Background: Surgical treatment of adult degenerative scoliosis (DS) always remains a challenge and often necessitates complex multilevel surgery via traditional open approaches. However, the severity of the procedure, in association with the fact that many of these patients are at an advanced age with several comorbidities, results in high rate of complications. Therefore, during the last decade, minimally invasive procedures have gained a place in the treatment of this pathology. Our aim is to determine the safety and efficacy of extra lateral lumbar interbody fusion (XLIF) with or without supplemented instrumentation in the treatment of DS.Methods: In a retrospective case series study, we reviewed the files of patients who underwent XLIF in our Hospital between 2008 and 2017. We recorded the patients' demographic characteristics, clinical parameters such as back-pain [visual analogue scale (VAS)] and back-related disability [Oswestry Disability Index (ODI)], as well as radiological parameters including the Cobb angle. Comparison of the before and after results were performed with the t-test and Chi-square test, where appropriate.Results: Twelve patients fulfilled the eligibility criteria of our study. All patients were female, with a mean age of 64.5 years (SD =7.8 years) and 28 months (SD =13 months) follow-up. The XLIF decreased the pain intensity by 4.66 cm (SD =1.23 cm), and improved the back-related disability by 26% (SD =8.35%) in the ODI scale at the 6-month follow-up. Similarly, scoliosis improved by an average of 11.5° (SD =7°) and lordosis changed by an average of 13.5° (SD =10.86°). All changes were statistically significant. There were three complications, two patients presented meralgia paresthetica, which resolved spontaneously in 3 months, and in one patient occurred an intraoperative bowel perforation treated with bowel anastomosis.Conclusions: XLIF is a feasible and efficient alternative in the treatment of DS. It can be the treatment of choice in elderly patients in whom comorbidities increase the perioperative risk of complications.

Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI‐based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. Level of Evidence 5 Technical Efficacy Stage 2

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The available literature data demonstrated that there is no association of collagen type-(2a) and intracranial aneurysms, through EX28 G>C (rs42524) polymorphism according to the model-fitting process and the model-free approach. Regarding the INT46 T>G (rs2621215) polymorphisms, the latter models indicated that there could be a protective effect of the G-allele against the development of intracranial aneurysms. However, the majority of studies are from East Asia, therefore the results are applicable primarily to that patient population.