Incorporating Supramaximal Resection into Survival Stratification of IDH-Wildtype Glioblastoma: A Refined Multi-institutional Recursive Partitioning Analysis.

PURPOSE: To propose a novel recursive partitioning analysis (RPA) classification model in patients with IDH-wildtype glioblastomas that incorporates the recently expanded conception of the extent of resection (EOR) in terms of both supramaximal and total resections. EXPERIMENTAL DESIGN: This multicenter cohort study included a developmental cohort of 622 patients with IDH-wildtype glioblastomas from a single institution (Severance Hospital) and validation cohorts of 536 patients from three institutions (Seoul National University Hospital, Asan Medical Center, and Heidelberg University Hospital). All patients completed standard treatment including concurrent chemoradiotherapy and underwent testing to determine their IDH mutation and MGMTp methylation status. EORs were categorized into either supramaximal, total, or non-total resections. A novel RPA model was then developed and compared to a previous RTOG RPA model. RESULTS: In the developmental cohort, the RPA model included age, MGMTp methylation status, KPS, and EOR. Younger patients with MGMTp methylation and supramaximal resections showed a more favorable prognosis (class I: median overall survival [OS] 57.3 months), while low-performing patients with non-total resections and without MGMTp methylation showed the worst prognosis (class IV: median OS 14.3 months). The prognostic significance of the RPA was subsequently confirmed in the validation cohorts, which revealed a greater separation between prognostic classes for all cohorts compared to the previous RTOG RPA model. CONCLUSIONS: The proposed RPA model highlights the impact of supramaximal versus total resections and incorporates clinical and molecular factors into survival stratification. The RPA model may improve the accuracy of assessing prognostic groups.

Needle-Like Multifunctional Biphasic Microfiber for Minimally Invasive Implantable Bioelectronics.

Implantable bioelectronics has attracted significant attention in electroceuticals and clinical medicine for precise diagnosis and efficient treatment of target diseases. However, conventional rigid implantable devices face challenges such as poor tissue-device interface and unavoidable tissue damage during surgical implantation. Despite continuous efforts to utilize various soft materials to address such issues, their practical applications remain limited. Here, a needle-like stretchable microfiber composed of a phase-convertible liquid metal (LM) core and a multifunctional nanocomposite shell for minimally invasive soft bioelectronics is reported. The sharp tapered microfiber can be stiffened by freezing akin to a conventional needle to penetrate soft tissue with minimal incision. Once implanted in vivo where the LM melts, unlike conventional stiff needles, it regains soft mechanical properties, which facilitate a seamless tissue-device interface. The nanocomposite incorporating with functional nanomaterials exhibits both low impedance and the ability to detect physiological pH, providing biosensing and stimulation capabilities. The fluidic LM embedded in the nanocomposite shell enables high stretchability and strain-insensitive electrical properties. This multifunctional biphasic microfiber conforms to the surfaces of the stomach, muscle, and heart, offering a promising approach for electrophysiological recording, pH sensing, electrical stimulation, and radiofrequency ablation in vivo.

MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement.

Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.

Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis.

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS: For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION: The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas. METHODS: In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score. RESULTS: The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance. CONCLUSIONS: The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Safety, Tolerability, and Pharmacokinetics of a Novel Macrocyclic Gadolinium-Based Contrast Agent, HNP-2006, in Healthy Subjects.

OBJECTIVES: Gadolinium-based contrast agents (GBCAs) are indispensable in contrast-enhanced magnetic resonance imaging. A higher risk of gadolinium deposition in linear GBCAs required the introduction of macrocyclic GBCAs with a stable molecular structure. We conducted the first-in-human study to evaluate the safety, tolerability, and pharmacokinetics (PKs) of HNP-2006, a novel macrocyclic GBCA, in healthy male subjects. MATERIALS AND METHODS: A randomized, placebo-controlled, double-blind, single-ascending dose study was conducted. Subjects received either a single intravenous bolus injection of HNP-2006 or its matching placebo with a treatment-to-placebo ratio of 6:2 at the dose level of 0.02, 0.05, 0.1, 0.2, and 0.3 mmol/kg. Safety was assessed through routine clinical assessments. Blood sampling and urine collection were performed up to 72 hours postdose for PK assessments. Noncompartmental methods were used to calculate PK parameters, and a population PK model was constructed. RESULTS: Overall, 40 subjects completed the study. Fourteen subjects reported 22 treatment-emergent adverse events (TEAEs). The severity of all TEAEs was mild, and the HNP-2006 dose was associated with the incidence of TEAEs. The most common TEAEs included nausea and dizziness, which occurred within an hour of administration. HNP-2006 was rapidly eliminated by urinary excretion with a half-life of 1.8-2.0 hours and showed a dose-proportional PK. A 2-compartment model had the best fit with the population PK analysis. CONCLUSIONS: A single intravenous dose of HNP-2006 was well-tolerated and safe up to 0.30 mmol/kg. HNP-2006 was rapidly excreted in urine and exhibited dose-independent PK profiles.

Blood-brain barrier disruption imaging in postoperative cerebral hyperperfusion syndrome using DCE-MRI.

Little has been reported about the association between cerebral hyperperfusion syndrome (CHS) and blood-brain barrier (BBB) disruption in human. We aimed to investigate the changes in permeability after bypass surgery in cerebrovascular steno-occlusive diseases using dynamic contrast-enhanced MRI (DCE-MRI) and to demonstrate the association between CHS and BBB disruption. This retrospective study included 36 patients (21 hemispheres in 18 CHS patients and 20 hemispheres in 18 controls) who underwent combined bypass surgery for moyamoya and atherosclerotic steno-occlusive diseases. DCE-MRI and arterial spin labeling perfusion-weighted imaging (ASL-PWI) were obtained at the baseline, postoperative state, and discharge. Perfusion and permeability parameters were calculated at the MCA territory (CBF(territorial), Ktrans(territorial), Vp(territorial)) and focal perianastomotic area (CBF(focal), Ktrans(focal), Vp(focal)) of operated hemispheres. As compared with the baseline, both CBF(territorial) and CBF(focal) increased in the postoperative period and decreased at discharge, corresponding well to symptoms in the CHS group. Vp(focal) was lower in the postoperative period and at discharge, as compared with the baseline. In the control group, no parameters significantly differed among the three points. In conclusion, Vp at the focal perianastomotic area significantly decreased in patients with CHS during the postoperative period. BBB disruption may be implicated in the development of CHS after bypass surgery.

Distortion correction using topup algorithm by single k-space (TASK) for echo planar imaging.

Distortion of echo planar imaging (EPI) can be corrected using B0 field maps, which can be estimated with the topup algorithm that requires two EPI images with opposite distortions. In this study, we propose a new algorithm, termed topup algorithm by single K-space (TASK), to generate two input images from a single k-space for the topup algorithm to correct EPI distortions. The centric EPI contains the opposite phase-encoding polarities in one k-space, which can be divided into two halves with opposite distortions. Therefore, two inputs could be extracted by dividing the k-space into halves and processing them using the proposed procedure including an iterative procedure of automatic brain masking and uniformity correction. The efficiency of TASK was evaluated using 3D EPI. Quantitative evaluations showed that TASK corrected EPI distortion at a similar level to the traditional methods. The estimated field maps from the conventional topup and TASK showed a high correlation ([Formula: see text]). An ablation study showed the validity of every suggested step. Furthermore, it was confirmed that TASK was effective for distortion correction of two-shot centric EPI as well, demonstrating its wider applicability. In conclusion, TASK can correct EPI distortions by its own single k-space information with no additional scan.

Neuroepithelial tumor with EWSR1::PATZ1 fusion: A literature review.

We present the clinicopathological and molecular genetic characteristics of a neuroepithelial tumor (NET), EWSR1::PATZ1 fusion-positive with a literature review. This fusion has recently been discovered in rare central nervous system tumors and soft tissue sarcomas and was not included in the fifth edition of the WHO classifications. We identified this fusion in 2 NETs. The first case involved a 7-year-old girl and the second case occurred in a 53-year-old man; both presented with headaches and vomiting. The pediatric case initially showed an intermediate grade of the tumor, but upon recurrences, it transformed into a high-grade tumor with 2 relapses in 8.3 years. This case exhibited high mitotic activity (20/10 high-power fields), and a high Ki-67 index (21%). The TERT promoter (TERTp) mutation was present in both initial and recurrent tumors. In contrast, the adult case was a low-grade tumor with no mitotic activity or recurrence over 13.5 months after subtotal resection and gamma knife surgery. Interestingly, the pediatric case demonstrated a longer survival time compared to conventional glioblastoma. The TERTp mutation, similar to being a molecular signature in adult-type glioblastoma, could also be an indicator of high-grade behavior in PATZ1 fusion NET.

Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype.

PURPOSE: To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype. METHODS: Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 7:3. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated. RESULTS: A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91). CONCLUSION: The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.

Erratum: Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions.

This corrects the article on p. 959 in vol. 23, PMID: 36175000.

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Co-Delivery of Metabolic Modulators Leads to Simultaneous Lactate Metabolism Inhibition and Intracellular Acidification for Synergistic Cancer Therapy.

Simultaneous lactate metabolism inhibition and intracellular acidification (LIIA) is a promising approach for inducing tumor regression by depleting ATP. However, given the limited efficacy of individual metabolic modulators, a combination of various modulators is required for highly efficient LIIA. Herein, a co-delivery system that combines lactate transporter inhibitor, glucose oxidase, and O2 -evolving nanoparticles is proposed. As a vehicle, a facile room-temperature synthetic method for large-pore mesoporous silica nanoparticles (L-MSNs) is developed. O2 -evolving nanoparticles are then conjugated onto L-MSNs, followed by immobilizing the lactate transporter inhibitor and glucose oxidase inside the pores of L-MSNs. To load the lactate transporter inhibitor, which is too small to be directly loaded into the large pores, it is encapsulated in albumin by controlling the albumin conformation before being loaded into L-MSNs. Notably, inhibiting lactate efflux shifts the glucose consumption mechanism from lactate metabolism to glucose oxidase reaction, which eliminates glucose and produces acid. This leads to synergistic LIIA and subsequent ATP depletion in cancer cells. Consequently, L-MSN-based co-delivery of modulators for LIIA shows high anticancer efficacy in several mouse tumor models without toxicity in normal tissues. This study provides new insights into co-delivery of small-molecule drugs, proteins, and nanoparticles for synergistic metabolic modulation in tumors.

Clinicogenetic characteristics and the effect of radiation on the neural stem cell niche in subventricular zone-contacting glioblastoma.

BACKGROUND AND PURPOSE: Neural stem cells (NSCs) in the subventricular zone (SVZ) are recognized as the cellular origin of glioblastoma (GBM) and a potential therapeutic target. However, the characteristics of SVZ contacting GBM (SVZ + GBM) and radiotherapeutic strategies for NSCs are still controversial. Here, we investigated the clinicogenetic features of SVZ + GBM and evaluated the dose effect of NSC irradiation depending on SVZ involvement. MATERIALS AND METHODS: We identified 125 patients with GBM treated with surgery followed by chemoradiotherapy. The genomic profiles were obtained by next-generation sequencing targeting 82 genes. NSCs in the SVZ and hippocampus were contoured using standardized methods, and dosimetric factors were analyzed. SVZ + GBM was defined as GBM with SVZ involvement in a T1 contrast-enhanced image. Progression-free survival (PFS) and overall survival (OS) were used as endpoints. RESULTS: The number of patients with SVZ + GBM was 95 (76%). SVZ + GBM showed lower PFS than GBM without SVZ involvement (SVZ-GBM) (median 8.6 vs. 11.5 months, p = 0.034). SVZ contact was not associated with any specific genetic profile but was an independent prognostic factor in multivariate analysis. In SVZ + GBM, patients receiving high doses to the ipsilateral NSC region showed significantly better OS (HR = 1.89, p = 0.011) and PFS (HR = 1.77, p = 0.013). However, in SVZ-GBM, high doses to the ipsilateral NSC region were associated with worse OS (HR = 0.27, p = 0.013) and PFS (HR = 0.37, p = 0.035) in both univariate and multivariate analyses. CONCLUSION: SVZ involvement in GBM was not associated with distinct genetic features. However, irradiation of NSCs was associated with better prognosis in patients with tumors contacting the SVZ.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS: Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS: The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION: DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT: The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS: • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Language Mapping in Brain Tumor Surgery: Validation With Direct Cortical Stimulation and Cortico-Cortical Evoked Potential.

OBJECTIVE: Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging-derived tractography (DTI-t) contribute to the localization of language areas, but their accuracy remains controversial. This study aimed to investigate the diagnostic performance of preoperative fMRI and DTI-t obtained with a simultaneous multi-slice technique using intraoperative direct cortical stimulation (DCS) or corticocortical evoked potential (CCEP) as reference standards. MATERIALS AND METHODS: This prospective study included 26 patients (23-74 years; male:female, 13:13) with tumors in the vicinity of Broca's area who underwent preoperative fMRI and DTI-t. A site-by-site comparison between preoperative (fMRI and DTI-t) and intraoperative language mapping (DCS or CCEP) was performed for 226 cortical sites to calculate the sensitivity and specificity of fMRI and DTI-t for mapping Broca's areas. For sites with positive signals on fMRI or DTI-t, the true-positive rate (TPR) was calculated based on the concordance and discordance between fMRI and DTI-t. RESULTS: Among 226 cortical sites, DCS was performed in 100 sites and CCEP was performed in 166 sites. The specificities of fMRI and DTI-t ranged from 72.4% (63/87) to 96.8% (122/126), respectively. The sensitivities of fMRI (except for verb generation) and DTI-t were 69.2% (9/13) to 92.3% (12/13) with DCS as the reference standard, and 40.0% (16/40) or lower with CCEP as the reference standard. For sites with preoperative fMRI or DTI-t positivity (n = 82), the TPR was high when fMRI and DTI-t were concordant (81.2% and 100% using DCS and CCEP, respectively, as the reference standards) and low when fMRI and DTI-t were discordant (≤ 24.2%). CONCLUSION: fMRI and DTI-t are sensitive and specific for mapping Broca's area compared with DCS and specific but insensitive compared with CCEP. A site with a positive signal on both fMRI and DTI-t represents a high probability of being an essential language area.

Non-invasive flow mapping of parasagittal meningeal lymphatics using 2D interslice flow saturation MRI.

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques. While in many studies it has been difficult to detect mLVs and confirm their signal source noninvasively, the detection of mLVs in this study was confirmed by their posterior to anterior flow direction and their velocities and morphological features, which were consistent with those from the literature. In addition, IR-ALADDIN was compared with contrast-enhanced black blood imaging to confirm the detection of mLVs and its similarity. For the quantification of flow velocity of mLVs, IR-ALADDIN was performed at three inversion times of 2000, 2300, and 2600 ms (three-TI IR-ALADDIN) for both a flow phantom and humans. For this preliminary result, the flow velocity of the dorsal mLVs in humans ranged between 2.2 and 2.7 mm/s. Overall, (i) the single-TI IR-ALADDIN can be used as a novel non-invasive method to visualize mLVs in the whole brain with scan time of ~ 17 min and (ii) the multi-TI IR-ALADDIN can be used as a way to quantify the flow velocity of mLVs with a scan time of ~ 10 min (or shorter) in a limited coverage. Accordingly, the suggested approach can be applied to noninvasively studying meningeal lymphatic flows in general and also understanding the clearance pathways of waste production through mLVs in humans, which warrants further investigation.