Bias, Biostatistics, and Prognostic Factors

The creation of the international thymic malignancies interest group as a model for rare diseases

Similar to other orphan diseases, little progress has been made in the past decades in thymic malignancies. A determination to make a difference, despite the challenges facing a rare disease, led to the formation of the International Thymic Malignancies Interest Group (ITMIG) in 2010. This organization has brought together the majority of those focused on the management of thymic malignancies and has built a foundation for scientific collaboration, including consistent use of terms, an international database, and multidisciplinary engagement of clinicians and researchers from around the world. ITMIG has embarked on the development of novel approaches to research particularly suited to a rare condition. ITMIG has gained substantial recognition for the rapid progress that has been made and serves as a model for the advancement of knowledge in a rare disease.

γ -H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer

Cancer is a leading cause of death worldwide and the prognostic evaluation of cancer patients is of great importance in medical care. The use of artificial neural networks in prediction problems is well established in human medical literature. The aim of the current study was to assess the prognostic value of a series of clinical and molecular variables with the addition of γ -H2AX-a new DNA damage response marker-for the prediction of prognosis in patients with early operable non-small cell lung cancer by comparing the γ -H2AX-based artificial network prediction model with the corresponding LR one. Two prognostic models of 96 patients with 27 input variables were constructed by using the parameter-increasing method in order to compare the predictive accuracy of neural network and logistic regression models. The quality of the models was evaluated by an independent validation data set of 11 patients. Neural networks outperformed logistic regression in predicting the patient's outcome according to the experimental results. To assess the importance of the two factors p53 and γ -H2AX, models without these two variables were also constructed. JR and accuracy of these models were lower than those of the models using all input variables, suggesting that these biological markers are very important for optimal performance of the models. This study indicates that neural networks may represent a potentially more useful decision support tool than conventional statistical methods for predicting the outcome of patients with non-small cell lung cancer and that some molecular markers, such as γ -H2AX, enhance their predictive ability.