Frontiers in oncology
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Frontiers in oncology · Jan 2019
Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.
Purpose: To investigate whether a combination of radiomics and automatic machine learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of primary breast cancer can non-invasively predict axillary sentinel lymph node (SLN) metastasis. Methods: 62 patients who received a DCE-MRI breast scan were enrolled. Tumor resection and sentinel lymph node (SLN) biopsy were performed within 1 week after the DCE-MRI examination. ⋯ In the validation set, with respect to the accuracy and MSE, the SVM demonstrated the highest performance, with an accuracy, AUC, sensitivity (for positive SLN), specificity (for positive SLN) and Mean Squared Error (MSE) of 0.85, 0.83, 0.71, 1, 0.26, respectively. Conclusions: We demonstrated the feasibility of combining artificial intelligence and radiomics from DCE-MRI of primary tumors to predict axillary SLN metastasis in breast cancer. This non-invasive approach could be very promising in application.
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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
Combination of Intravesical Bacille Calmette-Guérin and Chemotherapy vs. Bacille Calmette-Guérin Alone in Non-muscle Invasive Bladder Cancer: A Meta-Analysis.
Background: About 75% of newly diagnosed bladder cancer cases suffer from non-muscle invasive bladder cancer (NMIBC), which used to recur and progress despite transurethral resection of bladder tumor (TURBT). This meta-analysis was conducted to examine if combined application of intravesical bacille Calmette-Guérin (BCG) with chemotherapy is associated with better prognosis. Methods: Systematic searches of randomized controlled trials (RCTs) concerning NMIBC were performed in PubMed, EMbase, CENTRAL, CNKI, WanFang, VIP, CBM databases, and some specialized websites. ⋯ The rate of fever (RR = 0.50, 95%CI: 0.27-0.91, P = 0.02), irritative bladder symptoms (RR = 0.69, 95%CI: 0.52-0.90, P = 0.007) and hematuria (RR = 0.50, 95%CI: 0.28-0.89, P = 0.02) were significantly decreased in patients treated with combination therapy compared to those with BCG alone. There were no statistically significant differences between combination therapy and BCG alone in toxicity (RR = 0.69, 95%CI: 0.34-1.40, P = 0.30), gastrointestinal reaction (RR = 2.54, 95%CI: 0.61-10.60, P = 0.20) or cystitis (RR = 0.67, 95%CI: 0.29-1.54, P = 0.34). Conclusions: Combined application of intravesical BCG and chemotherapy appears to be an effective treatment for patients with intermediate- to high-risk NMIBC, but not for those with tumor in situ alone or recurrent bladder cancer.
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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.
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Frontiers in oncology · Jan 2019
Publication Landscape Analysis on Gliomas: How Much Has Been Done in the Past 25 Years?
Introduction: The body of glioma-related literature has grown significantly over the past 25 years. Despite this growth in the amount of published research, gliomas remain one of the most intransigent cancers. The purpose of this study was to analyze the landscape of glioma-related research over the past 25 years using machine learning and text analysis. ⋯ The current research landscape covers clinical, pre-clinical, biological, and technical aspects of glioblastoma; at present, researchers appear to be less concerned with glioblastoma's psychological effects or patients' end-of-life care. Conclusion: Publication of glioma-related research has expanded rapidly over the past 25 years. Common topics include the disease's molecular background, patients' survival, and treatment outcomes; more research needs to be done on the psychological aspects of glioblastoma and end-of-life care.