Clinical outcomes of posterior fossa arteriovenous malformations: a single center experience.

BACKGROUND: Posterior fossa arterio-venous malformations (pfAVMs) are challenging lesions due to the anatomical particularities of the posterior fossa, and the high incidence of hemorrhagic presentation. The two most important goals when treating AVMs are preserving neurological function and preventing rupture, or a second hemorrhage. The aim of this study was to analyze the clinical and imaging features of pfAVMs to identify the factors that influence the prognosis of these patients. METHODS: We conducted a single-center retrospective observational study that included patients treated at our institution with pfAVMs between January 1997 and December 2021. RESULTS: A total of 48 patients were included. A good modified Rankin score (mRS) was observed in 33 cases (69%) at presentation. Thirty-four patients (71%) presented with a ruptured AVM. Out of these, 19 patients (40%) had intraventricular hemorrhage. Microsurgical resection was performed in 33 cases (69%), while in the other cases, the patients opted for conservative management (7 cases, 15%), stereotactic radiosurgery (SRS) (6 cases, 12%), or endovascular treatment (2 cases, 4%). Patients ≤ 30 years old were more prone to hemorrhagic presentation (OR: 5.23; 95% CI: 1.42-17.19; p = 0.024) and this remained an independent risk factor for rupture after multivariate analysis as well (OR: 4.81; 95% CI: 1.07-21.53; p = 0.040). Following multivariate analysis, the only factor independently associated with poor prognosis in the surgically treated subgroup was a poor clinical status (mRS 3-5) at admission (OR: 96.14; 95% CI: 5.15-1793.9; p = 0.002). CONCLUSIONS: Management of posterior fossa AVMs is challenging, and patients who present with ruptured AVMs often have a poor clinical status at admission leading to a poor prognosis. Therefore, proper and timely management of these patients is essential.

Grading of glioma tumors using digital holographic microscopy.

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists' experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.