Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.

BACKGROUND: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. METHODS: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms "artificial intelligence", "machine learning", and "pituitary". RESULTS: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60-72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80-0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. CONCLUSION: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI's real-world utility and improve clinical practice.

Alpha 1-antichymotrypsin contributes to stem cell characteristics and enhances tumorigenicity of glioblastoma.

BACKGROUND: Glioblastomas (GBMs) are the main primary brain tumors in adults with almost 100% recurrence rate. Patients with lateral ventricle proximal GBMs (LV-GBMs) exhibit worse survival compared to distal locations for unknown reasons. One hypothesis is the proximity of these tumors to the cerebrospinal fluid (CSF) and its chemical cues that can regulate cellular phenotype. We therefore investigated the role of CSF on GBM gene expression and the role of a CSF-induced gene, SERPINA3, in GBM malignancy in vitro and in vivo. METHODS: We utilized human CSF and GBM brain tumor-initiating cells (BTICs). We determined the impact of SERPINA3 expression in glioma patients using The Cancer Genome Atlas (TCGA) database. SERPINA3 expression changes were evaluated at mRNA and protein levels. The effects of knockdown (KD) and overexpression (OE) of SERPINA3 on cell migration, viability and cell proliferation were evaluated. Stem cell characteristics on KD cells were evaluated by differentiation and colony formation experiments. Tumor growth was studied by intracranial and flank injections. RESULTS: GBM-CSF increased BTIC migration accompanied by upregulation of the SERPINA3 gene. In patient samples and TCGA data, we observed SERPINA3 to correlate directly with brain tumor grade and indirectly with GBM patient survival. SERPINA3 KD induced a decrease in cell proliferation, migration, invasion, and stem cell characteristics, while SERPINA3 OE increased cell migration. In vivo, SERPINA3 KD BTICs showed increased survival in a murine model. CONCLUSIONS: SERPINA3 plays a key role in GBM malignancy and its inhibition results in a better outcome using GBM preclinical models.

Long-term hydrocephalus alters the cytoarchitecture of the adult subventricular zone.

Hydrocephalus can develop secondarily to a disturbance in production, flow and/or absorption of cerebrospinal fluid. Experimental models of hydrocephalus, especially subacute and chronic hydrocephalus, are few and limited, and the effects of hydrocephalus on the subventricular zone are unclear. The aim of this study was to analyze the effects of long-term obstructive hydrocephalus on the subventricular zone, which is the neurogenic niche lining the lateral ventricles. We developed a new method to induce hydrocephalus by obstructing the aqueduct of Sylvius in the mouse brain, thus simulating aqueductal stenosis in humans. In 120-day-old rodents (n=18 per group), the degree of ventricular dilatation and cellular composition of the subventricular zone were studied by immunofluorescence and transmission electron microscopy. In adult patients (age>18years), the sizes of the subventricular zone, corpus callosum, and internal capsule were analyzed by magnetic resonance images obtained from patients with and without aqueductal stenosis (n=25 per group). Mice with 60-day hydrocephalus had a reduced number of Ki67+ and doublecortin+cells on immunofluorescence, as well as decreased number of neural progenitors and neuroblasts in the subventricular zone on electron microscopy analysis as compared to non-hydrocephalic mice. Remarkably, a number of extracellular matrix structures (fractones) contacting the ventricular lumen and blood vessels were also observed around the subventricular zone in mice with hydrocephalus. In humans, the widths of the subventricular zone, corpus callosum, and internal capsule in patients with aqueductal stenosis were significantly smaller than age and gender-matched patients without aqueductal stenosis. In summary, supratentorial hydrocephalus reduces the proliferation rate of neural progenitors and modifies the cytoarchitecture and extracellular matrix compounds of the subventricular zone. In humans, this similar process reduces the subventricular niche as well as the width of corpus callosum and internal capsule.