Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy.

Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness.

BACKGROUND: In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. METHODS: We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome. RESULTS: Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]). CONCLUSIONS: Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

The Neurogenome study: Comprehensive molecular profiling to optimize treatment for Danish glioblastoma patients.

Background: Glioblastoma is an aggressive brain cancer with no possibility for cure. Treatment and survival have only improved slightly since 2005 when the current regime was implemented. The limited improvements in the treatment of glioblastoma may reflect our poor understanding of the disease. We hypothesize that systematically collected translational data will improve knowledge and hereby treatment. Methods: We have been performing whole exome sequencing in glioblastoma tumor tissue since 2016 and whole genome sequencing (WGS) since 2020 with the aim of offering experimental treatment. Results: We have sequenced 400+ GBM patients and from these 100+ are paired tumor samples from relapse surgery. To develop genomic profiling and to increase the information on each patient´s contribution, we have initiated the Neurogenome study as of June 2022. The Neurogenome protocol is a national, comprehensive, translational, and omic protocol. It is a continuation of 2 previous protocols from 2016 and forth in our department, but with more substudies added, focusing on the translational and clinical utility. We collect and analyze data from an out-patient clinic in a systematic approach to a number of subprojects ranging from basic science to applied clinical science, including clinical trials. Conclusions: The protocol will act as a backbone for future projects in the national research center, Danish Comprehensive Cancer Center-Brain Tumor Center with the overall aim to select eligible patients for experimental treatment based upon genomic alterations. The article will present the Neurogenome setup and a presentation of selected projects that are based upon inclusion.

3D Ultrasound and MRI in Assessing Resection Margins during Tongue Cancer Surgery: A Research Protocol for a Clinical Diagnostic Accuracy Study.

Surgery is the primary treatment for tongue cancer. The goal is a complete resection of the tumor with an adequate margin of healthy tissue around the tumor.Inadequate margins lead to a high risk of local cancer recurrence and the need for adjuvant therapies. Ex vivo imaging of the resected surgical specimen has been suggested for margin assessment and improved surgical results. Therefore, we have developed a novel three-dimensional (3D) ultrasound imaging technique to improve the assessment of resection margins during surgery. In this research protocol, we describe a study comparing the accuracy of 3D ultrasound, magnetic resonance imaging (MRI), and clinical examination of the surgical specimen to assess the resection margins during cancer surgery. Tumor segmentation and margin measurement will be performed using 3D ultrasound and MRI of the ex vivo specimen. We will determine the accuracy of each method by comparing the margin measurements and the proportion of correctly classified margins (positive, close, and free) obtained by each technique with respect to the gold standard histopathology.

Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI.

Introduction: Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations. Methods: We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment. Results: We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model. Conclusion: In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation.

Quantification of cross-vendor variation in ADC measurements in vendor-specific prostate MRI-protocols.

PURPOSE: The purpose of this study was to quantify the variability of Apparent Diffusion Coefficient (ADC) and test if there were statistically significant differences in ADC between MRI systems and sequences. METHOD: With a two-chamber cylindrical ADC phantom with fixed ADC values (1,000 and 1,600x10-6 mm2/s) a single-shot (ss) Echo Planar Imaging (EPI), a multi-shot EPI, a reduced field of view DWI (zoom) and a Turbo Spin Echo DWI sequence were tested in six MRI systems from three vendors at 1.5 T and 3 T. Technical parameters were according to Prostate Imaging Reporting and Data System Version 2.1. ADC maps were calculated by vendor specific algorithms. Absolute and relative differences in ADC from the phantom-ADC were calculated and differences between sequences were tested. RESULTS: At 3 T absolute differences from phantom given ADC (∼1,000 and âˆ¼ 1,600x10-6 mm2/s) were -83 - 42x10-6 mm2/s (-8.3%-4.2%) and -48 - 15x10-6 mm2/s (-3%-0.9%), respectively and at 1.5 T absolute differences were -81 - 26x10-6 mm2/s (-2.6%-8.1%) and -74 - 67x10-6 mm2/s (-4.6%-4.2%), respectively. Significant statistical differences in ADC measurements were identified between vendors in all sequences except for ssEPI and zoom at 3 T in the 1,600x10-6 mm2/s phantom chamber. Significant differences were also identified between ADC measurements at 1.5 T and 3 T in some of the sequences and vendors, but not all. CONCLUSION: The variation of ADC between different MRI systems and prostate specific DWI sequences is limited in this phantom study and without apparent clinical relevance. However, prospective multicenter studies of prostate cancer patients are needed for further investigation.

Effect of aerobic exercise training on the fat fraction of the liver in persons with chronic hepatitis B and hepatic steatosis: Trial protocol for a randomized controlled intervention trial- The FitLiver study.

BACKGROUND: The global prevalence of chronic hepatitis B is more than 300 million people, and in Denmark, 17,000 people are estimated to have chronic hepatitis B. Untreated, chronic hepatitis B can lead to the development of liver cirrhosis and liver cancer. There is no curable therapy. In persons with obesity and chronic hepatitis B infection, the development of hepatic steatosis imposes a double burden on the liver, leading to an increased risk of cirrhosis and liver cancer. In patients without chronic hepatitis B, exercise interventions have shown beneficial effects on hepatic steatosis through improvements in fat fraction of the liver, insulin resistance, fatty acid metabolism, and glucose metabolism, as well as activation of liver-induced regulatory protein secretion (hepatokines) after the exercise intervention. OBJECTIVE: To investigate in persons with chronic hepatitis B and hepatic steatosis: Primary: Whether exercise will decrease the fat fraction of the liver. Secondary: If exercise will affect hepatokine secretion and if it will improve lipid- and glucose metabolism, liver status, markers of inflammation, body composition, and blood pressure. METHODS: A randomized, controlled, clinical intervention trial consisting of 12 weeks of aerobic exercise training or no intervention. Thirty persons with chronic hepatitis B and hepatic steatosis will be randomized 1:1. Before and after the intervention, participants will undergo an MRI scan of the liver, blood sampling, oral glucose tolerance test, fibroscan, VO2max test, DXA scan, blood pressure measurements, and optional liver biopsy. Lastly, a hormone infusion test with somatostatin and glucagon to increase the glucagon/insulin ratio for stimulating secretion of circulating hepatokines will be performed. The training program includes three weekly training sessions of 40 min/session over 12 weeks. DISCUSSION: This trial, investigating high-intensity interval training in persons with chronic hepatitis B and hepatic steatosis, is the first exercise intervention trial performed on this group of patients. If exercise reduces hepatic steatosis and induces other beneficial effects of clinical markers in this group of patients, there might be an indication to recommend exercise as part of treatment. Furthermore, the investigation of the effect of exercise on hepatokine secretion will provide more knowledge on the effects of exercise on the liver. TRIAL REGISTRATION: Danish Capital Regions committee on health research ethics reference: H-21034236 (version 1.4 date: 19-07-2022) and ClinicalTrials.gov: NCT05265026.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI.

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Timing of Early Postoperative MRI following Primary Glioblastoma Surgery-A Retrospective Study of Contrast Enhancements in 311 Patients.

An early postoperative MRI is recommended following Glioblastoma surgery. This retrospective, observational study aimed to investigate the timing of an early postoperative MRI among 311 patients. The patterns of the contrast enhancement (thin linear, thick linear, nodular, and diffuse) and time from surgery to the early postoperative MRI were recorded. The primary endpoint was the frequencies of the different contrast enhancements within and beyond the 48-h from surgery. The time dependence of the resection status and the clinical parameters were analysed as well. The frequency of the thin linear contrast enhancements significantly increased from 99/183 (50.8%) within 48-h post-surgery to 56/81 (69.1%) beyond 48-h post-surgery. Similarly, MRI scans with no contrast enhancements significantly declined from 41/183 (22.4%) within 48-h post-surgery to 7/81 (8.6%) beyond 48-h post-surgery. No significant differences were found for the other types of contrast enhancements and the results were robust in relation to the choice of categorisation of the postoperative periods. Both the resection status and the clinical parameters were not statistically different in patients with an MRI performed before and after 48 h. The findings suggest that surgically induced contrast enhancements are less frequent when an early postoperative MRI is performed earlier than 48-h, supporting the recommendation of a 48-h window for an early postoperative MRI.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

An Automatic DWI/FLAIR Mismatch Assessment of Stroke Patients.

DWI/FLAIR mismatch assessment for ischemic stroke patients shows promising results in determining if patients are eligible for recombinant tissue-type plasminogen activator (r-tPA) treatment. However, the mismatch criteria suffer from two major issues: binary classification of a non-binary problem and the subjectiveness of the assessor. In this article, we present a simple automatic method for segmenting stroke-related parenchymal hyperintensities on FLAIR, allowing for an automatic and continuous DWI/FLAIR mismatch assessment. We further show that our method's segmentations have comparable inter-rater agreement (DICE 0.820, SD 0.12) compared to that of two neuro-radiologists (DICE 0.856, SD 0.07), that our method appears robust to hyper-parameter choices (suggesting good generalizability), and lastly, that our methods continuous DWI/FLAIR mismatch assessment correlates to mismatch assessments made for a cohort of wake-up stroke patients at hospital submission. The proposed method shows promising results in automating the segmentation of parenchymal hyperintensity within ischemic stroke lesions and could help reduce inter-observer variability of DWI/FLAIR mismatch assessment performed in clinical environments as well as offer a continuous assessment instead of the current binary one.

A deep learning-based whole-body solution for PET/MRI attenuation correction.

BACKGROUND: Deep convolutional neural networks have demonstrated robust and reliable PET attenuation correction (AC) as an alternative to conventional AC methods in integrated PET/MRI systems. However, its whole-body implementation is still challenging due to anatomical variations and the limited MRI field of view. The aim of this study is to investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI and use it as a whole-body solution for PET AC in a PET/MRI system. MATERIALS AND METHODS: Fifteen patients underwent PET/CT followed by PET/MRI with whole-body coverage from skull to feet. We performed MRI truncation correction and employed co-registered MRI and CT images for training and leave-one-out cross-validation. The network was pretrained with region-specific images. The accuracy of the AC maps and reconstructed PET images were assessed by performing a voxel-wise analysis and calculating the quantification error in SUV obtained using DL-based sCT (PETsCT) and a vendor-provided atlas-based method (PETAtlas), with the CT-based reconstruction (PETCT) serving as the reference. In addition, region-specific analysis was performed to compare the performances of the methods in brain, lung, liver, spine, pelvic bone, and aorta. RESULTS: Our DL-based method resulted in better estimates of AC maps with a mean absolute error of 62 HU, compared to 109 HU for the atlas-based method. We found an excellent voxel-by-voxel correlation between PETCT and PETsCT (R2 = 0.98). The absolute percentage difference in PET quantification for the entire image was 6.1% for PETsCT and 11.2% for PETAtlas. The regional analysis showed that the average errors and the variability for PETsCT were lower than PETAtlas in all regions. The largest errors were observed in the lung, while the smallest biases were observed in the brain and liver. CONCLUSIONS: Experimental results demonstrated that a DL approach for whole-body PET AC in PET/MRI is feasible and allows for more accurate results compared with conventional methods. Further evaluation using a larger training cohort is required for more accurate and robust performance.

Can preoperative brain imaging features predict shunt response in idiopathic normal pressure hydrocephalus? A PRISMA review.

PURPOSE: This systematic literature review aimed to identify brain computed tomography (CT) and magnetic resonance imaging (MRI) features that could be used to discriminate idiopathic normal pressure hydrocephalus (iNPH) shunt responders from non-responders. METHODS: PubMed, Embase, Web of Science, and Cochrane were searched following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles reporting preoperative CT and/or MRI features and iNPH shunt response evaluated by changes in gait, dementia, and urinary incontinence were included. Title and abstract screening and full-text article evaluation were done by two authors. Data on patient demographics and inclusion criteria, brain image evaluation, shunting methods, and shunt response evaluation were recorded. RESULTS: The search resulted in 1274 studies after removing duplicates. Twenty-seven studies were chosen for final review. Both structural (i.e., callosal angle, disproportionately enlarged subarachnoid space hydrocephalus (DESH), and temporal horn diameter) and physiological brain imaging (including aqueductal flow measurement and brain perfusion) had been examined. Fourteen out of 27 studies found no difference in any assessed imaging parameters between responders and non-responders, and none of the examined imaging parameters was repeatedly and consistently reported as significantly different between the two groups. CONCLUSIONS: No brain imaging parameters were consistently and repeatedly reported as different between iNPH shunt responders and non-responders.

Causes and Risk Factors of Pediatric Spontaneous Intracranial Hemorrhage-A Systematic Review.

Previous studies suggest that the most common cause of spontaneous intracerebral hemorrhage in children and adolescents is arteriovenous malformations (AVMs). However, an update containing recently published data on pediatric spontaneous intracranial hemorrhages is lacking. The aim of this study is to systematically analyze the published data on the etiologies and risk factors of pediatric spontaneous intracranial hemorrhage. This systematic review was performed in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A search in PubMed, Embase, Scopus, Web of Science and Cochrane Library was conducted aiming for articles published in year 2000 and later, containing data on etiology and risk factors of spontaneous intracranial hemorrhages in unselected cohorts of patients aged between 1 month and 18 years. As a result, forty studies were eligible for data extraction and final analysis. These included 7931 children and adolescents with 4009 reported etiologies and risk factors. A marked variety of reported etiologies and risk factors among studies was observed. Vascular etiologies were the most frequently reported cause of pediatric spontaneous intracranial hemorrhages (n = 1727, 43.08% of all identified etiologies or risk factors), with AVMs being the most common vascular cause (n = 1226, 70.99% of all vascular causes). Hematological and systemic causes, brain tumors, intracranial infections and cardiac causes were less commonly encountered risk factors and etiologies.

An fMRI-compatible system for targeted electrical stimulation.

BACKGROUND: Neuromodulation is a rapidly expanding therapeutic option considered within neuropsychiatry, pain and rehabilitation therapy. Combining electrostimulation with feedback from fMRI can provide information about the mechanisms underlying the therapeutic effects, but so far, such studies have been hampered by the lack of technology to conduct safe and accurate experiments. Here we present a system for fMRI compatible electrical stimulation, and the first proof-of-concept neuroimaging data with deep brain stimulation (DBS) in pigs obtained with the device. NEW METHOD: The system consists of two modules, placed in the control and scanner room, connected by optical fiber. The system also connects to the MRI scanner to timely initiate the stimulation sequence at start of scan. We evaluated the system in four pigs with DBS in the subthalamic nucleus (STN) while we acquired BOLD responses in the STN and neocortex. RESULTS: We found that the system delivered robust electrical stimuli to the implanted electrode in sync with the preprogrammed fMRI sequence. All pigs displayed a DBS-STN induced neocortical BOLD response, but none in the STN. COMPARISONS WITH EXISTING METHOD: The system solves three major problems related to electric stimuli and fMRI examinations, namely preventing distortion of the fMRI signal, enabling communication that synchronize the experimental conditions, and surmounting the safety hazards caused by interference with the MRI scanner. CONCLUSIONS: The fMRI compatible electrical stimulator circumvents previous problems related to electroceuticals and fMRI. The system allows flexible modifications for fMRI designs and stimulation parameters, and can be customized to electroceutical applications beyond DBS.

Surgically Induced Contrast Enhancements on Intraoperative and Early Postoperative MRI Following High-Grade Glioma Surgery: A Systematic Review.

For the radiological assessment of resection of high-grade gliomas, a 72-h diagnostic window is recommended to limit surgically induced contrast enhancements. However, such enhancements may occur earlier than 72 h post-surgery. This systematic review aimed to assess the evidence on the timing of the postsurgical MRI. PubMed, Embase, Web of Science and Cochrane were searched following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles describing surgically induced contrast enhancements on MRI after resection for high-grade gliomas were included and analysed. The frequency of different contrast enhancement patterns on intraoperative MRI (iMRI) and early postoperative MRI (epMRI) was recorded. The search resulted in 1443 studies after removing duplicates, and a total of 12 studies were chosen for final review. Surgically induced contrast enhancements were reported at all time points after surgery, including on iMRI, but their type and frequency vary. Thin linear contrast enhancements were commonly found to be surgically induced and were less frequently recorded on postoperative days 1 and 2. This suggests that the optimal time to scan may be at or before this time. However, the evidence is limited, and higher-quality studies using larger and consecutively sampled populations are needed.

[MR-guided laser interstitial thermal therapy in the treatment of brain tumours and epilepsy].

MR-guided laser interstitial thermal therapy (LITT) is a minimally invasive neurosurgical procedure, which in the last decade has gained significant momentum of the treatment of intracranial tumours and epileptic foci. In brief, LITT utilises the heat from a stereotactically placed laser catheter to selectively ablate a lesion or a structure under real-time MRI guidance, which is summarised and discussed in this review. The first LITT system gained FDA approval in 2007 and was CE-marked in 2018. In December 2020, the first patient with recurrent glioblastoma was treated at the Department of Neurosurgery at Rigshospitalet, Copenhagen.

Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients.

BACKGROUND: Radiotherapy (RT) planning for cervical cancer patients entails the acquisition of both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Further, molecular imaging by Positron Emission Tomography (PET) could contribute to target volume delineation as well as treatment response monitoring. The objective of this study was to investigate the feasibility of a PET/MRI-only RT planning workflow of patients with cervical cancer. This includes attenuation correction (AC) of MRI hardware and dedicated positioning equipment as well as evaluating MRI-derived synthetic CT (sCT) of the pelvic region for positioning verification and dose calculation to enable a PET/MRI-only setup. MATERIAL AND METHODS: 16 patients underwent PET/MRI using a dedicated RT setup after the routine CT (or PET/CT), including eight pilot patients and eight cervical cancer patients who were subsequently referred for RT. Data from 18 patients with gynecological cancer were added for training a deep convolutional neural network to generate sCT from Dixon MRI. The mean absolute difference between the dose distributions calculated on sCT and a reference CT was measured in the RT target volume and organs at risk. PET AC by sCT and a reference CT were compared in the tumor volume. RESULTS: All patients completed the examination. sCT was inferred for each patient in less than 5 s. The dosimetric analysis of the sCT-based dose planning showed a mean absolute error (MAE) of 0.17 ± 0.12 Gy inside the planning target volumes (PTV). PET images reconstructed with sCT and CT had no significant difference in quantification for all patients. CONCLUSIONS: These results suggest that multiparametric PET/MRI can be successfully integrated as a one-stop-shop in the RT workflow of patients with cervical cancer.

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature.

The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74-0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.

Urokinase-Type Plasminogen Activator Receptor (uPAR) PET/MRI of Prostate Cancer for Noninvasive Evaluation of Aggressiveness: Comparison with Gleason Score in a Prospective Phase 2 Clinical Trial.

The aim of this study was to evaluate the correlation between uptake of the PET ligand 68Ga-NOTA-AE105, targeting the urokinase-type plasminogen activator receptor (uPAR), and Gleason score in patients undergoing prostate biopsy. Methods: Patients with clinical suspicion of prostate cancer (PCa) or previously diagnosed with PCa were prospectively enrolled in this phase 2 trial. A combination of uPAR PET and multiparametric MRI (mpMRI) was performed, and the SUV in the primary tumor, as delineated by mpMRI, was measured by 2 independent readers. The correlation between the SUV and the Gleason score obtained by biopsy was assessed. Results: A total of 27 patients had histologically verified PCa visible on mpMRI and constituted the study population. There was a positive correlation between the SUVmax and the Gleason score (Spearman ρ = 0.55; P = 0.003). Receiver operating characteristic analysis showed an area under the curve of 0.88 (95% CI, 0.67-1.00) for discriminating a Gleason score of greater than or equal to 3 + 4 from a Gleason score of less than or equal to 3 + 3. A cutoff for the tumor SUVmax could be established with a sensitivity of 96% (79%-99%) and a specificity of 75% (30%-95%) for detecting a Gleason score of greater than or equal to 3 + 4. For discriminating a Gleason score of greater than or equal to 4 + 3 from a Gleason score of less than or equal to 3 + 4, a cutoff could be established for detecting a Gleason score of greater than or equal to 4 + 3 with a sensitivity of 93% (69%-99%) and a specificity of 62% (36%-82%). Conclusion: SUV measurements from uPAR PET in primary tumors, as delineated by mpMRI, showed a significant correlation with the Gleason score, and the tumor SUVmax was able to discriminate between low-risk Gleason score profiles and intermediate risk Gleason score profiles with a high diagnostic accuracy. Consequently, uPAR PET/MRI could be a promising method for the noninvasive evaluation of PCa and might reduce the need for repeated biopsies (e.g., in active surveillance).