Aaron Michael Rulseh

Gadolinium-based contrast agents (GBCA) were introduced with high expectations for favorable efficacy, low nephrotoxicity, and minimal allergic-like reactions. Nephrogenic systemic fibrosis and proven gadolinium retention in the body including the brain has led to the restriction of linear GBCAs and a more prudent approach regarding GBCA indication and dosing. In this review, we present the chemical, physical, and clinical aspects of this topic and aim to provide an equanimous and comprehensive summary of contemporary knowledge with a perspective of the future. In the first part of the review, we present various elements and compounds that may serve as MRI contrast agents. Several GBCAs are further discussed with consideration of their relaxivity, chelate structure, and stability. Gadolinium retention in the brain is explored including correlation with the presence of metalloprotein ferritin in the same regions where visible hyperintensity on unenhanced T1-weighted imaging occurs. Proven interaction between ferritin and gadolinium released from GBCAs is introduced and discussed, as well as the interaction of other elements with ferritin; and manganese in patients with impaired liver function or calcium in Fahr disease. We further present the concept that only high-molecular-weight forms of gadolinium can likely visibly change signal intensity on unenhanced T1-weighted imaging. Clinical data are also presented with respect to potential neurological manifestations originating from the deep-brain nuclei. Finally, new contrast agents with relatively high relaxivity and stability are introduced. Critical relevance statement: GBCA may accumulate in the brain, especially in ferritin-rich areas; however, no adverse neurological manifestations have been detected in relation to gadolinium retention. Key Points: Gadolinium currently serves as the basis for MRI contrast agents used clinically. No adverse neurological manifestations have been detected in relation to gadolinium retention. Future contrast agents must advance chelate stability and relativity, facilitating lower doses. Graphical

Objectives: Accurate detection of metastatic brain lesions (MBL) is critical due to advances in radiosurgery. We compared the results of three readers in detecting MBL using T1-weighted 2D spin echo (SE) and sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) sequences with whole-brain coverage at both 1.5 T and 3 T. Methods: Fifty-six patients evaluated for MBL were included and underwent a standard protocol (1.5 T, n = 37; 3 T, n = 19), including postcontrast T1-weighted SE and SPACE. The rating was performed by three raters in two sessions > six weeks apart. The true number of MBL was determined using all available imaging including follow-up. Intraclass correlations for intra-rater and inter-rater agreement were calculated. Signal intensity ratios (SIR; enhancing lesion, white matter) were determined on a subset of 46 MBL > 4 mm. A paired t-test was used to evaluate postcontrast sequence order and SIR. Reader accuracy was evaluated by the coefficient of determination. Results: A total of 135 MBL were identified (mean/subject 2.41, SD 6.4). The intra-rater agreement was excellent for all 3 raters (ICC = 0.97–0.992), as was the inter-rater agreement (ICC = 0.995 SE, 0.99 SPACE). Subjective qualitative ratings were lower for SE images; however, signal intensity ratios were higher in SE sequences. Accuracy was high in all readers for both SE (R² 0.95–0.96) and SPACE (R² 0.91–0.96) sequences. Conclusions: Although SE sequences are superior to gradient echo sequences in the detection of small MBL, they have long acquisition times and frequent artifacts. We show that T1-weighted SPACE is not inferior to standard thin-slice SE sequences in the detection of MBL at both imaging fields. Critical relevance statement: Our results show the suitability of 3D T1-weighted turbo spin echo (TSE) sequences (SPACE, CUBE, VISTA) in the detection of brain metastases at both 1.5 T and 3 T. Key points: • Accurate detection of brain metastases is critical due to advances in radiosurgery. • T1-weighted SE sequences are superior to gradient echo in detecting small metastases. • T1-weighted 3D-TSE sequences may achieve high resolution and relative insensitivity to artifacts. • T1-weighted 3D-TSE sequences have been recommended in imaging brain metastases at 3 T. • We found T1-weighted 3D-TSE equivalent to thin-slice SE at 1.5 T and 3 T. Graphical

Tumour Treating Fields (TTFields) are electric fields that disrupt processes required for cancer cell viability and tumour progression. TTFields therapy gained FDA approval and CE accreditation for newly diagnosed glioblastoma (ndGBM) following the EF-14 study (NCT00916409). We aimed to determine the real-world survival benefit of TTFields therapy. Survival data from clinical studies in TTFields therapy-treated patients with ndGBM were identified using a literature search and analysed. The Cochran Q test and Higgins I2 statistic were used to assess and quantify inter-study heterogeneity. Survival curves were pooled using a distribution-free random-effects method. Among studies evaluating the clinical effcacy of TTFields therapy in ndGBM, six studies were identified which compared the addition of TTFields therapy to standard of care (SOC) vs SOC alone. The meta-analysis showed a significant improvement in OS for TTFields therapy-treated patients vs SOC alone (P < 0.001). The pooled effect was robust and independent of any individual study. In post-approval studies, the pooled median OS was 22.2 months (95% CI, 17.3–42.6) and 17.3 months (95% CI, 13.6–22.0) for TTFields therapy-treated patients and SOC, respectively. Rates of gross total resection were generally higher in the real-world setting, irrespective of TTFields therapy use. Average device usage of ≥75% was associated with prolonged survival vs <75% usage (pooled HR: 0.63; 95% CI, 0.48–0.83; P = 0.001). Data suggest a significant survival benefit when TTFields therapy is added to SOC for patients with ndGBM. Usage of ≥75% may be meaningful in the real-world setting.

Introduction: The prognosis of glioblastoma remains unfavorable. TTFields utilize low intensity electric fields (frequency 150–300 kHz) that disrupt cellular processes critical for cancer cell viability and tumor progression. TTFields are delivered via transducer arrays placed on the patients’ scalp. Methods: Between the years 2004 and 2022, 55 patients (20 female), aged 21.9–77.8 years (mean age 47.3±11.8 years; median 47.6 years) were treated with TTFields for newly-diagnosed GBM, and compared to 54 control patients (20 females), aged 27.0–76.7 years (mean age 51.4±12.2 years; median 51.7 years) (p=0.08). All patients underwent gross total or partial resection of GBM. One patient had biopsy only. When available, MGMT promoter methylation status and IDH mutation was detected.Results: Patients on TTFields therapy demonstrated improvements in PFS and OS relative to controls (hazard ratio: 0.64, p=0.031; and 0.61, p=0.028 respectively). TTFields average time on therapy was 74.8% (median 82%): median PFS of these patients was 19.75 months. Seven patients with TTFields usage ≤60% (23–60%, mean 46.3%, median 53%) had a median PFS of 7.95 months (p=0.0356). Control patients with no TTFields exposure had a median PFS of 12.45 months. Median OS of TTF patients was 31.67 months compared to 24.80 months for controls.Discussion: This is the most extensive study on newly-diagnosed GBM patients treated with TTFields, covering a period of 18 years at a single center and presenting not only data from clinical trials but also a group of 36 patients treated with TTFields as a part of routine clinical practice.

Introduction: We aimed to evaluate T2 relaxation times in the follow-up of patients with glioblastoma (GBM), the most common and aggressive primary brain tumor with nearly universal recurrence following standard therapy. Methods: In this partially retrospective study, 53 patients (mean age 45.05 years ± 12.6) with non-recurrent GBM followed at our department roughly every 2 months after surgery and combined chemo-radiotherapy were included (>180 days progression-free survival [PFS] as determined by RANO,¹ ≥3 MRI examinations in the PFS interval [same scanner and protocol]). Thirty-six patients additionally received Tumor-Treating Fields (TTFields) therapy. All data used were prior to progression. T2 relaxation rates (1/T2) were calculated voxel-wise assuming mono-exponential decay. Whole-brain (WB) histogram values were extracted from 1/T2 maps including skewness, kurtosis, mean, median, 15 and 85 percentile values and variance. We additionally segmented 1/T2 maps using a 5 compartment Gaussian mixture model (Python v3.8.2), producing mean, variance and voxel percentage for each component. To evaluate predictive potential with respect to PFS, we used a deep recurrent neural network (long short-term memory [LSTM] model) in Tensorflow v2.3.1 (4 timesteps); the features used included the above metrics as well as TTFields treatment status and scanner. Twenty subjects were included in the predictive model, as inclusion criteria were stricter (progression, ≤120 days between all MRIs [mean 43.6 days], ≥4 PFS MRIs). Two models were tested: a regression model (days to progression) and a classification model (±18 months PFS; models differed in output layer). Both models were run 10 times; mean results are presented. Results: We found WB median 1/T2 correlated with PFS, as values decreased prior to progression. WB median 1/T2 linear regression slopes also differed in progression versus pseudo-progression, as values were relatively stable in pseudo-progression. The deep LSTM regression model achieved an R² of 97%. The deep LSTM classification model achieved a mean macro precision of 86%, recall 85% and F1 accuracy of 85% in predicting progression within 18 months. Conclusions: We found very intriguing results with WB median 1/T2 measurements in distinguishing progression from pseudo-progression, and in suggesting progression despite otherwise unremarkable imaging. A more complex predictive model trained on pre-progression data, using a fully-automated segmentation method followed by deep learning, showed very promising results. The present results may find utility in the monitoring of GBM patients and may be advantageous to include in multimodal predictive models. Reference: 1. Wen PY et al. J Clin Oncol 2010;28:1963-1972. Citation Format: Aaron M. Rulseh, Josef Vymazal. Prediction of progression-free survival in patients with primary glioblastoma: MRI T2 relaxivity and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4138.

We show that clinical gadolinium-based contrast agents can form Gd³⁺-ferritin nanoparticles under physiological conditions. We suggest they can contribute to the T₁ intensity changes in ferritin-rich brain areas observed in patients examined by MRI.

Introduction: Glioblastoma (GBM) is the most common malignant primary brain tumor, and methods to improve the early detection of disease progression and evaluate treatment response are highly desirable. We therefore explored changes in whole-brain apparent diffusion coefficient (ADC) values with respect to survival (progression-free [PFS], overall [OS]) in a cohort of GBM patients followed at regular intervals until disease progression. Methods: A total of 43 subjects met inclusion criteria and were analyzed retrospectively. Histogram data were extracted from standardized whole-brain ADC maps including skewness, kurtosis, entropy, median, mode, 15th percentile (p15) and 85th percentile (p85) values, and linear regression slopes (metrics versus time) were fitted. Regression slope directionality (positive/negative) was subjected to univariate Cox regression. The final model was determined by aLASSO on metrics above threshold. Results: Skewness, kurtosis, median, p15 and p85 were all below threshold for both PFS and OS and were analyzed further. Median regression slope directionality best modeled PFS (p = 0.001; HR 3.3; 95% CI 1.6–6.7), while p85 was selected for OS (p = 0.002; HR 0.29; 95% CI 0.13–0.64). Conclusions: Our data show tantalizing potential in the use of whole-brain ADC measurements in the follow up of GBM patients, specifically serial median ADC values which correlated with PFS, and serial p85 values which correlated with OS. Whole-brain ADC measurements are fast and easy to perform, and free of ROI-placement bias.

Primary central nervous system lymphoma (PCNSL) is a rare, aggressive brain neoplasm that accounts for roughly 2-6% of primary brain tumors. In contrast, glioblastoma (GBM) is the most frequent and severe glioma subtype, accounting for approximately 50% of diffuse gliomas. The aim of the present study was to evaluate morphological MRI characteristics in histologically-proven PCNSL and GBM at the time of their initial presentation. We retrospectively evaluated standard diagnostic MRI examinations in 54 immunocompetent patients (26 female, 28 male; age 62.6 ± 11.5 years) with histologically-proven PCNSL and 54 GBM subjects (21 female, 33 male; age 59 ± 14 years). Several significant differences between both infiltrative brain tumors were found. PCNSL lesions enhanced homogenously in 64.8% of cases, while nonhomogeneous enhancement was observed in 98.1% of GBM cases. Necrosis was present in 88.9% of GBM lesions and only 5.6% of PCNSL lesions. PCNSL presented as multiple lesions in 51.9% cases and in 35.2% of GBM cases; however, diffuse infiltrative type of brain involvement was observed only in PCNSL (24.1%). Optic pathways were infiltrated more commonly in PCNSL than in GBM (42.6% vs. 5.6%, respectively, p <0.001). Other cranial nerves were affected in 5.6% of PCNSL, and in none of GBM. Signs of bleeding were rare in PCNSL (5.6%) and common in GBM (44.4%); p < 0.001. Both supratentorial and infratentorial localization was present only in PCNSL (27.7%). Involvement of the basal ganglia was more common in PCNSL (55.6%) than in GBM (18.5%); (p < 0.001). Cerebral cortex was affected significantly more often in GBM (83.3%) than in PCNSL (51.9%); mostly by both enhancing and non-enhancing infiltration. Routine morphological MRI is capable of differentiating between GBM and PCNSL lesions in many cases at time of initial presentation. A solitary infiltrative supratentorial lesion with nonhomogeneous enhancement and necrosis was typical for GBM. PCNSL presented with multiple lesions that enhanced homogenously or as diffuse infiltrative type of brain involvement, often with basal ganglia and optic pathways affection.

Purpose: Multiple system atrophy (MSA) is a rare neurodegenerative disease that remains poorly understood, and the diagnosis of MSA continues to be challenging. We endeavored to improve the diagnostic process and understanding of in vivo characteristics of MSA by diffusion tensor imaging (DTI). Materials and methods: Twenty MSA subjects, ten parkinsonian dominant (MSA-P), ten cerebellar dominant (MSA-C), and 20 healthy volunteer subjects were recruited. Fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity maps were processed using tract-based spatial statistics. Diffusion data were additionally evaluated in the basal ganglia. A support vector machine was used to assess diagnostic utility, leave-one-out cross-validation in the evaluation of classification schemes, and receiver operating characteristic analyses to determine cutoff values. Results: We detected widespread changes in the brain white matter of MSA subjects; however, no group-wise differences were found between MSA-C and MSA-P subgroups. Altered DTI metrics in the putamen and middle cerebellar peduncles were associated with a positive parkinsonian and cerebellar phenotype, respectively. Concerning clinical applicability, we achieved high classification performance on mean diffusivity data in the combined bilateral putamen and middle cerebellar peduncle (accuracy 90.3%±9%, sensitivity 86.5%±11%, and specificity 99.3%±4%). Conclusion: DTI in the middle cerebellar peduncle and putamen may be used in the diagnosis of MSA with a high degree of accuracy.