka, Jared Bassmann, Yuting Chou, Mithat Gonen, Peter J. Allen, T. Peter Kingham, Michael I. D'Angelica, Jeffrey A. Drebin, William R. Jarnagin, Richard K. Do, Vinod P. Balachandran, Amber L. Simpson. The use of CT radiomics to predict immune infiltrate in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2444.