Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting.

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 clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18-96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2 -weighted, T2 -FLAIR, T1 -weighted, diffusion-weighted, and gradient-recalled echo T2 *-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.

Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy?

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 doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

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: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION: Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

The experience of interval scans for adults living with primary malignant brain tumors.

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 understanding of how adults living with PMBTs experience and cope with interval scanning. METHODS: Twelve patients diagnosed with WHO grade III or IV PMBT from two sites in the UK took part. Using a semi-structured interview guide, they were asked about their experiences of interval scans. A constructivist grounded theory approach was used to analyze data. RESULTS: Although most participants found interval scans uncomfortable, they accepted that scans were something that they had to do and were using various coping methods to get through the MRI scan. All participants said that the wait between their scan and results was the most difficult part. Despite the difficulties they experienced, all participants said that they would rather have interval scans than wait for a change in their symptoms. Most of the time, scans provided relief, gave participants some certainty in an uncertain situation, and a short-term sense of control over their lives. CONCLUSION: The present study shows that interval scanning is important and highly valued by patients living with PMBT. Although interval scans are anxiety provoking, they appear to help people living with PMBT cope with the uncertainty of their condition.

Robotically facilitated parafasicular microsurgery to a brain arteriovenous malformation in a paediatric patient.

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 deeply situated micro-arteriovenous malformation. The surgical footprint of this procedure was minimised along with the neurological morbidity. We hope that our contribution will be of assistance to others in integrating such systems given a similar clinical problem. CLINICAL PRESENTATION: A right-handed 9-year old girl presented to her local emergency department after a sudden onset of severe headache accompanied by vomiting. An intracranial haemorrhage centred in the right centrum semiovale with intraventricular extension was evident and she was transferred urgently to the regional paediatric neurosurgical centre, where an external ventricular drain (EVD) was sited. A digital subtraction angiogram demonstrated a small right hemispheric arteriovenous shunt irrigated by peripheral branches of the middle cerebral artery & a robotically facilitated parafasicular microsurgical approach was performed to disconnect the arteriovenous malformation. CONCLUSION: We describe the successful microsurgical in-situ disconnection of a deeply-situated, fistulous micro-AVM via a port system itself delivered directly to the target with a supervisory-control robotic system. This minimised the surgical disturbance along a relatively long white matter trajectory and demonstrates the feasibility of this approach for deeply located arteriovenous fistulae or fistulous AVMs.

Perfusion Imaging for Endovascular Thrombectomy in Acute Ischemic Stroke Is Associated With Improved Functional Outcomes in the Early and Late Time Windows.

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 perfusion imaging (noncontrast computed tomography/computed tomography angiography) in the early (<6 hours) and late (6-24 hours) time windows, between October 2015 and March 2020, were compared. The primary outcome was the ordinal shift in the modified Rankin Scale score at hospital discharge. Other outcomes included functional independence (modified Rankin Scale score ≤2) and in-hospital mortality, symptomatic intracerebral hemorrhage, successful reperfusion (Thrombolysis in Cerebral Infarction score 2b-3), early neurological deterioration, futile recanalization (modified Rankin Scale score 4-6 despite successful reperfusion) and procedural time metrics. Multivariable analyses were performed, adjusted for age, sex, baseline stroke severity, prestroke disability, intravenous thrombolysis, mode of anesthesia (Model 1) and including EVT technique, balloon guide catheter, and center (Model 2). RESULTS: We included 4249 patients, 3203 in the early window (593 with perfusion versus 2610 without perfusion) and 1046 in the late window (378 with perfusion versus 668 without perfusion). Within the late window, patients with perfusion imaging had a shift towards better functional outcome at discharge compared with those without perfusion imaging (adjusted common odds ratio [OR], 1.45 [95% CI, 1.16-1.83]; P=0.001). There was no significant difference in functional independence (29.3% with perfusion versus 24.8% without; P=0.210) or in the safety outcome measures of symptomatic intracerebral hemorrhage (P=0.53) and in-hospital mortality (10.6% with perfusion versus 14.3% without; P=0.053). In the early time window, patients with perfusion imaging had significantly improved odds of functional outcome (adjusted common OR, 1.51 [95% CI, 1.28-1.78]; P=0.0001) and functional independence (41.6% versus 33.6%, adjusted OR, 1.31 [95% CI, 1.08-1.59]; P=0.006). Perfusion imaging was associated with lower odds of futile recanalization in both time windows (late: adjusted OR, 0.70 [95% CI, 0.50-0.97]; P=0.034; early: adjusted OR, 0.80 [95% CI, 0.65-0.99]; P=0.047). CONCLUSIONS: In this real-world study, acquisition of perfusion imaging for EVT was associated with improvement in functional disability in the early and late time windows compared with nonperfusion neuroimaging. These indirect comparisons should be interpreted with caution while awaiting confirmatory data from prospective randomized trials.

Accurate brain-age models for routine clinical MRI examinations.

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 care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

Deep learning to automate the labelling of head MRI datasets for computer vision applications.

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. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports ('reference-standard report labels'); a subset of these examinations (n = 250) were assigned 'reference-standard image labels' by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images.

Glioblastoma post-operative imaging in neuro-oncology: current UK practice (GIN CUP study).

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 neuroimaging following initial neurosurgical treatment. This study assessed the current imaging practices amongst UK neuro-oncology centres, thus providing baseline data and informing future practice. METHODS: The lead neuro-oncologist, neuroradiologist and neurosurgeon from every UK neuro-oncology centre were invited to complete an online survey. Participants were asked about current and ideal imaging practices following initial treatment. RESULTS: Ninety-two participants from all 31 neuro-oncology centres completed the survey (100% response rate). Most centres routinely performed an early post-operative MRI (87%, 27/31), whereas only a third performed a pre-radiotherapy MRI (32%, 10/31). The number and timing of scans routinely performed during adjuvant TMZ treatment varied widely between centres. At the end of the adjuvant period, most centres performed an MRI (71%, 22/31), followed by monitoring scans at 3 monthly intervals (81%, 25/31). Additional short-interval imaging was carried out in cases of possible pseudoprogression in most centres (71%, 22/31). Routine use of advanced imaging was infrequent; however, the addition of advanced sequences was the most popular suggestion for ideal imaging practice, followed by changes in the timing of EPMRI. CONCLUSION: Variations in neuroimaging practices exist after initial glioblastoma treatment within the UK. Multicentre, longitudinal, prospective trials are needed to define the optimal imaging schedule for assessment. KEY POINTS: • Variations in imaging practices exist in the frequency, timing and type of interval neuroimaging after initial treatment of glioblastoma within the UK. • Large, multicentre, longitudinal, prospective trials are needed to define the optimal imaging schedule for assessment.

Outcome study of the pipeline embolization device for treatment of intracranial aneurysms at a single UK institution.

BACKGROUND: The introduction of flow-diverting stents in the last decade provides an alternative endovascular treatment choice in selected intracranial aneurysms. This retrospective analysis of a UK centre's experience provides insight into clinical and radiographic outcomes. METHODS: Electronic patient records, diagnostic and procedural images and written procedural records for patients treated with the PED between August 2009 and April 2014 were reviewed. Follow-up TOF MRA was performed after treatment. Clinical and radiographic outcomes were analyzed and compared with other PED studies. RESULTS: Twenty-nine patients with 30 attempted PED treatments were reviewed representing 3.5% of the treated aneurysm patient cohort. 63.6% (21/33) of the aneurysms were wide-necked (>4 mm), 60.6% (20/33) were large or giant (≥10 mm). The mean aneurysm sac diameter was 12.0 mm; the mean neck width was 4.5 mm. Mortality and morbidity rates were 3.3% and 10.0%, respectively. The total adequate occlusion rate was 78.1% (25/32) at 18 months. The neck width of aneurysms with residual sac filling and complete occlusion differed significantly (p = 0.04). CONCLUSIONS: Highly selected aneurysms treated with a PED in a UK centre have similar occlusion and complication rates when compared to non-UK studies. Again, it appeared that delayed aneurysm rupture remained a risk for PED treatment in large or giant aneurysms. Follow-up with TOF MRA gave similar occlusion results compared to those obtained with DSA in other studies. The influence of neck size on occlusion rate should be examined in future PED studies.

Evaluation of two selection tests for recruitment into radiology specialty training.

BACKGROUND: This study evaluated whether two selection tests previously validated for primary care General Practice (GP) trainee selection could provide a valid shortlisting selection method for entry into specialty training for the secondary care specialty of radiology. METHODS: We conducted a retrospective analysis of data from radiology applicants who also applied to UK GP specialty training or Core Medical Training. The psychometric properties of the two selection tests, a clinical problem solving (CPS) test and situational judgement test (SJT), were analysed to evaluate their reliability. Predictive validity of the tests was analysed by comparing them with the current radiology selection assessments, and the licensure examination results taken after the first stage of training (Fellowship of the Royal College of Radiologists (FRCR) Part 1). RESULTS: The internal reliability of the two selection tests in the radiology applicant sample was good (α ≥ 0.80). The average correlation with radiology shortlisting selection scores was r = 0.26 for the CPS (with p < 0.05 in 5 of 11 shortlisting centres), r = 0.15 for the SJT (with p < 0.05 in 2 of 11 shortlisting centres) and r = 0.25 (with p < 0.05 in 5 of 11 shortlisting centres) for the two tests combined. The CPS test scores significantly correlated with performance in both components of the FRCR Part 1 examinations (r = 0.5 anatomy; r = 0.4 physics; p < 0.05 for both). The SJT did not correlate with either component of the examination. CONCLUSIONS: The current CPS test may be an appropriate selection method for shortlisting in radiology but would benefit from further refinement for use in radiology to ensure that the test specification is relevant. The evidence on whether the SJT may be appropriate for shortlisting in radiology is limited. However, these results may be expected to some extent since the SJT is designed to measure non-academic attributes. Further validation work (e.g. with non-academic outcome variables) is required to evaluate whether an SJT will add value in recruitment for radiology specialty training and will further inform construct validity of SJTs as a selection methodology.

Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment.

PURPOSE: The acquisition of ever increasing volumes of high resolution magnetic resonance imaging (MRI) data has created an urgent need to develop automated and objective image analysis algorithms that can assist in determining tumor margins, diagnosing tumor stage, and detecting treatment response. METHODS: We have shown previously that Minkowski functionals, which are precise morphological and structural descriptors of image heterogeneity, can be used to enhance the detection, in T1 -weighted images, of a targeted Gd(3+) -chelate-based contrast agent for detecting tumor cell death. We have used Minkowski functionals here to characterize heterogeneity in T2 -weighted images acquired before and after drug treatment, and obtained without contrast agent administration. RESULTS: We show that Minkowski functionals can be used to characterize the changes in image heterogeneity that accompany treatment of tumors with a vascular disrupting agent, combretastatin A4-phosphate, and with a cytotoxic drug, etoposide. CONCLUSIONS: Parameterizing changes in the heterogeneity of T2 -weighted images can be used to detect early responses of tumors to drug treatment, even when there is no change in tumor size. The approach provides a quantitative and therefore objective assessment of treatment response that could be used with other types of MR image and also with other imaging modalities.

Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning.

PURPOSE: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous guidewire navigation in MT vasculature using inverse reinforcement learning (IRL) to leverage expert demonstrations. METHODS: Employing the Simulation Open Framework Architecture (SOFA), this study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized the soft actor-critic algorithm to train models with various reward functions and compared their performance in silico. RESULTS: We demonstrated feasibility of navigation using IRL. When evaluating single- versus dual-device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through 'reward shaping'. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s). CONCLUSIONS: We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by effectively employing IRL based on demonstrator expertise. The results underscore the potential of using reward shaping to efficiently train models, offering a promising avenue for enhancing the accessibility and precision of MT procedures. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability.

SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma.

Background and objective: Malignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors. Methods: In this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance. Results: Several measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %. Conclusions: The research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies.

Background: Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. Methods: A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results: Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Conclusions: Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Endovascular treatment to improve outcomes for medium vessel occlusions: The ESCAPE-MeVO trial.

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 insufficient evidence to guide EVT decision-making in MeVO stroke. AIMS: The primary aim of the ESCAPE-MeVO trial is to demonstrate that acute, rapid EVT in patients with acute ischemic stroke due to MeVO results in better clinical outcomes compared to best medical management. Secondary outcomes are to demonstrate the safety of EVT, its impact on self-reported health-related quality of life, and cost-effectiveness. SAMPLE SIZE ESTIMATES: Based on previously published data, we estimate a sample size of 500 subjects to achieve a power of 85% with a two-sided alpha of 0.05. To account for potential loss to follow-up, 530 subjects will be recruited. METHODS AND DESIGN: ESCAPE-MeVO is a multicenter, prospective, randomized, open-label study with blinded endpoint evaluation (PROBE design), clinicaltrials.gov: NCT05151172. Subjects with acute ischemic stroke due to MeVO meeting the trial eligibility criteria will be allocated in a 1:1 ratio to best medical care plus EVT versus best medical care only. Patients will be screened only at comprehensive stroke centers to determine if they are eligible for the trial, regardless of whether they were previously treated at a primary care center. Key eligibility criteria are (1) acute ischemic stroke due to MeVO that is clinically and technically eligible for EVT, (2) last-known well within the last 12 h, (3) National Institutes of Health Stroke Scale > 5 or 3-5 with disabling deficit, (4) high likelihood of salvageable tissue on non-invasive neuroimaging. STUDY OUTCOMES: The primary outcome is the modified Rankin scale 90 days after randomization (shift analysis), whereby modified Rankin Score 5 and 6 will be collapsed into one category. Secondary outcomes include dichotomizations of the modified Rankin Score at 90 days, 24 h National Institutes of Health Stroke Score, difference between 24 h and baseline National Institutes of Health Stroke Score, mortality at 90 days, health-related quality of life (EQ-5D-5 L), Lawton scale of instrumental activities of daily living score, reperfusion quality (MeVO expanded Thrombolysis in Cerebral Infarction Score) and infarct volume at 24 h, and cost-effectiveness of endovascular recanalization. Safety outcomes include symptomatic and asymptomatic intracranial hemorrhage and procedural complications. DISCUSSION: The ESCAPE-MeVO trial will demonstrate the effect of endovascular thrombectomy in addition to best medical management vis-à-vis best medical management in patients with acute ischemic stroke due to MeVO and provide data for evidence-based treatment decision-making in acute MeVO stroke. DATA ACCESS STATEMENT: The raw data discussed in this mansucript will be made available by the corresponding author upon reasonable request.

Sex-Specific Differences in Patients with IDH1-Wild-Type Grade 4 Glioma in the ReSPOND Consortium.

BACKGROUND AND PURPOSE: Understanding sex-based differences in patients with glioblastoma is necessary for accurate personalized treatment planning to improve patient outcomes. Our purpose was to investigate sex-specific differences in molecular, clinical, and radiologic tumor parameters, as well as survival outcomes in patients with glioblastoma, isocitrate dehydrogenase-1 wild-type (IDH1-WT), grade 4. MATERIALS AND METHODS: Retrospective data of 1832 patients with glioblastoma, IDH1-WT with comprehensive information on tumor parameters was acquired from the Radiomics Signatures for Precision Oncology in Glioblastoma consortium. Data imputation was performed for missing values. Sex-based differences in tumor parameters, such as age, molecular parameters, preoperative Karnofsky performance score (KPS), tumor volumes, epicenter, and laterality were assessed through nonparametric tests. Spatial atlases were generated by using preoperative MRI maps to visualize tumor characteristics. Survival time analysis was performed through log-rank tests and Cox proportional hazard analyses. RESULTS: Glioblastoma was diagnosed at a median age of 64 years in women compared with 61.9 years in men (false discovery rate [FDR] = 0.003). Men had a higher KPS (above 80) as compared with women (60.4% women versus 69.7% men, FDR = 0.044). Women had lower tumor volumes in enhancing (16.7 cm3 versus 20.6 cm3 in men, FDR = 0.001), necrotic core (6.18 cm3 versus 7.76 cm3 in men, FDR = 0.001), and edema regions (46.9 cm3 versus 59.2 cm3 in men, FDR = 0.0001). The right temporal region was the most common tumor epicenter in the overall population. Right as well as left temporal lobes were more frequently involved in men. There were no sex-specific differences in survival outcomes and mortality ratios. Higher age, unmethylated O6-methylguanine-DNA-methyltransferase promoter and undergoing subtotal resection increased the mortality risk in both men and women. CONCLUSIONS: Our study demonstrates significant sex-based differences in clinical and radiologic tumor parameters of patients with glioblastoma. Sex is not an independent prognostic factor for survival outcomes and the tumor parameters influencing patient outcomes are identical for men and women.