Distinct MRI pattern of "pseudoresponse" in recurrent glioblastoma multiforme treated with regorafenib: Case report and literature review

: Antiangiogenic agents can induce a distinct MRI pattern in glioblastoma, characterized by a decrease in the contrast enhancement on T1-weighted images and a simultaneous hyperintensity on T2-weighted or fluid-attenuated inversion recovery images

Endoscopic endonasal approach for loco-regional recurrent clivus chordomas

Introduction. Role of surgery for loco-regional recurrences of clivus chordomas (CCs) is still debated. It has been proposed in selected cases with a curative or with palliative intent, eventually followed by radiation or chemo/radiation treatments. Only limited data on the endoscopic endonasal approach (EEA) are available. Research question. To assess the role of EEA for loco-regional recurrent CCs. Materials and Methods. All consecutive loco-regional recurrent CCs operated by EEA at our Institution from 1998 to 2021 were identified. The extension of tumor resection, symptoms control, overall survival (OS), and progression free survival (PFS) were assessed. Results. Series includes 54 patients (53.7% females, mean age 55± 14 years). Surgery was planned with a resective aim in 35 (64.8%) patients, while it was palliative in 19 (35.2%). Gross tumor removal was achieved in 24 cases (44.4%). Main complications consisted of 2 (3.7%) CSF leaks. Further local relapses were observed in 30 (55.5%) patients after 25± 24 months; 29 (53.7%) patients deceased after 34 ± 31 months. OS and PFS were lower in these cases than primary surgeries (p<0.001 and p<0.001), but cases undergone surgery with a resective aim had a significant better OS and PFS than for those treated for palliation (p<0.001). Determinants of recurrences were tumoral size (p=0.48) and previous radiotherapy (p=009). Discussion and Conclusions. EEA has proven to be effective for loco-regional recurrent CCs alleviating patients symptoms and preserving their quality of life with limited morbidities. However, because overall prognosis is poor, EEA should be reserved to selected recurrent cases

Role of endoscopic endonasal approach for craniopharyngiomas extending into the third ventricle in adults

Introduction. Recent advancements in endoscopic endonasal approach (EEA) have favored its adoption for craniopharyngiomas extended to 3rd ventricle (3VCPs). However, for lack of extensive series, its outcome, limits, and indications remain debated. Research question. To assess the EEA results of for 3VCPs and identify those factors determining the choice of this approach. Material and Methods. Records of patients with 3VCPs, consecutively operated through an EEA at our Institution were retrospectively analyzed. Demographic and clinico-radiological data, rate of tumor resection, complications and outcome at follow-up were collected. Results. Thirty-six patients (19 females, mean age: 51.1 ± 15.9 yrs) were included. Extended transplanum-transtuberculum approach was performed in all cases Radical resection was achieved in 33 patients (91.7%). At follow-up, visual deficits improved/normalized in 21 cases (58.3%), and 35 (97.2%) presented with panhypopituitarism and DI. Anatomical (displacement of the chiasm and hypothalamus), clinical (age and pre-operative visual and endocrinological function) and tumoral (consistency, presence of hydrocephalus) parameters resulted relevant in determining the choice of this approach. Discussion and Conclusion. EEA offers a valid and direct route for 3VCPs, which permits to safely manage these tumors. In our series, EEA was chosen for tubero-infundibular forms with chiasm displaces antero-superiorly, and preferred in younger patients, with visual disturbances, comprimesed endocrinological function and no hydrocephalus. It requires a specific training and should be reserved in dedicated centers. Because no single approach is ideal for every 3VCP, all surgical options should be considered as complementary and selected basing on clinical, anatomical and tumoral features of each case

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Attivazione corticale

Fin dalle sue origini, negli anni Novanta, la tecnica dell'attivazione funzionale cerebrale (functional Magnetic Resonance lmaging, fMRI) \ue8 stata molto usata non solo per la definizione incruenta di molte funzioni fisiologiche cerebrali, ma anche per le ampie possibilit\ue0 d'impiego nella pratica clinica. In questo capitolo saranno presi in considerazione i meccanismi neurofisiologici alla base dell'attivazione corticale, partendo dalla genesi del contrasto BOLD e dall'accoppiamento neurovascolare, fino ad arrivare alla risposta emodinamica. Saranno, quindi, descritte le motivazioni relative all'uso dei paradigmi, saranno illustrati alcuni paradigmi usati nella pratica clinica e verr\ue0 spiegato come viene acquisito ed elaborato un esame di attivazione funzionale corticale. Si esamineranno, poi, vantaggi e limiti della tecnica e le principali applicazioni cliniche. Infine, sar\ue0 brevemente esaminato un nuovo approccio funzionale della fMRI: lo studio del cosiddetto Default Mode Network (DMN)

Use of fMRI activation paradigms: A presurgical tool for mapping brain function

In this paper, we focus our attention on the phenomena at the root of eloquent brain maps, the description of some activation paradigms, their main presurgical application and some new approaches to fMRI

Functional MRI at 3.0 tesla

The localization of the BOLD signal becomes reliable at high MR fields. This allows not only to confirm and deepen many known functional phenomena but also to throw new light in many aspects of the physiology and pathology of the brai

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability

Extracorporeal Membrane Oxygenation (ECMO) in an Infant with COVID-19: A Case Report with Literature Review

Background: SARS-CoV-2 infection tends to be lethal to the elderly population. However, sometimes children are also involved. Case presentation: We present the case of a female infant with a corrected gestational age of 39 weeks and 4 days with severe COVID-19 pneumonia and co-infection of Klebsiella pneumoniae that was supported with extracorporeal membrane oxygenation (ECMO). Results: We reported the clinical case and reviewed the literature articles on ECMO and Covid-19 in infants and children up to two years of age. Conclusion: It is crucial to be aware of certain risk factors (severe prematurity, coinfection), which, when linked to SARS-CoV-2 infection, must immediately alert us to the possible criticality of the clinical condition of patients, as highlighted by our own clinical case

Amygdala responses to masked and low spatial frequency fearful faces. A preliminary fMRI study in panic disorder

Previous studies have demonstrated amygdala activation in response to fearful faces even if presented below the threshold of conscious visual perception. It has also been proposed that subcortical regions are selectively sensitive to low spatial frequency (LSF) information. However, chronic hyperarousal may reduce amygdala activation in panic disorder (PD). Our aim was to establish whether the amygdala is engaged by masked and LSF fearful faces in PD as compared to healthy subjects. Neutral faces were used as the mask stimulus. Thirteen PD patients (seven females, six males; mean age=29.1 (S.D: 5.9)) and 15 healthy volunteers (seven females, eight males; mean age=27.9 (S.D. 4.5)) underwent two passive viewing tasks during a 3T functional magnetic resonance imaging (fMRI) as follows: 1) presentation of faces with fearful versus neutral expressions (17ms) using a backward masking procedure and 2) presentation of the same faces whose spatial frequency contents had been manipulated by low-pass filtering. Level of awareness was confirmed by a forced choice fear-detection task. Whereas controls showed bilateral activation to fearful masked faces versus neutral faces, patients failed to show activation within the amygdala. LSF stimuli did not elicit amygdala response in either group, contrary to the view that LSF information plays a crucial role in the processing of facial expressions in the amygdala. Findings suggest maladaptive amygdala responses to potentially threatening visual stimuli in PD patients