Purpose: The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET). Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n ¼ 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was used for survival analysis. Results: An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC ¼ 0.907; validation set: AUC ¼ 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P ¼ 0.002). Conclusions: The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.

The aim of the present study was to determine the most meaningful preoperative prognostic factor of cancer-related death in ovarian cancer patients by comparing potentially prognostic systemic inflammatory response (SIR) markers. The levels of fibrinogen, albumin, C-reactive protein (CRP), and serum cancer antigen-125 (CA-125) and the neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) were evaluated in 190 ovarian cancer patients to identify predictors of overall survival (OS) and progression-free survival (PFS) using univariate and multivariate analyses. Patients with a PLR >203 had a shorter PFS and OS than the patients in PLR ≤203 group (11 vs. 24 months and 28 vs. 64 months). Univariate analyses revealed that tumor stage, postoperative residual tumor mass, ascites, and the levels of all SIR markers were associated with PFS and OS. Multivariate analysis revealed that PLR was independently associated with PFS (hazard ratio [HR] 1.852, 95% confidence interval [CI] 1.271-2.697, P = 0.001) and OS (HR 2.158, 95%CI 1.468-3.171, P< 0.001), as well as tumor stage and postoperative residual tumor mass. In contrast, fibrinogen remained significant only for PFS (HR 1.724, 95%CI 1.197-2.482, P = 0.003). Patients with a PLR >203 were more prone to have advanced tumor stage (P = 0.002), postoperative residual tumor mass >2 cm (P = 0.032), malignant ascites (P < 0.001), and all the other elevated SIR markers (P < 0.001). Preoperative PLR is superior to other SIR markers (CA-125, NLR, fibrinogen, CRP, and albumin) as a predictor of survival in ovarian cancer patients.

Our results demonstrated that TBL1XR1 induced lymphangiogenesis and lymphatic metastasis in ESCC via upregulation of VEGF-C, and may represent a novel prognostic biomarker and therapeutic target for patients with ESCC.