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An air-fluid level within a gastrointestinal stromal tumor (GIST) is unusual and indicates the presence of a fistula within the lumen of the GI tract. Until recently, the optimal management of such patients was not clear-cut. This retrospective study investigated the clinicopathological characteristics, surgical procedures, pre-and post-operative management, and prognosis of patients with GIST containing an air-fluid level. Data of GIST patients, spanning 5 years, including 17 GIST patients with air-fluid levels in the experimental group and 34 GIST patients without air-fluid levels in the control group, were retrieved from two hospitals in China. The clinicopathological characteristics, types of surgery, management, and clinical outcomes of GIST patients were compared between the two groups. GISTs containing air-fluid levels were significantly different from GISTs without air-fluid levels regarding tumor morphology, NIH risk category, invasion of adjacent organs, and necrosis or ulceration. Most GIST patients with air-fluid levels (14/17, 82.4%) received open surgery, significantly higher than the 20.6% in the control group. Targeted therapy with Imatinib mesylate (IM) was implemented in all GIST patients in the experimental group (17/17, 100%); markedly higher than those (3/34, 8.8%) in the control group. During follow-up, recurrence and death rates (5.9% and 5.9%) in the experimental group were higher than those (2.9% and 0%) in the control group. Open surgery is commonly performed in GIST patients with air-fluid levels who also require targeted therapy with IM. The Torricelli-Bernoulli sign could be a risk factor, adversely affecting the patient's prognosis. [ABSTRACT FROM AUTHOR]

Objectives: This study is to investigate if polymerase I and transcript release factor (PTRF) acts as a modulator in glioblastoma (GBM) chemoresistance. Methods: Multidrug resistant (MDR) GBM cell line U251AR was established by exposing the U251 cell line to imatinib. The 2D-DIGE and MALDI-TOF/TOF-MS were performed on U251 and U251AR cell lines to screen MDR-related proteins. The expression of PTRF was determined by Western blot and quantitative RT-PCR analyses. Results: When compared with the parental U251 cells, expression of 21 proteins was significantly altered in U251AR cells. Among the 21 differentially expressed proteins, the expression of PTRF was up-regulated by 2.14 folds in U251AR cells when compared with that in the parental U251 cells. Knockdown of PTRF in GBM cell lines significantly increased chemosensitivity of cells to various chemical drugs and decreased the expression levels of caveolin1, a major structural component of caveolae. Expression levels of PTRF and caveolin1 were significantly up-regulated in the relapsed GBM patients. The mRNA level of PTRF and caveolin1 showed a positive correlation in the same GBM specimens. Conclusions: Our results indicate that PTRF acts as a modulator in GBM chemoresistance. [ABSTRACT FROM AUTHOR]

Background: Machine learning algorithms used to classify cystic renal masses (CRMs) nave not been applied to unenhanced CT images, and their diagnostic accuracy had not been compared against radiologists. Method: This retrospective study aimed to develop radiomics models that discriminate between benign and malignant CRMs in a triple phase computed tomography (CT) protocol and compare the diagnostic accuracy of the radiomics approach with experienced radiologists. Predictive models were established using a training set and validation set of unenhanced and enhanced (arterial phase [AP] and venous phase [VP]) CT images of benign and malignant CRMs. The diagnostic capabilities of the models and experienced radiologists were compared using Receiver Operating Characteristic (ROC) curves. Results: On unenhanced, AP and VP CT images in the validation set, the AUC, specificity, sensitivity and accuracy for discriminating between benign and malignant CRMs were 90.0 (95%CI: 81–98%), 90.0%, 90.5% and 90.2%; 93.0% (95%CI: 86–99%), 86.7%, 95.2% and 88.3%; and 95.0% (95%CI: 90%-100%), 93.3%, 90.5% and 92.1%, respectively, for the radiomics models. Diagnostic accuracy of the radiomics models differed significantly on unenhanced images in the training set vs. each radiologist (p = 0.001 and 0.003) but not in the validation set (p = 0.230 and 0.590); differed significantly on AP images in the validation set vs. each radiologist (p = 0.007 and 0.007) but not in the training set (p = 0.663 and 0.663); and there were no differences on VP images in the training or validation sets vs. each radiologist (training set: p = 0.453 and 0.051, validation set: p = 0.236 and 0.786). Conclusions: Radiomics models may have clinical utility for discriminating between benign and malignant CRMs on unenhanced and enhanced CT images. The performance of the radiomics model on unenhanced CT images was similar to experienced radiologists, implying it has potential as a screening and diagnostic tool for CRMs. [ABSTRACT FROM AUTHOR]