Association of facility type with overall survival in patients with nonsurgically managed pancreatic cancer.

Aim: To investigate the association between receiving treatment at academic centers and overall survival in pancreatic ductal adenocarcinoma patients who do not receive definitive surgery of the pancreatic tumor. Methods: Using the National Cancer Database, patients who were diagnosed with pancreatic ductal adenocarcinoma between 2004 to 2016 were identified. Results: Of 262,209 patients, 101,003 (38.5%) received treatment at academic centers. In the multivariable Cox regression analysis, patients who received treatment at a nonacademic facility had significantly worse overall survival compared with patients who were treated at an academic center (hazard ratio: 1.279; 95% CI: 1.268-1.290; p = 0.001). Conclusion: Compared with treatment at academic centers, treatment at nonacademic centers was associated with significantly worse overall survival in patients with nonsurgically managed pancreatic ductal adenocarcinoma.

The aim of this study is to examine the association between receiving treatment at academic hospitals and overall survival in patients diagnosed with pancreatic cancer who do not receive definitive surgery of the pancreatic tumor. The authors used the National Cancer Database to identify patients who were diagnosed with pancreatic cancer between 2004 and 2016. The authors' study included 262,209 patients. The authors found that patients who received treatment at nonacademic hospitals were on average 28% more likely to die of any cause compared with patients who were treated at academic centers (hazard ratio: 1.279; 95% CI: 1.268­1.290; p < 0.001). Patients who received treatment at nonacademic hospitals were more likely to die of any cause compared with patients who received treatment at academic hospitals.

Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction.

Radiomic analysis has recently demonstrated versatile uses in improving diagnostic and prognostic prediction accuracy for lung cancer. However, since lung tumors are subject to substantial motion due to respiration, the stability of radiomic features over the respiratory cycle of the patient needs to be investigated to better evaluate the robustness of the inter-patient feature variability for clinical applications, and its impact in such applications needs to be assessed. A full panel of 841 radiomic features, including tumor intensity, shape, texture, and wavelet features, were extracted from individual phases of a four-dimensional (4D) computed tomography on 20 early-stage non-small-cell lung cancer (NSCLC) patients. The stability of each radiomic feature was assessed across different phase images of the same patient using the coefficient of variation (COV). The relationship between individual COVs and tumor motion magnitude was inspected. Population COVs, the mean COVs of all 20 patients, were used to evaluate feature motion stability and categorize the radiomic features into 4 different groups. The two extremes, the Very Small group (COV≤5%) and the Large group (COV>20%), each accounted for about a quarter of the features. Shape features were the most stable, with COV≤10% for all features. A clinical study was subsequently conducted using 140 early-stage NSCLC patients. Radiomic features were employed to predict the overall survival with a 500-round bootstrapping. Identical multiple regression model development process was applied, and the model performance was compared between models with and without a feature pre-selection step based on 4D COV to pre-exclude unstable features. Among the systematically tested cutoff values, feature pre-selection with 4D COV≤5% achieved the optimal model performance. The resulting 3-feature radiomic model significantly outperformed its counterpart with no 4D COV pre-selection, with P = 2.16x10-27 in the one-tailed t-test comparing the prediction performances of the two models.