Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing to high morbidity and mortality rates and placing a substantial economic burden on healthcare systems worldwide. The diagnosis of TBI relies on clinical information along with Computed Tomography (CT) scans. Addressing the multifaceted challenges posed by TBI has seen the development of innovative, data-driven approaches, for this complex condition. Particularly noteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority of TBI cases where conventional methods often fall short. As such, we review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI. We categorize ML applications based on their data sources, and there is a spectrum of ML techniques used to date. Most of these techniques have primarily focused on diagnosis, with relatively few attempts at predicting the prognosis. This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data-driven approaches and standard diagnostic data.Comment: The manuscript has 34 pages, 3 figures, and 4 table

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

Study protocol for investigating the clinical performance of an automated blood test for glial fibrillary acidic protein and ubiquitin carboxy-terminal hydrolase L1 blood concentrations in elderly patients with mild traumatic BRAIN Injury and reference values (BRAINI-2 Elderly European study): a prospective multicentre observational study

Computed tomography; Neurosurgery; Trauma managementTomografia computaritzada; Neurocirurgia; Gestió del traumaTomografía computarizada; Neurocirugía; Gestión del traumaIntroduction Two blood brain-derived biomarkers, glial fibrillar acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), can rule out intracranial lesions in patients with mild traumatic brain injury (mTBI) when assessed within the first 12 hours. Most elderly patients were excluded from previous studies due to comorbidities. Biomarker use in elderly population could be affected by increased basal levels. This study will assess the performance of an automated test for measuring serum GFAP and UCH-L1 in elderly patients to predict the absence of intracranial lesions on head CT scans after mTBI, and determine both biomarkers reference values in a non-TBI elderly population. Methods and analysis This is a prospective multicentre observational study on elderly patients (≥65 years) that will be performed in Spain, France and Germany. Two patient groups will be included in two independent substudies. (1) A cohort of 2370 elderly patients (1185<80 years and 1185≥80 years; BRAINI2-ELDERLY DIAGNOSTIC AND PROGNOSTIC STUDY) with mTBI and a brain CT scan that will undergo blood sampling within 12 hours after mTBI. The primary outcome measure is the diagnostic performance of GFAP and UCH-L1 measured using an automated assay for discriminating between patients with positive and negative findings on brain CT scans. Secondary outcome measures include the performance of both biomarkers in predicting early (1 week) and midterm (3 months) neurological status and quality of life after trauma. (2) A cohort of 480 elderly reference participants (BRAINI2-ELDERLY REFERENCE STUDY) in whom reference values for GFAP and UCHL1 will be determined. Ethics and dissemination Ethical approval was obtained from the Institutional Review Boards of Hospital 12 de Octubre in Spain (Re#22/027) and Southeast VI (Clermont Ferrand Hospital) (Re# 22.01782.000095) in France. The study’s results will be presented at scientific meetings and published in peer-review publications.This study was supported by a grant from the European Institute of Innovation and Technology (EIT) Health (BP 2022–2024). EIT Health is supported by EIT, a body of the European Union. BioMérieux is responsible for the development and manufacturing of the VIDAS GFAP and VIDAS UCH-L1 assays. BioMérieux will provide in-kind support to this study by supplying the assays for measuring UCH–L1 and GFAP necessary for this study

Development of a lesion localisation tool to improve outcome prediction in Traumatic Brain Injury patients

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022Traumatic brain injury (TBI) is a highly heterogeneous pathology that poses severe health and socioeconomic problems on a global scale. Neuroimaging research and development has advanced its clinical care in numerous ways, as injured brains are being imaged and studied in greater detail. The size and location of TBI lesions are often necessary to accurately determine a prognosis, which is key in defining a patient-specific rehabilitation program. This dissertation aims to investigate the impact of lesion characteristics, such as volume and location, on outcome prediction in TBI patients. Lesion localisation was achieved by comparing annotated TBI lesions to a brain atlas. Furthermore, other lesion characteristics were examined across different Magnetic Resonance Imaging (MRI) sequences and scanners, with results suggesting that the use of different scanners or MRI contrasts introduced biases in said lesion characteristics. Patient outcome was predicted using four generalised linear models. Besides clinical variables, these models included lesion volume, group and location as predictors. Model comparison indicated that lesion volume could be beneficial for outcome prediction, but may be dependent on both lesion group and location. Overall, this methodology showed potential in uncovering the effect that certain lesion groups and/or locations have on patient outcome after TBI

Role of diffusion tensor imaging as an imaging biomarker and theranostic tool in structural imaging of traumatic brain injury

Neuroimaging technology is at a "newborn" stage in the evaluation of TBI. While additional literature are obviously required to decide whether these modalities and progress in knowledge with noninvasive monitors will allow early and consistent recognition of revocable secondary brain damages, the final query is whether these new modalities will help in treatment plans that will absolutely mark result. DTI is an influential instrument for assessing white matter anatomy and related anomalies. DTI was formerly an investigation tool, but is using clinical practice. Accepting the terms and basic ideas of this method can aid in the clinical implementation and interpretation of this blend of structural and physiologic white matter evaluation. In conclusion, although DTI is as a diagnostic tool for severity of TBI and as an outcome predictor, but severe preclinical and clinical validation of each imaging method should be a top importance

Clinical-pathological study on β-APP, IL-1β, GFAP, NFL, Spectrin II, 8OHdG, TUNEL, miR-21, miR-16, miR-92 expressions to verify DAI-diagnosis, grade and prognosis

Traumatic brain injury (TBI) is one of the most important death and disability cause, involving substantial costs, also in economic terms, when considering the young age of the involved subject. Aim of this paper is to report a series of patients treated at our institutions, to verify neurological results at six months or survival; in fatal cases we searched for βAPP, GFAP, IL-1β, NFL, Spectrin II, TUNEL and miR-21, miR-16, and miR-92 expressions in brain samples, to verify DAI diagnosis and grade as strong predictor of survival and inflammatory response. Concentrations of 8OHdG as measurement of oxidative stress was performed. Immunoreaction of β-APP, IL-1β, GFAP, NFL, Spectrin II and 8OHdG were significantly increased in the TBI group with respect to control group subjects. Cell apoptosis, measured by TUNEL assay, were significantly higher in the study group than control cases. Results indicated that miR-21, miR-92 and miR-16 have a high predictive power in discriminating trauma brain cases from controls and could represent promising biomarkers as strong predictor of survival, and for the diagnosis of postmortem traumatic brain injury

Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria. Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT). Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that presented sub-optimal results. The first one was based on the similarity metric of the registration of every scan to the study-specific CT template, the second aimed to identify any scans with regions that were completely collapsed post registration, and the final one identified scans with a significant volume of intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted, along with a bias assessment of the BLAST-CT tool. Our results show that the constructed pipeline is able to successfully localise TBI lesions across the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each brain region to be inversely correlated with the lesion volume within that region. No considerable bias was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male and female patients within a specific age range was caused by the discrepancy in lesion volume presented by the scans included in each sample