Purpose Locally advanced pancreatic cancer (LAPC) treatment has varying practice patterns with poor outcomes. We investigated treatment using single-agent chemotherapy and multiagent chemotherapy (MAC) with or without radiation therapy (RT) at high-volume facilities (HVFs) and academic centers (ACs). Methods and Materials The National Cancer Database was used to obtain data on 10,139 patients with LAPC. HVF was defined as the top 5% of facilities per number of patients treated at each facility. Univariate and multivariable (MVA) analysis Cox regressions were performed to identify the impact of HVF, AC, MAC, and RT on overall survival (OS). Results The median age of patients was 66 years (range, 22-90); 50.1% were male and 49.9% female. Of the patients, 46.1% received MAC, 53.8% received single-agent chemotherapy, 45.7% received RT, 54.3% did not receive RT, and 5% underwent surgical resection. The median follow-up was 48.8 months. On MVA, treatment at HVFs and ACs remained significantly associated with improved OS, with a hazard ratio (HR) of 0.84 ( P < .001) and 0.94 ( P = .004), respectively. The median OS for HVF treatment compared with low-volume facilities was 14.3 versus 11.2 months, respectively ( P < .001). The median OS for AC treatment versus non-AC was 12.1 versus 10.8 months, respectively ( P < .001). Additionally, on MVA, receipt of RT and MAC remained significantly associated with improved OS (HR: 0.76; P < .001; and HR: 0.73; P < .001, respectively). MVA for receipt of surgery showed that MAC is a significant predictor for receiving surgery (odds ratio: 1.29; P = .009). Conclusions Our results build on a growing literature supporting RT and MAC in treating LAPC. Additionally, we believe that—in the absence of prospective data—this makes a strong case for considering MAC with RT at ACs and HVFs for treating LAPC.

Background and purpose: Adaptive radiation planning for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment, while traditional response evaluation criteria inefficiently characterize tumors with complex morphological features or intrinsically low metabolism. To better assess treatment response of PA, we quantify and compare regional morphological and metabolic features of the 3D pre-and post-radiation therapy (RT) tumor models. Materials and methods: Thirty-one PA patients with pre and post-RT Positron emission tomography/computed tomography (PET/CT) scans were evaluated. 3D meshes of pre-and post-RT tumors were generated and registered to establish vertex-wise correspondence. To assess tumor response, Mahalanobis distances (M dist | Fusion) between pre-and post-RT tumor surfaces with anatomic and metabolic fused vectors were calculated for each patient. M dist | Fusion was evaluated by overall survival (OS) prediction and survival risk classification. As a comparison, the same analyses were conducted on traditional imaging/physiological predictors, and distances measurements based on metabolic and morphological features only. Results: Among all the imaging/physiological parameters, M dist | Fusion was shown to be the best predictor of OS (HR = 0.52, p = 0.008), while other parameters failed to reach significance. Moreover, M dist | Fusion outperformed traditional morphologic and metabolic measurements in patient risk stratification, either alone (HR = 11.51, p < 0.001) or combined with age (HR = 9.04, p < 0.001). Conclusions: We introduced a PET/CT-based novel morphologic and metabolic pipeline for response evaluation in locally advanced PA. The fused M dist | Fusion outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction and risk stratification. The novel fusion model may serve as a new imagingmarker to more accurately characterize the heterogeneous tumor RT response.

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311 Background: Adaptive radiation therapy for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment. Traditional RECIST criteria poorly characterize tumors with complex morphological features, while PET imaging inefficiently detects tumors with intrinsically low standardized uptake value (SUV). Here, we performed regional comparisons of 3D intact PA surfaces pre and post chemoradiotherapy (CRT) utilizing surface measurements containing both morphological and metabolic features to better assess response. Methods: Twenty-one locally advanced PA patients with pre- and 6-8 week post-CRT 18F FDG-PET/CT scans were evaluated. Boundaries of initial and post-CRT tumors were manually defined on respective CT images. On each of the tumors, 3D meshes were generated, followed by surface based registration to achieve vertex-wise correspondence. For each surface vertex, a multivariate vector was formed from two components: anatomic (deformation tensors resulted from surface registration), and metabolic (regional SUV obtained from radius to surface projections). To assess tumor response, paired mahabanobis distance (Mdist) between pre- and post-CRT tumor surfaces with previously formed multivariate vectors were calculated for each patient. Mdist was evaluated using Cox analysis correlated with overall survival (OS) and compared with measurements based on serum CA19-9, volume, SUVmax and SUVmean. Results: Among all the tested parameters, Mdist is the best predictor of OS, with a hazard ratio of 0.437 (p = 0.036). Post-CRT versus pre-CRT ratios based on volume and SUVmax both reached borderline significance (p = 0.0769 and 0.0799, respectively), while CA19-9 and SUVmean failed in predicting OS in our small cohort of patients. Conclusions: We introduced a PET/CT-based novel morphologic and metabolic pipeline for post-CRT response evaluation in locally advanced PA. The fused Mdist outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction. The presented fused model may serve as a new biomarker to better characterize the heterogeneity of tumor response to CRT and a predictive marker for surgical resection.

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