Titlebook: Brain Tumors; A Pocket Guide Nimish A. Mohile,Alissa A. Thomas Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive l

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Machine Learning for Causal Inferenceeyes, leptomeninges and spinal cord. Histologic diagnosis can be made by brain biopsy, vitrectomy or cerebrospinal fluid evaluation but a clinical diagnosis must also rule out systemic lymphoma. The current evidence for therapy favors the use of high-dose methotrexate-based chemotherapy regimens fol

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What Is Applied Machine Learning?or itself. Proper management of brain edema can have a meaningful impact on a patient’s quality of life. The primary method of treating brain edema includes the use of corticosteroids but the clinical benefits of these drugs need to be carefully balanced with numerous acute, subacute and chronic sid

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What Is Applied Machine Learning?on or surgery. Clinical workup for patients with brain tumor presenting with cognitive changes includes evaluation of tumor and treatment-related causes as well as common conditions related to metabolic abnormalities and medication toxicities. In this chapter, we describe distinct presentations of c

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https://doi.org/10.1007/978-3-030-96756-7re complex multi-agent regimens and intravenous formulations. Understanding common and rare toxicities of chemotherapy is critical for optimal management of patients with brain tumors in order to ensure that they are being treated with appropriate doses and to maintain quality of life. In this chapt

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https://doi.org/10.1007/978-3-642-28661-2matic worsening of brain edema, skin toxicity, alopecia and ear and Eustachian tube dysfunction. Bone marrow toxicity due to irradiation of the skull is uncommon but there can be associated lymphopenia. Later effects of radiation include radiation necrosis that can be difficult to distinguish from t

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