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Objective: In rare cases, Lyme neuroborreliosis (LNB) can induce cerebral vasculitis leading to severe stenosis of the cerebral vasculature and consecutive ischemia. Therapy is based on anti-biotic treatment of the tick-borne disease, whereas interventional therapeutic options have not been assessed yet. Material and Methods: We report on a patient with LNB and concomitant stenoses and progressive and fatal vasculitis of the cerebral vessels despite all therapeutic efforts by the departments of neurology and interventional neuroradiology. In this context, we also conducted a literature review on endovascular treatment of LNB-associated cerebral ischemia. Results: A 52-year-old female presented with transient neglect and psychomotor slowdown (initial NIHSS = 0). MRI and serology led to the diagnosis of basal meningitis due to LNB with vasculitis of cerebral arteries. Despite immediate treatment with antibiotics and steroids, neurologic deterioration (NIHSS 8) led to an emergency angiography on day 2 after admission. Hemodynamically relevant stenoses of the MCA were treated via spasmolysis and PTA, leading to almost complete neurological recovery. Despite intensified medical treatment, the vasculitis progressed and could only be transiently ameliorated via repetitive spasmolysis. On day 19, she again presented with significant neurologic deterioration (NIHSS 9), and PTA and stenting of the nearly occluded MCA were performed with a patent vessel, initially without hemorrhagic complications. Despite all therapeutic efforts and preserved stent perfusion, vasculitis worsened and the concurrent occurrence of subdural hemorrhage led to the death of the patient. Conclusion: Neuroradiological interventions, i.e., spasmolysis, PTA, and, if necessary, stenting, can and should be considered in cases of LNB-induced vasculitis and stroke that are refractory to best medical treatment alone. Key point: Neuroradiological interventions can be considered in patients with vascular complications of Lyme neuroborreliosis as an additional extension of the primary drug therapy. [ABSTRACT FROM AUTHOR]

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future. [ABSTRACT FROM AUTHOR]