Targeted therapies in retinoblastoma: GD2-directed immunotherapy following autologous stem cell transplantation and evaluation of alternative target B7-H3.

BACKGROUND: GD2-directed immunotherapy is highly effective in the treatment of high-risk neuroblastoma (NB), and might be an interesting target also in other high-risk tumors. METHODS: The German-Austrian Retinoblastoma Registry, Essen, was searched for patients, who were treated with anti-GD2 monoclonal antibody (mAb) dinutuximab beta (Db) in order to evaluate toxicity, response and outcome in these patients. Additionally, we evaluated anti-GD2 antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) in retinoblastoma cell lines in vitro. Furthermore, in vitro cytotoxicity assays directed against B7-H3 (CD276), a new identified potential target in RB, were performed. RESULTS: We identified four patients with relapsed stage IV retinoblastoma, who were treated with Db following autologous stem cell transplantation (ASCT). Two out of two evaluable patients with detectable tumors responded to immunotherapy. One of these and another patient who received immunotherapy without residual disease relapsed 10 and 12 months after start of Db. The other patients remained in remission until last follow-up 26 and 45 months, respectively. In vitro, significant lysis of RB cell lines by ADCC and CDC with samples from patients and healthy donors and anti-GD2 and anti-CD276-mAbs were demonstrated. CONCLUSION: Anti-GD2-directed immunotherapy represents an additional therapeutic option in high-risk metastasized RB. Moreover, CD276 is another target of interest.

Evaluation of the contribution of individual arteries to the cerebral blood supply in patients with Moyamoya angiopathy: comparison of vessel-encoded arterial spin labeling and digital subtraction angiography.

PURPOSE: Vessel-encoded arterial spin labeling (VE-ASL) is able to provide noninvasive information about the contribution of individual arteries to the cerebral perfusion. The aim of this study was to compare VE-ASL to the diagnostic standard digital subtraction angiography (DSA) with respect to its ability to visualize vascular territories. METHODS: In total, 20 VE-ASL and DSA data sets of 17 patients with Moyamoya angiopathy with and without revascularization surgery were retrospectively analyzed. Two neuroradiologists independently assessed the agreement between VE-ASL and DSA using a 4-point Likert scale (no- very high agreement). Additionally, grading of the vascular supply of subterritories (A1-A2, M1-M6) on the VE-ASL images and angiograms was performed. The intermodal agreement was calculated for all subterritories in total and for the subdivision into without and after revascularization (direct or indirect bypass). RESULTS: There was a very high agreement between the VE-ASL and the DSA data sets (median = 1, modus = 1) with a substantial inter-rater agreement (kw = 0.762 (95% CI 0.561-0.963)). The inter-modality agreement between VE-ASL and DSA in vascular subterritories was almost perfect for all subterritories (k = 0.899 (0.865-0.945)), in the subgroup of direct revascularized subterritories (k = 0.827 (0.738-0.915)), in the subgroup of indirect revascularized subterritories (k = 0.843 (0.683-1.003)), and in the subgroup of never revascularized subterritories (k = 0.958 (0.899-1.017)). CONCLUSION: Vessel-encoded ASL seems to be a promising non-invasive method to depict the contributions of individual arteries to the cerebral perfusion before and after revascularization surgery.

Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality.

INTRODUCTION: Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence. MATERIAL AND METHODS: This retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale. RESULTS: Accelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging. CONCLUSION: In conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

Evaluation of the cerebrovascular reactivity in patients with Moyamoya Angiopathy by use of breath-hold fMRI: investigation of voxel-wise hemodynamic delay correction in comparison to [15O]water PET.

PURPOSE: Patients with Moyamoya Angiopathy (MMA) require hemodynamic assessment to evaluate the risk of stroke. Hemodynamic evaluation by use of breath-hold-triggered fMRI (bh-fMRI) was proposed as a readily available alternative to the diagnostic standard [15O]water PET. Recent studies suggest voxel-wise hemodynamic delay correction in hypercapnia-triggered fMRI. The aim of this study was to evaluate the effect of delay correction of bh-fMRI in patients with MMA and to compare the results with [15O]water PET. METHODS: bh-fMRI data sets of 22 patients with MMA were evaluated without and with voxel-wise delay correction within different shift ranges and compared to the corresponding [15O]water PET data sets. The effects were evaluated combined and in subgroups of data sets with most severely impaired CVR (apparent steal phenomenon), data sets with territorial time delay, and data sets with neither steal phenomenon nor delay between vascular territories. RESULTS: The study revealed a high mean cross-correlation (r = 0.79, p < 0.001) between bh-fMRI and [15O]water PET. The correlation was strongly dependent on the choice of the shift range. Overall, no shift range revealed a significantly improved correlation between bh-fMRI and [15O]water PET compared to the correlation without delay correction. Delay correction within shift ranges with positive high high cutoff revealed a lower agreement between bh-fMRI and PET overall and in all subgroups. CONCLUSION: Voxel-wise delay correction, in particular with shift ranges with high cutoff, should be used critically as it can lead to false-negative results in regions with impaired CVR and a lower correlation to the diagnostic standard [15O]water PET.

Hemodynamic evaluation of patients with Moyamoya Angiopathy: comparison of resting-state fMRI to breath-hold fMRI and [15O]water PET.

PURPOSE: Patients with Moyamoya Angiopathy (MMA) require hemodynamic evaluation to assess the risk of stroke. Assessment of cerebral blood flow with [15O]water PET and acetazolamide challenge is the diagnostic standard for the evaluation of the cerebral perfusion reserve (CPR). Estimation of the cerebrovascular reactivity (CVR) by use of breath-hold-triggered fMRI (bh-fMRI) as an index of CPR has been proposed as a reliable and more readily available approach. Recent findings suggest the use of resting-state fMRI (rs-fMRI) which requires minimum patient compliance. The aim of this study was to compare rs-fMRI to bh-fMRI and [15O]water PET in patients with MMA. METHODS: Patients with MMA underwent rs-fMRI and bh-fMRI in the same MRI session. Maps of the CVR gained by both modalities were compared retrospectively by calculating the correlation between the mean CVR of 12 volumes of interest. Additionally, the rs-maps of a subgroup of patients were compared to CPR-maps gained by [15O]water PET. RESULTS: The comparison of the rs-maps and the bh-maps of 24 patients revealed a good correlation (Pearson's r = 0.71 ± 0.13; preoperative patients: Pearson's r = 0.71 ± 0.17; postoperative patients: Pearson's r = 0.71 ± 0.11). The comparison of 7 rs-fMRI data sets to the corresponding [15O]water PET data sets also revealed a high level of agreement (Pearson's r = 0.80 ± 0.19). CONCLUSION: The present analysis indicates that rs-fMRI might be a promising non-invasive method with almost no patient cooperation needed to evaluate the CVR. Further prospective studies are required.

Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality.

RATIONALE AND OBJECTIVES: Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIRDLR) sequence of the brain compared to conventional (standard) FLAIR (FLAIRS) imaging. MATERIALS AND METHODS: Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIRDLR was conducted using the same MRI acquisition parameters as the FLAIRS sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39 minute instead of 2:40 minutes (-38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed. RESULTS: The average age of the patients was 63 ± 11years. FLAIRDLR exhibited significantly less image noise than FLAIRS, with P-values of< .001 and< .05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIRDLR, with a median score of 4 compared to a median score of 3 in FLAIRS (P-values of<.001 for both readers). In terms of overall image quality, FLAIRDLR was rated superior to FLAIRS, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIRDLR in 68/70 cases. CONCLUSION: The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation.

Cerebrovascular Reactivity Assessed by Breath-Hold Functional MRI in Patients with Neurological Post-COVID-19 Syndrome-A Pilot Study.

Endothelial dysfunction represents a potential pathomechanism of neurological post-COVID-19 syndrome (PCS). A recent study demonstrated reduced cerebrovascular reactivity (CVR) in patients with PCS. The aim of this pilot study was to prospectively assess CVR in patients with PCS using breath-hold functional MRI (bh-fMRI). Fourteen patients with neurological PCS and leading symptoms of fatigue/memory issues/concentration disorder (PCSfmc), 11 patients with PCS and leading symptoms of myopathy/neuropathy (PCSmn), and 17 healthy controls underwent bh-fMRI. Signal change and time to peak (TTP) were assessed globally and in seven regions of interest and compared between the subgroups using one-way ANCOVA adjusting for age, time since infection, Fazekas score, and sex. No significant differences were observed. In PCS patients, the global CVR exhibited a slight, non-significant tendency to be lower compared to healthy controls (PCSfmc: 0.78 ± 0.11%, PCSmn: 0.84 ± 0.10% and 0.87 ± 0.07%). There was a non-significant trend towards lower global TTP values in the PCS subgroups than in the control group (PCSfmc: 26.41 ± 1.39 s, PCSmn: 26.32 ± 1.36 s versus 29.52 ± 0.93 s). Endothelial dysfunction does not seem to be the sole pathomechanism of neurological symptoms in PCS. Further studies in larger cohorts are required.

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT). However, the associated ionizing radiation poses long-term risks. Modern artificial intelligence reconstruction algorithms have shown promise in reducing radiation dose while maintaining image quality. Therefore, we aimed to evaluate the dose reduction capabilities of a deep learning-based denoising (DLD) algorithm in traumatic neuroradiological emergency CT scans. MATERIALS AND METHODS: This retrospective single-center study included 100 patients with neuroradiological trauma CT scans. Full-dose (100%) and low-dose (25%) simulated scans were processed using iterative reconstruction (IR2) and DLD. Subjective and objective image quality assessments were performed by four neuroradiologists alongside clinical endpoint analysis. Bayesian sensitivity and specificity were computed with 95% credible intervals. RESULTS: Subjective analysis showed superior scores for 100% DLD compared to 100% IR2 and 25% IR2 (p < 0.001). No significant differences were observed between 25% DLD and 100% IR2. Objective analysis revealed no significant CT value differences but higher noise at 25% dose for DLD and IR2 compared to 100% (p < 0.001). DLD exhibited lower noise than IR2 at both dose levels (p < 0.001). Clinical endpoint analysis indicated equivalence to 100% IR2 in fracture detection for all datasets, with sensitivity losses in hemorrhage detection at 25% IR2. DLD (25% and 100%) maintained comparable sensitivity to 100% IR2. All comparisons demonstrated robust specificity. CONCLUSIONS: The evaluated algorithm enables high-quality, fully diagnostic CT scans at 25% of the initial radiation dose and improves patient care by reducing unnecessary radiation exposure.

Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical-pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality.

BACKGROUND AND PURPOSE: This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging. METHODS: In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES ) and DL TSE sequences (TSEDL ) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale. RESULTS: TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging. CONCLUSION: The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

MRI-Based Assessment of Risk for Stroke in Moyamoya Angiopathy (MARS-MMA): An MRI-Based Scoring System for the Severity of Moyamoya Angiopathy.

Before revascularization, moyamoya patients require hemodynamic evaluation. In this study, we evaluated the scoring system Prior Infarcts, Reactivity and Angiography in Moyamoya Disease (PIRAMID). We also devised a new scoring system, MRI-Based Assessment of Risk for Stroke in Moyamoya Angiopathy (MARS-MMA), and compared the scoring systems with respect to the capability to predict impaired [15O]water PET cerebral perfusion reserve capacity (CPR). We evaluated 69 MRI, 69 DSA and 38 [15O]water PET data sets. The PIRAMID system was validated by ROC curve analysis with neurological symptomatology as a dependent variable. The components of the MARS-MMA system and their weightings were determined by binary logistic regression analysis. The comparison of PIRAMID and MARS-MMA was performed by ROC curve analysis. The PIRAMID score correlated well with the symptomatology (AUC = 0.784). The MARS-MMA system, including impaired breath-hold-fMRI, the presence of the Ivy sign and arterial wall contrast enhancement, correlated slightly better with CPR impairment than the PIRAMID system (AUC = 0.859 vs. 0.827, Akaike information criterion 140 vs. 146). For simplified clinical use, we determined three MARS-MMA grades without loss of diagnostic performance (AUC = 0.855). The entirely MRI-based MARS-MMA scoring system might be a promising tool to predict the risk of stroke.

Glioma Type Prediction with Dynamic Contrast-Enhanced MR Imaging and Diffusion Kurtosis Imaging-A Standardized Multicenter Study.

The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification.

Breath-Hold-Triggered BOLD fMRI in Drug-Resistant Nonlesional Focal Epilepsy-A Pilot Study.

PURPOSE: Individuals with drug-resistant epilepsy may benefit from epilepsy surgery. In nonlesional cases, where no epileptogenic lesion can be detected on structural magnetic resonance imaging, multimodal neuroimaging studies are required. Breath-hold-triggered BOLD fMRI (bh-fMRI) was developed to measure cerebrovascular reactivity in stroke or angiopathy and highlights regional network dysfunction by visualizing focal impaired flow increase after vasodilatory stimulus. This regional dysfunction may correlate with the epileptogenic zone. In this prospective single-center single-blind pilot study, we aimed to establish the feasibility and safety of bh-fMRI in individuals with drug-resistant non-lesional focal epilepsy undergoing presurgical evaluation. METHODS: In this prospective study, 10 consecutive individuals undergoing presurgical evaluation for drug-resistant focal epilepsy were recruited after case review at a multidisciplinary patient management conference. Electroclinical findings and results of other neuroimaging were used to establish the epileptogenic zone hypothesis. To calculate significant differences in cerebrovascular reactivity in comparison to the normal population, bh-fMRIs of 16 healthy volunteers were analyzed. The relative flow change of each volume of interest (VOI) of the atlas was then calculated compared to the flow change of the whole brain resulting in an atlas of normal cerebral reactivity. Consequently, the mean flow change of every VOI of each patient was tested against the healthy volunteers group. Areas with significant impairment of cerebrovascular reactivity had decreased flow change and were compared to the epileptogenic zone localization hypothesis in a single-blind design. RESULTS: Acquisition of bh-fMRI was feasible in 9/10 cases, with one patient excluded due to noncompliance with breathing maneuvers. No adverse events were observed, and breath-hold for intermittent hypercapnia was well tolerated. On blinded review, we observed full or partial concordance of the local network dysfunction seen on bh-fMRI with the electroclinical hypothesis in 6/9 cases, including cases with extratemporal lobe epilepsy and those with nonlocalizing 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). CONCLUSION: This represents the first report of bh-fMRI in individuals with epilepsy undergoing presurgical evaluation. We found bh-fMRI to be feasible and safe, with a promising agreement to electroclinical findings. Thus, bh-fMRI may represent a potential modality in the presurgical evaluation of epilepsy. Further studies are needed to establish clinical utility.