SEOM-GEINO clinical guidelines for high-grade gliomas of adulthood (2022)

High-grade gliomas (HGG) are the most common primary brain malignancies and account for more than half of all malignant primary brain tumors. The new 2021 WHO classification divides adult HGG into four subtypes: grade 3 oligodendroglioma (1p/19 codeleted, IDH-mutant); grade 3 IDH-mutant astrocytoma; grade 4 IDH-mutant astrocytoma, and grade 4 IDH wild-type glioblastoma (GB). Radiotherapy (RT) and chemotherapy (CTX) are the current standard of care for patients with newly diagnosed HGG. Several clinically relevant molecular markers that assist in diagnosis and prognosis have recently been identified. The treatment for recurrent high-grade gliomas is not well defined and decision-making is usually based on prior strategies, as well as several clinical and radiological factors. Whereas the prognosis for GB is grim (5-year survival rate of 5-10%) outcomes for the other high-grade gliomas are typically better, depending on the molecular features of the tumor. The presence of neurological deficits and seizures can significantly impact quality of life

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts