Thomas C Booth

• EDiNR DMCC MBBS MA MRCP FRCR PhD
• Reader at King's College London

Aims The aim of the study is to identify machine learning model based on radiological biomarkers that can predict immune status within tumor microenvironment of IDH wildtype glioblastoma. Conclusion Radiogenomic SVM model non-invasively predicts immune status in wildtype glioblastoma microenvironment with good accuracy. This model has potential t...

Rationale Clinical outcomes in acute ischemic stroke due to medium vessel occlusion (MeVO) are often poor when treated with best medical management. Data from non-randomized studies suggest that endovascular treatment (EVT) may improve outcomes in MeVO stroke, but randomized data on potential benefits and risks are hitherto lacking. Thus, there is...

Background: Understanding sex-based differences in glioblastoma patients is necessary for accurate personalized treatment planning to improve patient outcomes. Purpose: To investigate sex-specific differences in molecular, clinical and radiological tumor parameters, as well as survival outcomes in glioblastoma, isocitrate dehydrogenase-1 wildtyp...

Background Immunotherapy is an effective “precision medicine” treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as pre-operative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical t...

Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for cl...

Background Apical ground-glass opacification (GGO) identified on CT angiography (CTA) performed for suspected acute stroke was developed in 2020 as a coronavirus-disease-2019 (COVID-19) diagnostic and prognostic biomarker in a retrospective study during the first wave of COVID-19. Objective To prospectively validate whether GGO on CTA performed fo...

Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of dou...

Background The aim was to predict survival of glioblastoma at eight months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. Methods Retrospective and prospective data were collected from 206 consecutive glioblastoma, IDH-...

Background The Pipeline Vantage Embolization Device (PEDV) is the fourth-generation pipeline flow diverter for intracranial aneurysm treatment. There are no outcome studies for the second PEDV version. We aimed to evaluate safety and efficacy outcomes. Primary and secondary objectives were to determine outcomes for unruptured and ruptured cohorts,...

Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)—involving automation of dataset labelling—represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologi...

AIM The goal of this study was to understand sex-specific differences in the molecular, clinical and radiological tumor parameters and survival outcomes of Glioblastoma (GBM) patients within the international GBM dataset, known as the ReSPOND (Radiomic Signatures for PrecisiON Diagnostics) consortium. METHODS Sex-based differences were retrospecti...

PURPOSE Glioblastoma is the most prevalent primary malignant brain tumor in adults, with a median overall survival (OS) of approximately 15 months and only limited advancements in prognostication and survival prediction. This study aims to evaluate an AI-based prognostic stratification model for OS prediction trained on the ReSPOND consortium data...

Diffuse gliomas are the commonest malignant primary brain tumour in adults. Herein, we present the most comprehensive analysis of the genomic landscape of adult glioma to date, by whole genome sequencing of 403 tumours. We identify an extended catalogue of recurrent coding and non-coding genetic mutations that represents a source for future studies...

Abstract Introduction: Radiogenomics, which involves obtaining imaging signatures of the underlying genome, can potentially identify immune biomarkers and enable personalized neo-adjuvant immunotherapy treatment. In this study, we performed systematic review of radiogenomic biomarkers for immunotherapy in glioblastoma and reviewed online databases...

AIMS Immunotherapy is an effective precision medicine treatment for several cancers. Obtaining imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients, has the potential to identify immune biomarkers pre-operatively and allow personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the...

AIMS To evaluate current available datasets for radiogenomic analysis in glioblastoma METHOD A scoping review was performed in the literature from 2000-2023. List of datasets (open access, controlled, paid) available in the literature were tabulated. Qualitative analysis was performed. RESULTS Multiple datasets and data portals were available. Cl...

BACKGROUND Glioblastoma is the most common malignant primary brain tumour in adults and is usually incurable. Post-operative magnetic resonance imaging (MRI) is used to assess extent of resection, and subsequent follow-up MRI is used to monitor response to treatment and to detect progression. The benefit of regular, scheduled follow-up MRI on patie...

BACKGROUND Glioblastoma is an aggressive brain tumour routinely monitored with MRI. However, the potential to use imaging as a prognostic biomarker predicting survival after treatment starts is unclear. This study aims to predict survival within eight months of completing radiotherapy from MRIs using deep learning. MATERIAL AND METHODS The dataset...

Purpose: We report what we believe is the first application of robotically constrained image-guided surgery to approach a fistulous micro-arteriovenous malformation in a highly eloquent location. Drawing on institutional experience with a supervisory-control robotic system, a series of steps were devised to deliver a tubular retractor system to a...

Background Autonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. Objective To determine from recent literature, through a systematic review, the impact, challenges, an...

Purpose: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. Methods: Preoper...

Purpose The use of robotics is emerging for performing interventional radiology procedures. Robots in interventional radiology are typically controlled using button presses and joystick movements. This study identified how different human–robot interfaces affect endovascular surgical performance using interventional radiology simulations. Methods...

The aim of the study was to evaluate current available datasets for radiogenomic analysis in glioblastoma.

Purpose The recently introduced Pipeline Vantage Embolization Device with Shield Technology is the fourth generation of Pipeline flow diverter devices. Due to the relatively high rate of intraprocedural technical complications, modifications were subsequently made to the device after a limited release of the device in 2020. This study aimed to eval...

Purpose Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting th...

Purpose People with primary malignant brain tumors (PMBT) undergo anti-tumor treatment and are followed up with MRI interval scans. There are potential burdens and benefits to interval scanning, yet high-quality evidence to suggest whether scans are beneficial or alter outcomes of importance for patients is lacking. We aimed to gain an in-depth und...

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of ad...

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness abo...

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness abo...

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 onl...

Aim To assess the clinical performance of a commercially available machine learning (ML) algorithm in acute stroke. Materials and methods CT and CT angiography (CTA) studies of 104 consecutive patients (43 females, age range 19–93, median age 62) performed for suspected acute stroke at a single tertiary institution with real-time ML software analy...

PURPOSE T2-FLAIR mismatch (T2FM) is a highly specific imaging biomarker for isocitrate dehydrogenase (IDH) mutation in low-grade gliomas. Previous T2FM studies are inconsistent for glioblastoma (GBM)/grade-4 glioma, partly due to low IDH-mutation prevalence in high-grade gliomas. We leveraged a large multi-institutional GBM/grade-4 glioma cohort to...

Background Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms...

PURPOSE Glioblastoma, IDH-wildtype, is the most common primary malignant adult brain tumor with median overall survival (OS) of ~14 months, with little improvement over the last 20 years. We hypothesize that AI-based integration of quantitative tumor characteristics, independent of acquisition protocol and equipment, can reveal accurate generalizab...

PURPOSE Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are...

Introduction Colloid cysts, although benign, may occasionally cause obstructive hydrocephalus and sudden death. Reliable prognostic factors for symptomatic progression have been sought with heterogenous results. Methods We conducted a retrospective review of all cases of colloid cysts of the third ventricle managed at our centre between 2009 and 2...

AIMS The Gliocova dataset uses linked English national cancer data on all 51,775 adult primary brain tumour patients diagnosed between 2013-2018. Here we investigate patient safety and post-operative complications after first surgical intervention. METHOD We identified patients undergoing first surgical intervention (surgical debulking or biopsy)...

Introduction Glioblastoma is the most common malignant primary brain tumour with a median overall survival of 12–15 months (range 6–17 months), even with maximal treatment involving debulking neurosurgery and adjuvant concomitant chemoradiotherapy. The use of postoperative imaging to detect progression is of high importance to clinicians and patien...

Background People living with primary malignant brain tumours (PMBT) face a complex and unpredictable illness. Throughout the disease course they undergo various treatments and follow-up with regular interval scanning. There are potential costs and benefits to interval scanning, however there is no high-quality evidence to suggest interval scanning...

Objective We describe the chronological trends in cerebral revascularization surgery through a single-surgeon experience; and we review whether in the context of giant and fusiform cerebral aneurysms, flow-diverting stents have impacted on the use of cerebral revascularization surgery. Methods We review our single institution prospectively collect...

Introduction The recently introduced Pipeline Vantage Embolization Device With Shield Technology is the fourth generation of Pipeline flow-diverter devices. The device is manufactured from 48 or 64 drawn filled wires (DFT).¹ DFT technology is intended to improve the opening characteristics of the stent. The pore density is higher than previous gene...

Introduction A great variety of neurointerventional devices now exist and practitioners may not be familiar with compatibility especially of newer devices. As procedures become more complex and guide catheters increase in size more devices can often be inserted into a single catheter lumen. Predicting in advance whether(i) they will fit and (ii) wh...

Introduction Carotid webs are increasingly recognised as a cause of recurrent stroke even in patients receiving anticoagulant or antiplatelet therapy.¹ Carotid stenting (CAS) and endarterectomy (CEA) have both been used to treat the disease but the optimal therapy has not yet been established. Aims of study To compare outcomes of CAS and CEA to tr...

Introduction Flow diversion is a widely accepted technique for the treatment of intracranial aneurysms. Aim Study objectives are to provide safety and efficacy data on the FRED/FRED Jr devices in treatment of aneurysms in UK centers following a good clinical practice study design.We report on full population efficacy and safety results with follow...

Background Glioblastoma is the most common malignant brain tumor in adults and has a poor prognosis. This cohort of patients is diverse and imaging is vital to formulate treatment plans. Despite this, there is relatively little data on patterns of use of imaging and imaging workload in routine practice. Methods We examined imaging patterns for all...

Objective To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep learning mode...

Background The impact on clinical outcomes of patient selection using perfusion imaging for endovascular thrombectomy (EVT) in patients with acute ischemic stroke presenting beyond 6 hours from onset remains undetermined in routine clinical practice. Methods Patients from a national stroke registry that underwent EVT selected with or without perfu...

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Fede...

Background The effectiveness and safety of endovascular thrombectomy (EVT) in the late window (6–24 hours) for acute ischemic stroke (AIS) patients selected without advanced imaging is undetermined. We aimed to assess clinical outcomes and the relationship with time-to-EVT treatment beyond 6 hours of stroke onset without advanced neuroimaging. Met...

Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for mon...

Objective To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments. Methods The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perf...

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans in recent years. For many neurological conditions, this delay can result in poorer patient outcomes and inflated healthcare costs. Potentially, computer vision mod...

Objective Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies. Methods Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMB...

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient car...

Background Robotically performed neurointerventional surgery has the potential to reduce occupational hazards to staff, perform intervention with greater precision, and could be a viable solution for teleoperated neurointerventional procedures. Objective To determine the indication, robotic systems used, efficacy, safety, and the degree of manual...

PURPOSE Multi-parametric MRI and artificial intelligence (AI) methods were previously used to predict peritumoral neoplastic cell infiltration and risk of future recurrence in glioblastoma, in single-institution studies. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured, engineered/selected, and quantified b...

PURPOSE Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for gliob...

BACKGROUND The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). MATERIAL AND METHODS PRISMA methodology was followed. Articles published 09/2018-01/2021 (since previous reviews) were searched...

BACKGROUND Glioblastoma is a common and aggressive primary malignant brain tumour in adults associated with a poor prognosis and considerable symptom burden. Clinical review and serial neuroimaging remain the primary monitoring tools to assess for disease status. However, the evidence base for the existing surveillance imaging schedule is yet to be...

Objectives The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. Methods Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Thr...

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting t...

Introduction Paraplegia post-thoracoabdominal aortic aneurysm (TAAA) repair remains both a devastating and poorly understood complication. We related temporal changes in cellular and protein composition of cerebrospinal fluid (CSF) to neurological outcomes after TAAA repair to gain mechanistic insights driving paraplegia. Method Patients undergoin...

The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). Articles were searched for using MEDLINE, EMBASE, and the Cochrane Register. Included study participants were adult patients with high grad...

Objectives: With increasing availability and frequency of neurological imaging over the past decades, greater numbers of unruptured intracranial aneurysms (UIAs) are being detected, resulting in a dilemma for both patient and clinician. Our health economic study uses real-world data to compare the lifetime costs and consequences of current manageme...

Objectiv e To summarise current evidence for the utility of interval imaging in monitoring disease in adult brain tumours, and to develop a position for future evidence gathering while incorporating the application of data science and health economics. Methods Experts in ‘interval imaging’ (imaging at pre-planned time-points to assess tumour statu...

Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the...

The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). Articles published 09/2018–09/2020 were searched for using MEDLINE, EMBASE, and the Cochrane Register. Included study participants were adu...

Purpose There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment...

In conjunction with our recently published article entitled “COVID-19 Stroke Apical Lung Examination Study: A Diagnostic and Prognostic Imaging Biomarker in Suspected Acute Stroke,” the authors are pleased to also report our supplementary findings related to chest radiographs. In our search for relevant diagnostic biomarkers within the lung apices...

Background Deep learning has the potential to aid clinical decision‐making in dementia, by automatically classifying brain images. However, several key limitations currently prohibit clinical adoption: 1) network design must be optimised for 3D neuroimaging; 2) analysis must be computationally feasible; 3) model decisions must be interpretable. Int...

AI-based methods have shown great promise in a variety of biomedical research fields, including neurooncologic imaging. For example, machine learning methods have offered informative predictions of overall survival (OS) and progression-free survival (PFS), differentiation between pseudoprogression (PsP) and progressive disease (PD), and estimation...

Objectives MRI remains the preferred imaging investigation for glioblastoma. Appropriate and timely neuroimaging in the follow-up period is considered to be important in making management decisions. There is a paucity of evidence-based information in current UK, European and international guidelines regarding the optimal timing and type of neuroima...

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared...

Background and purpose: Diagnosis of coronavirus disease 2019 (COVID-19) relies on clinical features and reverse-transcriptase polymerase chain reaction testing, but the sensitivity is limited. Carotid CTA is a routine acute stroke investigation and includes the lung apices. We evaluated CTA as a potential COVID-19 diagnostic imaging biomarker. M...

Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the...

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text...

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared...

Neuroimaging—especially functional MR imaging (fMRI)—opens the door to non-invasively map cortical processing and to understand how our brain works. fMRI evolved from basic (Ogawa et al., Proc Natl Acad Sci U S A 87:9868–9872, 1990; Ogawa et al., Biophys J 64:803–812, 1993; Kwong et al., Proc Natl Acad Sci U S A 89:5675–5679, 1992) and clinical app...

Deep learning is attracting significant interest in the neuroimaging community as a means to diagnose psychiatric and neurological disorders from structural magnetic resonance images. However, there is a tendency amongst researchers to adopt architectures optimized for traditional computer vision tasks, rather than design networks customized for ne...

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text...

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared...