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Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating form of stroke frequently affecting young to middle-aged adults, with an unmet need to improve outcome. This special report focusses on the development of intrathecal haptoglobin supplementation as a treatment by reviewing current knowledge and progress, arriving at a Delphi-based global consensus regarding the pathophysiological role of extracellular hemoglobin and research priorities for clinical translation of hemoglobin-scavenging therapeutics. After aneurysmal subarachnoid hemorrhage, erythrocyte lysis generates cell-free hemoglobin in the cerebrospinal fluid, which is a strong determinant of secondary brain injury and long-term clinical outcome. Haptoglobin is the body’s first-line defense against cell-free hemoglobin by binding it irreversibly, preventing translocation of hemoglobin into the brain parenchyma and nitric oxide-sensitive functional compartments of cerebral arteries. In mouse and sheep models, intraventricular administration of haptoglobin reversed hemoglobin-induced clinical, histological, and biochemical features of human aneurysmal subarachnoid hemorrhage. Clinical translation of this strategy imposes unique challenges set by the novel mode of action and the anticipated need for intrathecal drug administration, necessitating early input from stakeholders. Practising clinicians (n=72) and scientific experts (n=28) from 5 continents participated in the Delphi study. Inflammation, microvascular spasm, initial intracranial pressure increase, and disruption of nitric oxide signaling were deemed the most important pathophysiological pathways determining outcome. Cell-free hemoglobin was thought to play an important role mostly in pathways related to iron toxicity, oxidative stress, nitric oxide, and inflammation. While useful, there was consensus that further preclinical work was not a priority, with most believing the field was ready for an early phase trial. The highest research priorities were related to confirming haptoglobin’s anticipated safety, individualized versus standard dosing, timing of treatment, pharmacokinetics, pharmacodynamics, and outcome measure selection. These results highlight the need for early phase trials of intracranial haptoglobin for aneurysmal subarachnoid hemorrhage, and the value of early input from clinical disciplines on a global scale during the early stages of clinical translation.

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository.

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5&ndash, 465.96 nm, 498.71&ndash, 509.62 nm, 556.91&ndash, 575.1 nm, 593.29&ndash, 615.12 nm, 636.94&ndash, 666.05 nm, 698.79&ndash, 731.53 nm and 884.32&ndash, 902.51 nm.

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Resumen Objetivo Evaluar y predecir factores que influyan en el pronostico y/o resultado clinico a los 6 meses de pacientes con hemorragia subaracnoidea espontanea, en grados iv y v de la World Federation of Neurosurgical Societies (WFNS). Material y metodos Estudio retrospectivo de una serie consecutiva de 394 pacientes que ingresaron en nuestro hospital, con diagnostico clinico y radiologico de hemorragia subaracnoidea espontanea, desde el 1 de enero de 1999 hasta el 30 de junio de 2009. Se seleccionaron 121 pacientes que reunian el criterio de estar en grado iv o v de la WFNS antes del tratamiento; se excluyeron 3 pacientes por perdida de seguimiento. La variable resultado se evaluo a los 6 meses del evento mediante la Escala de Resultados de Glasgow. Se considero un resultado estadisticamente significativo un valor de p Resultados Ciento veintiun pacientes se incluyeron en el analisis estadistico. La edad media de la serie fue de 54 anos (14-92). Los pacientes que presentaban una puntuacion en la Escala de Coma de Glasgow media inferior a 7 puntos (p v (p Conclusiones Los pacientes que ingresan con una gradacion de v en la escala de la WFNS y/o presentan trastorno pupilar y/o hematoma intraparenquimatoso asocian un peor resultado clinico final.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Introduction: More extensive surgical resection is associated with longer life expectancy for newly diagnosed gliomas. Current intraoperative techniques to delineate the limits and characteristics of gliomas, such as Immunofluorescence or Intraoperative Magnetic Resonance Imaging (iMRI), have limitations. iMRI is allows only a few images to be obtained and is costly. Immunofluorescence requires preoperative administration and is poor in low grade lesions. Hyperspectral imaging (HSI) is an emerging technology in the medical field that combines the benefits of digital imaging and spectroscopy, allowing to simultaneously exploit both the morphology and the chemical composition of the materials that are presented in a scene. Here we set out to apply this technique for the first time to identify and delineate brain tumour boundaries in real-time during neurosurgical operations. Materials and methods: Using a new hyperspectral acquisition system covering the spectral range from 400 nm to 1700 nm, a hyperspectral library of human brain affected by different types of tumours was generated (more than 250.000 spectral signatures). The images were acquired intraoperatively from 30 different patients after performing craniotomy and durotomy. Biopsies were taken in defined areas of the image and assessed by neuropathology. These pixels were used as the gold standard to generate a Support Vector Machine classifier. Results: Supervised learning algorithms show that it is possible to automatically distinguish between tumour tissue, normal tissue, hypervascularized tissue and other elements present in the brain surface using a previously captured database. Quantitative results computed employing the pixels used as the gold standard provide an overall accuracy above 95% in most cases. Using the developed hyperspectral brain cancer detection system, classification maps, where each type of tissue is represented using different colours, are provided to neurosurgeons as an aid-visualization tool for delimiting the tumour margins during a neurosurgical operation in real-time. Conclusions: A novel non-invasive medical imaging technique has been developed for delimiting the tumour margins during neurosurgical operations. HSI is a promising new technique to help neurosurgeons during brain tumour surgery, avoiding leaving unintentional tumour residuum or the excessive resection of normal brain.

To evaluate and predict factors influencing prognosis and/or clinical outcome at 6 months in patients with spontaneous subarachnoid haemorrhage, World Federation of Neurosurgical Societies (WFNS) grades iv and v.This was a retrospective study of a consecutive series of 394 patients admitted to our hospital with clinical and radiological diagnosis of spontaneous subarachnoid haemorrhage, from 1 January 1999 to 30 June 2009. We selected 121 patients who met the criteria of being in WFNS grades iv or v before treatment; 3 patients were excluded due to loss of tracking. The outcome variable was assessed 6 months after the event using the Glasgow Outcome Scale. A P value.05 was considered statistically significant.One hundred and twenty-one patients were included in the statistical analysis. The average age of the patients in the series was 54 years (14-92). Patients who had a mean Glasgow Coma Scale lower than 7 points (P.0001), those who were grade v (P.0001) in the pre-treatment WFNS scale and those with pupillary disorder (P=.002) had a worse clinical outcome. Likewise, those with associated intraparenchymal hematoma (P=.020) and those not receiving any treatment (P=.020) were also associated with a poor clinical outcome. These results were statistically significant.Patients admitted with a WFNS grade v and/or presenting pupil disorder and/or intraparenchymal hematoma were associated with worse clinical outcomes.