Frontiers in oncology
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Frontiers in oncology · Jan 2019
ReviewUnderstanding the Mechanisms of Resistance to CAR T-Cell Therapy in Malignancies.
Taking advantage of the immune system to exert an antitumor effect is currently a novel approach in cancer therapy. Adoptive transfer of T cells engineered to express chimeric antigen receptors (CARs) targeting a desired antigen has shown extraordinary antitumor activity, especially in refractory and relapsed B-cell malignancies. ⋯ However, with the widespread use of CAR T-cell therapy, problems of resistance and relapse are starting to be considered. This review provides a comprehensive picture of the mechanisms of resistance to CAR T-cell therapy from three aspects, namely, CAR T-cell factors, tumor factors, and tumor microenvironment factors, offering insights for improving CAR T-cell therapy.
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Frontiers in oncology · Jan 2019
The Role of 5-ALA in Low-Grade Gliomas and the Influence of Antiepileptic Drugs on Intraoperative Fluorescence.
Objectives: Intraoperative tumor visualization with 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence is widely applied for improved resection of high-grade gliomas. However, visible fluorescence is present only in a minority of low-grade gliomas (LGGs) according to current literature. Nowadays, antiepileptic drugs (AEDs) are frequently administered to LGG patients prior to surgery. ⋯ Thus, visible fluorescence was significantly more common in patients without AEDs compared to patients with preoperative AED intake (OR = 0,15 (CI 95% 0.012-1.07), p = 0.046). Conclusions: Our study shows a markedly higher rate of visible fluorescence in a series of LGGs compared to current literature. According to our preliminary data, preoperative intake of AEDs seems to reduce the presence of visible fluorescence in such tumors and should thus be taken into account in the clinical setting.
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Frontiers in oncology · Jan 2019
Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.
Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. ⋯ Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.
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Frontiers in oncology · Jan 2019
The Impact of Formal Mentorship Programs on Mentorship Experience Among Radiation Oncology Residents From the Northeast.
Purpose: Strong mentorship has been shown to improve mentee productivity, clinical skills, medical knowledge, and career preparation. We conducted a survey to evaluate resident satisfaction with mentorship within their radiation oncology residency programs. Methods and Materials: In January 2019, 126 radiation oncology residents training at programs in the northeastern United States were asked to anonymously complete the validated Munich Evaluation of Mentoring Questionnaire (MEMeQ). ⋯ Overall, 38% of residents were either satisfied/very satisfied with their mentoring experience, while 49% of residents were unsatisfied/very unsatisfied. Conclusion: Residents participating in a formal mentorship program are significantly more likely to be satisfied with their mentoring experience than those who are not. Our results suggest that radiation oncology residency programs should strongly consider implementing formal mentorship programs.
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Frontiers in oncology · Jan 2019
Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach.
Objectives: To investigate the ability of radiomics features from MRI in differentiating anaplastic oligodendroglioma (AO) from atypical low-grade oligodendroglioma using machine-learning algorithms. Methods: A total number of 101 qualified patients (50 participants with AO and 51 with atypical low-grade oligodendroglioma) were enrolled in this retrospective, single-center study. Forty radiomics features of tumor images derived from six matrices were extracted from contrast-enhanced T1-weighted (T1C) images and fluid-attenuation inversion recovery (FLAIR) images. ⋯ For models based on T1C images, the combination of LASSO and RF classifier represented the highest AUC of 0.904 in the validation group. For models based on FLAIR images, the combination of GBDT and RF classifier showed the highest AUC of 0.861 in the validation group. Conclusion: Radiomics-based machine-learning approach could potentially serve as a feasible method in distinguishing AO from atypical low-grade oligodendroglioma.