Pituitary Adenomas: Classification, Clinical Evaluation and Management

Pituitary adenomas are one of the most common brain tumors. They represent approximately 18% of all intracranial, and around 95% of sellar neoplasms. In recent years, our understanding of the pathophysiology and the behavior of these lesions has led to better control and higher curative rates. The treatment decision is largely dependent on type of the adenoma, clinical presentation, and the size of the lesion. In addition, incidental pituitary lesions add uncertainty in the decision-making process, especially for pituitary adenomas that can be medically managed. When surgery is indicated, the endoscopic endonasal transsphenoidal approach is the technique of choice, but open standard craniotomy approaches can also be the option in selected cases. The following chapter will review the classification, clinical presentation, pathophysiology, diagnostic work-up, selection of surgical approach, and treatment complications in pituitary adenomas

Glioblastoma in Elderly Population

Glioblastoma (GBM) is the third most common primary intracranial tumor and the commonest primary malignant brain tumor in adults. The peak incidence is between 65 and 84 years old. The incidence of GBM increases starkly with age—from 1.3/100,000 between the ages of 35–44 to 15.3/100,000 between the ages of 75–84 years. Elderly patients with GBM have increased comorbidities, lower functional status, aggressive tumor biology, and an overall worse outcome as compared with their younger counterparts. Age is an independent and powerful prognosticator of GBM outcomes, even if the performance status is controlled. Elderly patients with GBM represent a vulnerable heterogeneous cohort. Surgical resection in elderly patients offers a better outcome and improved quality of life as compared with biopsy alone and nowadays can be safely tolerated by elderly patients in specialized centers. The standard of care treatment of glioblastoma based on the Stupp’s protocol excluded patients over the age of 70. Thus, the standard of care treatment in elderly patients with GBM remains controversial. Selected elderly patients with excellent performance status may be treated with Stupp’s protocol. Elderly patients with lower functional status may be treated with a hypofractionated treatment regimen with concomitant and adjuvant temozolomide. Frail patients with MGMT methylated tumor can be treated with temozolomide monotherapy alone. It is also not unreasonable to treat elderly frail patients with MGMT unmethylated GBM with hypofractionated RT alone. Thus, treatment of elderly patients with GBM needs a multidisciplinary approach based on the extent of the tumor, MGMT methylation status, performance status, and even the social situation unique to the elderly patient. This chapter seeks to bring a comprehensive and updated review on the treatment of glioblastoma in the elderly population

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Stereotactic biopsies of brain lesions Biópsia estereotáxica de lesões encefálicas

OBJECTIVE: In the majority of cases, the correct treatment of brain lesions is possible only when the histopathological diagnosis is made. Several deep-seated lesions near eloquent areas are not safely approached by the classical neurosurgical procedures. These patients can get benefit by a minimally invasive procedure. METHOD: We present a series of 176 consecutive patients submitted to stereotactic biopsies due to a great variety of brain lesions. RESULTS: Histological diagnosis found in this series: glioma in 40.1% of the patients, other neoplasms in 12.2% and infectious or inflammatory diseases in 29.1 %. The result was inconclusive in 5.2% of the procedures. One patient died (0.6%) and two (1.2%) presented operative complications. The criteria, advantages and risks of the stereotactic biopsies are discussed. CONCLUSION: The efficacy of the method is adequate and morbid-mortality rates were low.OBJETIVO: O diagnóstico anatomopatológico das lesões encefálicas é muitas vezes necessário para a instituição do tratamento adequado. Entretanto, muitas lesões localizadas profundamente no encéfalo ou em centros nervosos de grande importância funcional não podem ser acessadas sem riscos, com a aplicação dos procedimentos neurocirúrgicos habituais. MÉTODO: Apresentamos uma série de 176 doentes submetidos a biópsias estereotáxicas de lesões encefálicas. RESULTADOS: Em 40,1% dos casos, o diagnóstico foi de glioma, em 12,2% de outras neoplasias e em 29,1%, de doenças infecciosas ou inflamatórias. O resultado foi inconclusivo em 5,2% dos doentes. Um (0,6%) doente faleceu e dois (1,2%) apresentaram graves complicações operatórias. Os critérios de seleção, as vantagens e os riscos da biópsia estereotáxica são discutidos. CONCLUSÃO: A eficácia do método é boa e a morbimortalidade das biópsias estereotáxicas é baixa

Neurogenic neuroprotection: clinical perspectives

Neurogenic neuroprotection is a promising approach for treating patients with ischemic brain lesions. In rats, stimulation of the deep brain nuclei has been shown to reduce the volume of focal infarction. In this context, protection of neural tissue can be a rapid intervention that has a relatively long-lasting effect, making fastigial nucleus stimulation (FNS) a potentially valuable method for clinical application. Although the mechanisms of neuroprotection induced by FNS remain partially unclear, important data have been presented in the last two decades. A 1-h electrical FNS reduced, by 59%, infarctions triggered by permanent occlusion of the middle cerebral artery in Fisher rats. The acute effect of electrical FNS is likely mediated by a prolonged opening of potassium channels, and the sustained effect appears to be linked to inhibition of the apoptotic cascade. A better understanding of the neuronal circuitry underlying neurogenic neuroprotection may contribute to improving neurological outcomes in ischemic brain insults

3D Preoperative Planning in the ER with OsiriX®: When There is No Time for Neuronavigation

The evaluation of patients in the emergency room department (ER) through more accurate imaging methods such as computed tomography (CT) has revolutionized their assistance in the early 80s. However, despite technical improvements seen during the last decade, surgical planning in the ER has not followed the development of image acquisition methods. The authors present their experience with DICOM image processing as a navigation method in the ER. The authors present 18 patients treated in the Emergency Department of the Hospital das Clínicas of the University of Sao Paulo. All patients were submitted to volumetric CT. We present patients with epidural hematomas, acute/subacute subdural hematomas and contusional hematomas. Using a specific program to analyze images in DICOM format (OsiriX®), the authors performed the appropriate surgical planning. The use of 3D surgical planning made it possible to perform procedures more accurately and less invasively, enabling better postoperative outcomes. All sorts of neurosurgical emergency pathologies can be treated appropriately with no waste of time. The three-dimensional processing of images in the preoperative evaluation is easy and possible even within the emergency care. It should be used as a tool to reduce the surgical trauma and it may dispense methods of navigation in many cases