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.

Background/Aims: Osteosarcoma (OS) is the most common primary malignant bone tumor tumorigenesis and progression are still poorly understood. Circular RNAs (circRNAs) have been This study aims to investigate the global changes in the expression pattern of circRNAs in osteosarcoma and provide a comprehensive understanding of differentially expressed circRNAs. Methods: Microarray based circRNA expression was determined in osteosarcoma cell lines and mRNA interaction network was predicted using bioinformatics. Gene Ontology analysis and 4 predict the functions of differentially expressed circRNAs. Results: We revealed a number of role of circRNAs in OS. Among these differentially expressed circRNAs, hsa_circRNA_103801 was up-regulated in both osteosarcoma cell lines and tissues, while hsa_circRNA_104980 was down-regulated. The most likely potential target miRNAs for hsa_circRNA_103801 include hsamiR-370-3p, hsa-miR-338-3p and hsa-miR-877-3p, while the most potential target miRNAs of hsa_circRNA_104980 consist of hsa-miR-1298-3p and hsa-miR-660-3p. Functional analysis and angiogenesis pathway, the Rap1 signaling pathway and the PI3K-Akt signaling pathway, while hsa_circRNA_104980 was related to some pathways such as the tight junction pathway.

Purpose : Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. Methods and Materials : A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility. Results : The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), P <0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), P =0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram. Conclusion : The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.

Background The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). Methods We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. Results The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. Conclusions Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.

Osteosarcoma is the most common primary bone tumor in children and adolescents. Many patients with osteosarcoma always develop drug resistance to current chemotherapy regimens, which induces a poor prognosis. And cancer stem cells (CSCs) have been reported to possess the properties to self-renew and maintain the phenotype of tumor, which may lead to clinical treatment failure. Thus, it is an urgent task to develop several potentially useful therapeutic agents, which could target CSCs in osteosarcoma. This study aims to clarify the in vitro and in vivo anti-osteosarcoma effects of dioscin, the primary component derived from Discorea nipponica Makino, and its molecular mechanism of action. In this study, all the ten human osteosarcoma cell lines were sensitive to dioscin treatment in a dose- and time-dependent manner. Dioscin inhibits proliferation and induces cell cycle arrest as well as apoptotic cell death in osteosarcoma cells. More importantly, oral administration of dioscin (60 mg/kg) showed significant therapeutic effect on osteosarcoma growth without obvious side effects in vivo. In addition, dioscin possesses the ability to suppress stem-cell-like phenotype of osteosarcoma cells. Mechanistically, dioscin inhibits osteosarcoma stem-cell-like properties and tumor growth through repression of Akt/GSK3/β-catenin pathway. Moreover, β-catenin expression in osteosarcoma patients was associated with clinical prognosis. Conclusively, the present study provides comprehensive evidence for the inhibition of dioscin on osteosarcoma stem-cell-like properties and tumor growth through repression of Akt/GSK3/β-catenin pathway, which suggests dioscin as a promising therapeutic regimen. And β-catenin may be a potential therapeutic target as well as a significant prognostic marker for osteosarcoma patients in clinic.

Curcumin (CUR), a promising naturally occurring dietary compound, is commonly recognized as the potential anti-inflammatory agent. While the application of CUR was hampered by its low stability and poor systemic bioavailability, it has been suggested that the biological activities of CUR are intimately related to its metabolites. In the current investigation, we aimed to comparatively explore the anti-inflammatory effects of tetrahydrocurcumin (THC), octahydrocurcumin (OHC), and CUR, and to elucidate the underlying action mechanisms on experimental mice models of acute inflammation, i.e., xylene-induced ear edema, acetic acid-induced vascular permeability, and carrageenan-induced paw edema. The results showed that THC and OHC exerted significant and dose-dependent inhibitions on the formation of ear edema induced by xylene and paw edema provoked by carrageenan and inhibited the Evans blue dye leakage in peritoneal cavity elicited by acetic acid. Moreover, THC and OHC treatments were more effective than CUR in selectively inhibiting the expression of cyclooxygenase 2 (COX-2) and suppressing nuclear factor-κB (NF-κB) pathways via transforming growth factor β activated kinase-1 (TAK1) inactivation in the carrageenan-induced mouse paw edema model.

We used length frequencies of captured walleyes Sander vitreus to indirectly estimate and compare selectivity between two experimental gill‐net configurations used to sample fish in Lake Erie: (1) a multifilament configuration currently used by the Ohio Department of Natural Resources (ODNR) with stretched‐measure mesh sizes ranging from 51 to 127 mm and a constant filament diameter (0.37 mm); and (2) a monofilament configuration with mesh sizes ranging from 38 to 178 mm and varying filament diameter (range = 0.20–0.33 mm). Paired sampling with the two configurations revealed that the catch of walleyes smaller than 250 mm and larger than 600 mm was greater in the monofilament configuration than in the multifilament configuration, but the catch of 250–600‐mm fish was greater in the multifilament configuration. Binormal selectivity functions yielded the best fit to observed walleye catches for both gill‐net configurations based on model deviances. Incorporation of deviation terms in the binormal selectivity functions (i.e., to relax the assumption of geometric similarity) further improved the fit to observed catches. The final fitted selectivity functions produced results similar to those from the length‐based catch comparisons: the monofilament configuration had greater selectivity for small and large walleyes and the multifilament configuration had greater selectivity for mid‐sized walleyes. Computer simulations that incorporated the fitted binormal selectivity functions indicated that both nets were likely to result in some bias in age composition estimates and that the degree of bias would ultimately be determined by the underlying condition, mortality rate, and growth rate of the Lake Erie walleye population. Before the ODNR switches its survey gear, additional comparisons of the different gill‐net configurations, such as fishing the net pairs across a greater range of depths and at more locations in the lake, should be conducted to maintain congruence in the fishery‐independent survey time series. Received January 5, 2011; accepted May 13, 2011

Models of entire managed systems, known as operating models or management strategy evaluation (MSE) models, have been developed in recent years to more fully account for uncertainty in multiple steps of fishery management. Here we describe an operating model of sea lamprey management in the Great Lakes and use the model to compare alternative management strategies for sea lamprey control in Lake Michigan. Control of sea lampreys is mainly achieved through the application of chemical lampricides that target stream-dwelling larvae before they become parasites. The operating model simulated uncertainty due to process variation in larval population dynamics, the accuracy of population assessments used to direct selection of areas to be chemically treated, and the effectiveness of these treatments. We used the operating model to compare the performance of stream selection strategies that either rely on assessments to direct chemical treatments or eliminate the assessment process altogether by relying on prior but uncertain knowledge of stream-level sea lamprey growth rates to specify a fixed schedule for chemical treatments. The fixed schedule strategy led to a modest improvement in expected suppression of parasitic sea lamprey abundance over the assessment-based strategy so long as assessment cost savings were allocated to chemical treatment when assessment was not used to select streams for treatment. We also evaluated the sensitivity of the assessment-based strategy to differing but plausible levels of assessment uncertainty. A moderate reduction in assessment uncertainty led to a large increase in suppression of parasitic sea lamprey abundance for the assessment-based selection strategy, emphasizing the importance of both accurately measuring and reducing assessment uncertainty.