Metabolite selection for machine learning in childhood brain tumour classification

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N-acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Clinical characteristics and evolution of hematological patients and COVID-19 in Argentina: a report from the Argentine Society of Hematology

Individuals with malignancies and COVID-19 have a lower survival compared with the general population. However, the information about the impact of COVID-19 on the whole hematological population is scarce. We aimed to describe the 30th day overall survival (OS) after COVID-19 infection in patients with a hematological disease in Argentina. A completely anonymous survey from the Argentine Society of Hematology was delivered to all the hematologists in Argentina; it started in April 2020. A cut-off to analyze the data was performed in December 2020 and, finally, 419 patients were reported and suitable for the analysis (average age: 58 years, 90% with malignant diseases). After the COVID-19 diagnosis, the 30-day OS for the whole population was 80.2%. From the entire group (419), 101 (24.1%) individuals required intensive care unit admission, where the 30-day OS was 46.6%. Among allogeneic stem cell transplant recipients, the 30-day OS was 70.3%. Factors associated with a low OS were two or more comorbidities, an active hematological disease and history of chemotherapy. In individuals with the three factors, the 30-day OS was 49.6% while the 30-day OS in those without those factors was 100%. Patients with hematological diseases have a higher mortality than the general population. This group represents a challenge and requires careful decision-making of the treatment in order not to compromise the chances of cure.