A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present

A systematic review finds underreporting of ethics approval, informed consent, and incentives in clinical trials

Objectives: In this study, we aim to review researchers’ reporting practices of the ethics statement, financial incentives, and local ethical committees’ profile in their clinical trials. Study Design and Setting: A systematic search was done through top-ranked 50 medical journals (Scimago Ranking) to retrieve 2,000 latest publications. Only primary clinical trials were included with no restriction to language or participants. Results: Among the 927 included trials, 14 trials (1.5%) did not report an ethical statement and two-third (63%) did not completely report the investigated components (Institutional Review eBoard approval, Helsinki Declaration, and informed consent). Moreover, 21 trials (2.26%) reported motivational incentives with the method and amount of payment for participants. Of them, 15 trials offered monetary incentives to participants in different forms. In the remaining six trials, the incentives were mainly medical benefits. Only one trial reported the profile or quality of local Institutional Review Board. Conclusion: A potential gap in the reporting practices of ethics statement and financial incentives was addressed in this review. Authors are urged to fully report all ethical components related to their study, including incentives and compensations plan. Medical journals are also recommended to implement further publication requirements concerning ethics reporting.Journal of Clinical Epidemiology, 91, pp.80-86; 201