Contrast-enhanced T1-weighted Magnetic Resonance Imaging Research Articles

A Pedicled Posterior Septal–Nasal Floor Flap and a Novel Rescue Flap for Skull Base Reconstruction

We devised a new surgical alternative to the conventional nasoseptal flap, a pedicled posterior septal-nasal floor flap that we named the Kegon flap. We evaluated the effectiveness of this flap for skull base reconstruction in patients with high-flow cerebrospinal fluid (CSF) leakage after sellar/parasellar tumor resection. The Kegon flap with a novel rescue flap was designed to preserve blood flow and mucosa anterior to the nasal septum and to avoid flap damage during surgery. We retrospectively evaluated postoperative flap perfusion with T1-weighted contrast-enhanced magnetic resonance imaging and characterized complications and wound healing in 5 patients who experienced high-flow CSF leakage after sellar/parasellar tumor resection requiring reconstruction. Postoperative T1-weighted contrast-enhanced magnetic resonance imaging demonstrated good flap perfusion in all patients. The area reconstructed with the Kegon flap healed within the first month following surgery. No postoperative CSF leakage or nasal hemorrhage was observed. There was no perforation of the anterior nasal septum after surgery. The mucosal defect had completely epithelialized in all patients by 3 months after surgery. There were no instances of prolonged nasal crusting or any subjective decrease in olfactory function 3 months after surgery. The use of a Kegon flap with a novel rescue flap was effective and helped preserve nasal structure and function in patients undergoing skull base reconstruction after sellar/parasellar tumor resection associated with high-flow CSF leakage.

Ivy Sign in Moyamoya Disease: A Comparative Study of the FLAIR Vascular Hyperintensity Sign Against Contrast-Enhanced MRI.

The ability of the ivy sign on contrast-enhanced T1-weighted MR imaging (CEMR) to reflect cerebral perfusion and postoperative revascularization in Moyamoya disease remains largely unknown. We aimed to compare the capabilities of CEMR and FLAIR. CEMR, FLAIR, arterial spin-labeling, and DSA were performed in 44 patients with Moyamoya disease. The ivy sign was scored separately on CEMR and FLAIR using the Alberta Stroke Program Early CT Score. The status of leptomeningeal collaterals was scored on DSA. The postoperative Matsushima grade was evaluated at least 3 months after surgical revascularization. Scoring of the ivy sign on CEMR showed excellent interrater reliability, and FLAIR vascular hyperintensity showed moderate interrater reliability. Correlation analyses revealed that DSA scores were more consistent with the CEMR-based ivy sign score (r = 0.25, P = .03) than with FLAIR vascular hyperintensity (r = 0.05, P = .65). The CEMR-based ivy sign score was significantly correlated with CBF in late-Suzuki stage Moyamoya disease (t = -2.64, P = .02). The CEMR-based ivy sign score at baseline was significantly correlated with the postoperative Matsushima grade (r = 0.48, P = .03). In this study, CEMR outperformed FLAIR in capturing the ivy sign in Moyamoya disease. In addition, the CEMR-based ivy sign score provided adequate information on hemodynamic status and postoperative neovascularization. The current study suggested that CEMR could be considered as an alternative to FLAIR in future studies investigating leptomeningeal collaterals in Moyamoya disease.

Comparison of MRI and CT for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma Based on a Non-Radiomics and Radiomics Method: Which Imaging Modality Is Better?

Computed tomography (CT) and magnetic resonance imaging (MRI) are both capable of predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). However, which modality is better is unknown. To intraindividually compare CT and MRI for predicting MVI in solitary HCC and investigate the added value of radiomics analyses. Retrospective. Included were 402 consecutive patients with HCC (training set:validation set=300:102). T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging MRI at 3.0T and contrast-enhanced CT. CT- and MR-based radiomics signatures (RS) were constructed using the least absolute shrinkage and selection operator regression. CT- and MR-based radiologic (R) and radiologic-radiomics (RR) models were developed by univariate and multivariate logistic regression. The performance of the RS/models was compared between two modalities. To investigate the added value of RS, the performance of the R models was compared with the RR models in HCC of all sizes and 2-5 cm in size. Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and compared using the Delong test. Histopathologic MVI was identified in 161 patients (training set:validation set=130:31). MRI-based RS/models tended to have a marginally higher AUC than CT-based RS/models (AUCs of CT vs. MRI, P: RS, 0.801 vs. 0.804, 0.96; R model, 0.809 vs. 0.832, 0.09; RR model, 0.835 vs. 0.872, 0.54). The improvement of RR models over R models in all sizes was not significant (P=0.21 at CT and 0.09 at MRI), whereas the improvement in 2-5 cm was significant at MRI (P < 0.05) but not at CT (P=0.16). CT and MRI had a comparable predictive performance for MVI in solitary HCC. The RS of MRI only had significant added value for predicting MVI in HCC of 2-5 cm. 3 TECHNICAL EFFICACY: Stage 2.

The Definition and Delineation of the Target Area of Radiotherapy Based on the Recurrence Pattern of Glioblastoma After Temozolomide Chemoradiotherapy.

Radiotherapy is an important treatment for glioblastoma (GBM), but there is no consensus on the target delineation for GBM radiotherapy. The Radiation Therapy Oncology Group (RTOG) and European Organisation for Research and Treatment of Cancer (EORTC) each have their own rules. Our center adopted a target volume delineation plan based on our previous studies. This study focuses on the recurrence pattern of GBM patients whose target delineations did not intentionally include the T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity area outside of the gross tumor volume (GTV). We prospectively collected 162 GBM cases and retrospectively analysed the clinical data and continuous dynamic magnetic resonance images (MRI) of 55 patients with recurrent GBM. All patients received concurrent radiotherapy and chemotherapy with temozolomide (TMZ). The GTV that we defined includes the postoperative T1-weighted MRI enhancement area and resection cavity. Clinical target volume 1 (CTV1) and CTV2 were defined as GTVs with 1 and 2 cm margins, respectively. Planning target volume 1 (PTV1) and PTV2 were defined as CTV1 and CTV2 plus a 3 mm margin with prescribed doses of 60 and 54 Gy, respectively. The first recurrent contrast-enhanced T1-weighted MRI was introduced into the Varian Eclipse radiotherapy planning system and fused with the original planning computed tomography (CT) images to determine the recurrence pattern. The median follow-up time was 15.8 months. The median overall survival (OS) and progression-free survival (PFS) were 17.7 and 7.0 months, respectively. Among the patients, 44 had central recurrences, two had in-field recurrences, one had marginal recurrence occurred, 11 had distant recurrences, and three had subependymal recurrences. Five patients had multiple recurrence patterns. Compared to the EORTC protocol, target delineation that excludes the adjacent T2/FLAIR hyperintensity area reduces the brain volume exposed to high-dose radiation (P = 0.000) without an increased risk of marginal recurrence. Therefore, it is worthwhile to conduct a clinical trial investigating the feasibility of intentionally not including the T2/FLAIR hyperintensity region outside of the GTV.

Detection of knee synovitis using non-contrast-enhanced qDESS compared with contrast-enhanced MRI

BackgroundTo assess diagnostic accuracy of quantitative double-echo in steady-state (qDESS) MRI for detecting synovitis in knee osteoarthritis (OA).MethodsPatients with different degrees of radiographic knee OA were included prospectively. All underwent MRI with both qDESS and contrast-enhanced T1-weighted magnetic resonance imaging (CE-MRI). A linear combination of the two qDESS images can be used to create an image that displays contrast between synovium and the synovial fluid. Synovitis on both qDESS and CE-MRI was assessed semi-quantitatively, using a whole-knee synovitis sum score, indicating no/equivocal, mild, moderate, and severe synovitis. The correlation between sum scores of qDESS and CE-MRI (reference standard) was determined using Spearman’s rank correlation coefficient and intraclass correlation coefficient for absolute agreement. Receiver operating characteristic analysis was performed to assess the diagnostic performance of qDESS for detecting different degrees of synovitis, with CE-MRI as reference standard.ResultsIn the 31 patients included, very strong correlation was found between synovitis sum scores on qDESS and CE-MRI (ρ = 0.96, p < 0.001), with high absolute agreement (0.84 (95%CI 0.14–0.95)). Mean sum score (SD) values on qDESS 5.16 (3.75) were lower than on CE-MRI 7.13 (4.66), indicating systematically underestimated synovitis severity on qDESS. For detecting mild synovitis or higher, high sensitivity and specificity were found for qDESS (1.00 (95%CI 0.80–1.00) and 0.909 (0.571–1.00), respectively). For detecting moderate synovitis or higher, sensitivity and specificity were good (0.727 (95%CI 0.393–0.927) and 1.00 (0.800–1.00), respectively).ConclusionqDESS MRI is able to, however with an underestimation, detect synovitis in patients with knee OA.

Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma.

Conventional magnetic resonance imaging (MRI) technics are insufficient in the differentiation of tumor progression from post-treatment changes in patients with treated glioblastoma. Previous studies have suggested that histogram analysis is a useful tool in the assessment of treatment response in different cancer types. The aim of the study was to to evaluate the effectiveness of MRI histogram analysis in the differentiation of tumor progression from pseudoprogression in patients with treated glioblastoma. Forty-six patients with glioblastoma who newly developed enhancing lesions following chemoradiation treatment were included in this retrospective study. Histogram analysis was performed from new enhancing lesions on T1-weighted contrast-enhanced MRI. Histogram analysis findings of patients with progression (23) and pseudoprogression (23) were compared. Mean, minimum, median, maximum, standard deviation, variance, entropy, skewness, uniformity values were found to be significantly higher in progressive disease (p < 0.05). A receiver-operating characteristic (ROC) curve analysis was performed for mean value, and area under the curve (AUC) was found as 0.975. When the threshold value was selected as 528.86, two groups could be differentiated with 95.7% sensitivity and 87.0% specificity. MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.

MRI Evaluation of Extramural Venous Invasion (EMVI) with Rectal Carcinoma Using High Resolution T2 and Combination of High Resolution T2 and Contrast Enhanced T1 Weighted Imaging.

Background:EMVI is a direct invasion of a vein by a tumor. As a predictor of hematogenous metastasis, it is a poor prognostic factor in rectal cancer and can be accurately identified on MRI prior to surgical procedure.Objective:To evaluate the role of contrast-enhanced T1-weighted magnetic resonance imaging (CET1WI) in addition to high-resolution T2-weighted imaging (HRT2WI) in assessing extramural venous invasion (EMVI) of rectal cancer.Methods:In all 195 patients with rectal cancer, HRT2WI and CET1WI sequences were produced within pre-operative MRI for the purpose of assessing for the presence of EMVI (mrEMVI). CET1WI sequences were produced following administration of Gadolinium contrast medium. mrEMVI assessment results were classified into two groups. Group A consisted of mrEMVI assessment results obtained using HRT2WI sequences only. Group B consisted of mrEMVI assessment results obtained using a combination of HRT2WI + CET1WI sequences. Results obtained for each group (A and B) were correlated with a histopathological finding (pEMVI) as a reference standard.Results:Out of a total of 195 rectal cancer patients, mrEMVI was positive in 41 (21%) patients in group A, and in 45 (23%) patients in group B. Histopathological finding demonstrated pEMVI in 54 (27.7%) patients. A statistical analysis of group A (HRT2WI sequences) resulted in 75.9% sensitivity to mrEMVI and 96.4% specificity, Positive Predictive Value of 89.1% and Negative Predictive Value of 91.2% (95% confidence interval (CI), p< 0.05). Statistical analysis of group B (HRT2WI + CET1WI sequences) resulted in 83.3% sensitivity to mrEMVI and 98.5% specificity, Positive Predictive Value of 89.1% and Negative Predictive Value of 91.2% (CI 95%, p< 0.05).Conclusion:T1-weighted magnetic resonance imaging (CET1WI) in addition to high-resolution T2-weighted imaging (HRT2WI) increased evaluation of extramural venous invasion (EMVI) of rectal cancer.

Imaging Assessment of Visceral Pleural Surface Invasion by Lung Cancer: Comparison of CT and Contrast-Enhanced Radial T1-Weighted Gradient Echo 3-Tesla MRI.

ObjectiveTo compare the diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3-tesla (3T) magnetic resonance imaging (MRI) and computed tomography (CT) for the detection of visceral pleural surface invasion (VPSI). Visceral pleural invasion by non-small-cell lung cancer (NSCLC) can be classified into two types: PL1 (without VPSI), invasion of the elastic layer of the visceral pleura without reaching the visceral pleural surface, and PL2 (with VPSI), full invasion of the visceral pleura.Materials and MethodsThirty-three patients with pathologically confirmed VPSI by NSCLC were retrospectively reviewed. Multidetector CT and contrast-enhanced 3T MRI with a free-breathing radial three-dimensional fat-suppressed volumetric interpolated breath-hold examination (VIBE) pulse sequence were compared in terms of the length of contact, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. Supplemental evaluation of the tumor-pleura interface (smooth versus irregular) could only be performed with MRI (not discernible on CT).ResultsAt the tumor-pleura interface, radial VIBE MRI revealed a smooth margin in 20 of 21 patients without VPSI and an irregular margin in 10 of 12 patients with VPSI, yielding an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score for VPSI detection of 91%, 83%, 95%, 91%, 91%, and 87%, respectively. The McNemar test and receiver operating characteristics curve analysis revealed no significant differences between the diagnostic accuracies of CT and MRI for evaluating the contact length, angle of mass margin, or arch distance-to-maximum tumor diameter ratio as predictors of VPSI.ConclusionThe diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3T MRI and CT were equal in terms of the contact length, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. The advantage of MRI is its clear depiction of the tumor-pleura interface margin, facilitating VPSI detection.

Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks

Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.

MRI brain segmentation using correlation based on adaptively regularised kernel-based fuzzy C-means clustering

Brain tumour detection is the most challenging aspect in the field of medical image processing and analysis. Whereas, the traditional techniques used in MRI brain tumour segmentation are extremely time consuming tasks. In this paper, the proposed approach contains two major steps: image acquisition and segmentation. Initially, the brain tumour detection was assessed by employing T1-weighted contrast enhanced magnetic resonance imaging (T1-WCEMRI) database. After image acquisition, segmentation was carried-out by using correlation based adaptively regularised kernel-based fuzzy C-means (ARKFCM) clustering along with Otsu thresholding. In conventional clustering methodologies, it was very hard to detect the ill-defined masses that highly decrease the segmentation accuracy. To address this concern, the kernel function in ARKFCM was replaced by a correlation function for localising the object in a complex template. In experimental analysis, the proposed approach distinguishes the normal brain region and brain tumour region by means of dice coefficient, Jaccard coefficient, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental outcome shows that the proposed methodology delivered average accuracy of 99.213% in brain tumour detection. The proposed methodology improved accuracy in brain tumour segmentation up to 3-3.2% compared to the existing methods: FCM and ARKFCM.

Endoscope-assisted fluorescence-guided resection allowing supratotal removal in glioblastoma surgery.

Several studies have proven the benefits of a wide extent of resection (EOR) of contrast-enhancing tumor in terms of progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma (GBM). Thus, gross-total resection (GTR) is the main surgical goal in noneloquently located GBMs. Complete tumor removal can be almost doubled by microscopic fluorescence guidance. Recently, a study has shown that an endoscope with a light source capable of inducing fluorescence allows visualization of remnant fluorescent tumor tissue even after complete microscopic fluorescence-guided (FG) resection, thereby increasing the rate of GTR. Since tumor infiltration spreads beyond the borders of contrast enhancement on MRI, the aim of this study was to determine via volumetric analyses of the EOR whether endoscope-assisted FG resection enables supratotal resection beyond the borders of contrast enhancement. The authors conducted a retrospective single-center analysis of a consecutive series of patients with primary GBM presumed to be noneloquently located and routinely operated on at their institution between January 2015 and February 2018 using a combined microscopic and endoscopic FG resection. A 20-mg/kg dose of 5-aminolevulinic acid (5-ALA) was administered 4 hours before surgery. After complete microscopic FG resection, the resection cavity was scanned using the endoscope. Detected residual fluorescent tissue was resected and embedded separately for histopathological examination. Nonenhanced and contrast-enhanced 3D T1-weighted MR images acquired before and within 48 hours after tumor resection were analyzed using 3D Slicer. Bias field-corrected data were used to segment brain parenchyma, contrast-enhancing tumor, and the resection cavity for volume definition. The difference between the pre- and postoperative brain parenchyma volume was considered to be equivalent to the resected nonenhancing but fluorescent tumor tissue. The volume of resected tumor tissue was calculated from the sum of resected contrast-enhancing tumor tissue and resected nonenhancing tumor tissue. Twelve patients with GBM were operated on using endoscopic after complete microscopic FG resection. In all cases, residual fluorescent tissue not visualized with the microscope was detected. Histopathological examination confirmed residual tumor tissue in all specimens. The mean preoperative volume of brain parenchyma without contrast-enhancing tumor was 1213.2 cm3. The mean postoperative volume of brain parenchyma without the resection cavity was 1151.2 cm3, accounting for a mean volume of nonenhancing but fluorescent tumor tissue of 62.0 cm3. The mean relative rate of the overall resected volume compared to the contrast-enhancing tumor volume was 244.7% (p < 0.001). Combined microscopic and endoscopic FG resection of GBM significantly increases the EOR and allows the surgeon to achieve a supratotal resection beyond the borders of contrast enhancement in noneloquently located GBM.

Predictive Value of Heterogeneously Enhanced Magnetic Resonance Imaging Findings With Computed Tomography Evidence of Calcification for Severe Motor Deficits in Spinal Meningioma.

ObjectiveSpinal meningioma is mostly benign, but they can exhibit neurological deficit. The relationship between neurological impairment and its radiographic findings, including intratumor magnetic resonance imaging (MRI) gadolinium enhancement and calcification in computed tomography (CT) scan, has not been studied. The purpose of this study was to investigate the association of preoperative image findings with neurological status in spinal meningioma. MethodsPatients histologically diagnosed with spinal meningioma (n = 24), with an average age of 65.4 years, were included. The patients were classified into 2 groups, the homogeneous and heterogeneous groups, based on the contrast-enhanced T1-weighted MRI findings. Further, baseline demographics (age, sex, presence of preoperative paralysis [manual muscle testing 3 or worse neurological deficit in upper and/or lower limbs], tumor level, tumor length, and tumor occupation ratio), histological findings (Ki-67 index and histological subtypes), and CT findings (presence of intratumor calcification and Hounsfield unit [HU] value) were examined. ResultsPreoperative paralysis was observed in 33.3% (8 of 24) of the patients. These patients exhibited frequent heterogeneous contrast-enhanced MRI findings than those without preoperative paralysis (57.1% vs. 14.3%, p = 0.040). Further, preoperative paralysis did not associate with tumor level, tumor length, tumor-occupied ratio, Ki-67 index, and histological subtypes. The heterogeneous group showed 100% intratumor calcification and higher maximum HU than the homogeneous group (1,109.8 vs. 379.2, p = 0.001). ConclusionThe heterogeneous contrast-induced MRI findings in the spinal meningioma were significantly associated with preoperative neurological impairment. Moreover, the intratumor contrast-deficient region in the heterogeneously enhanced tumors reflected marked calcification. The tumor hardness due to calcification may be related to preoperative neurological deficit.

Spontaneously changing MRI findings of primary central nervous system vasculitis: A case report

Spontaneously disappearing lesions on magnetic resonance imaging (MRI) has been described in primary central nervous system lymphoma. In this case, we report our experience of spontaneously changing MRI findings of primary central nervous system vasculitis (PCNSV) confirmed histopathologically. A 69-year-old man presented with sudden unsteady gait. Fluid-attenuated inversion recovery (FLAIR) images showed high-intensity areas in the right deep white matter. Contrast-enhanced T1-weighted MRI demonstrated a nodular lesion in the white matter of the left occipitoparietal lobe. On repeat MRI 7 days later, FLAIR hyperintense lesions had spontaneously disappeared and contrast-enhanced lesions had progressed, with new contrast lesions in the right corpus callosum. Repeat MRI 14 days after admission demonstrated contrast-enhancing lesions either increased or decreased in intensity in both occipitoparietal lobes. Contrast-enhancing lesions were therefore biopsied. Histopathological examination revealed vasculitis with fibrinoid necrosis. PCNSV was diagnosed without any signs of inflammation in blood vessels other than cerebral blood vessels. Spontaneously changing MRI findings may play an important role in diagnosing PCNSV.

Timeline Of Hippocampal Atrophy Following Whole-Brain Radiation Therapy With And Without Hippocampus Avoidance

In the past decade, the overall survival rates of patients with cerebral metastases have significantly improved. Quality of life and, more specifically, the preservation of neurocognitive functions have thus gained increasing importance. Cognitive decline has been specifically correlated with hippocampal atrophy. The aim of this study was to depict and compare the time course of hippocampal atrophy following conventional whole-brain radiation therapy (WBRT) and hippocampus avoidance WBRT (HA-WBRT). 168 patients with cerebral metastases of solid tumors without meningeal spread, who had received WBRT or HA-WBRT between December 2003 and December 2016 were identified. Forty-six HA-WBRT and 92 WBRT patients were included, who underwent at least one three-dimensional (3D) contrast-enhanced T1-weighted magnetic resonance imaging sequence (Gd-T1MRI) before and after irradiation. HA-WBRT patients were treated analogously to the ongoing HIPPORAD-trial (DRKS00004598) protocol: HA-WBRT (30 Gy in 12 fractions, D98% to hippocampus ≤9 Gy, D2% ≤ 17 Gy) with simultaneous integrated boost (51/42 Gy in 12 fractions) on metastases/resection cavities. WBRT was mainly performed with 35 Gy in 14 fractions (43.9%) and 30 Gy in 10 fractions (30.8%). The bilateral hippocampal volume was segmented and quantified using the Computational Anatomy Toolbox (CAT12), based on 3D Gd-T1MRI sequences within 24 months before and 48 months after therapy. Surgical resections, pre- and post-irradiations were taken into account. The statistical analysis of the non-linear time course of hippocampal atrophy was performed with R (Version 3.4.4) using a Generalized Additive Mixed Model (GAMM), with “time” and “irradiation group” (WBRT versus HA-WBRT) as variables of interest and by modelling patients and scanners as random effects. Variability arising from interindividual differences such as age, sex and total brain volume was statistically controlled for. A total of 922 Gd-T1MRI sequences were analyzed. By evaluating the time course of hippocampal atrophy in the interval between 24 months before and 48 months after radiation therapy, the results revealed a significant hippocampal volume decrease across time after both WBRT and HA-WBRT (p < 10-16). However, the atrophy across time was significantly lower in the HA-WBRT group compared to the WBRT group as indicated by a significant interaction of irradiation group by time (p < .006). HA-WBRT is a therapeutic option for patients with multiple brain metastases, which can effectively and durably minimize the extent of irradiation-induced hippocampal atrophy compared to conventional WBRT. Considering this effect, HA-WBRT with simultaneous integrated boost has the potential to prevent neurocognitive adverse effects, which will be further evaluated in the multicentric phase II HIPPORAD-trial.

Aging Is Positively Associated with Peri-Sinus Lymphatic Space Volume: Assessment Using 3T Black-Blood MRI.

Objectives: Aging is a major risk factor for many neurological disorders and is associated with dural lymphatic dysfunction. We sought to evaluate the association of aging with the volume of the peri-sinus lymphatic space using contrast-enhanced 3T T1-weighted black-blood magnetic resonance imaging (MRI). Methods: In this retrospective study, 165 presumed neurologically normal subjects underwent brain MRIs for cancer staging between April and November 2018. The parasagittal peri-sinus lymphatic space was evaluated using contrast-enhanced 3D T1-weighted black-blood MRIs, and volumes were measured with semiautomatic method. We compared the volumes of normalized peri-sinus lymphatic spaces between the elderly (≥65 years, n = 72) and non-elderly (n = 93) groups and performed multivariate logistic regression analyses to assess if aging is independently associated with the volume of normalized peri-sinus lymphatic spaces. Results: The normalized peri-sinus lymphatic space volume was significantly higher in the elderly than in the non-elderly (mean, 3323 ± 758.7 mL vs. 2968.7 ± 764.3 mL, p = 0.047). After adjusting the intracranial volume, age age was the strongest factor independently associated with peri-sinus lymphatic space volume (β coefficient, 28.4 (5.7–51.2), p = 0.015) followed by male sex (β coefficient, 672.4 (113.5–1230.8), p = 0.019). Conclusions: We found that the peri-sinus dural lymphatic space volume was higher in the elderly group than in the non-elderly group, and the increased peri-sinus lymphatic space was independently associated with aging. These findings indicate that the peri-sinus lymphatic space may be related with the aging process and lymphatic system dysfunction as well.

Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas

PurposeBone invasion in meningiomas is a prognostic determinant, and a priori knowledge may alter surgical techniques. Here, we aim to predict bone invasion in meningiomas using radiomic signatures based on preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging (MRI). MethodsIn this retrospective study, 490 patients diagnosed with meningiomas, including WHO grade I (448cases), grade II (38cases), and grade III (4cases), were enrolled and 213 out of 490 cases (43.5 %) had bone invasion. The patients were randomly divided into training (n = 343) and test (n = 147) datasets at a 7:3 ratio. For each patient, 1227 radiomic features were extracted from T1C and T2, respectively. Spearman’s correlation and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select the most informative features. Subsequently, a 5-fold cross-validation was used to compare the performance of different classification algorithms, and logistic regression was chosen to predict the risk of bone invasion. ResultsEight radiomic features were selected from T1C and T2 respectively, and three models were built using radiomic features. The radiomic models derived from T1C alone or a combination of T1C and T2 had the best performance in predicting risk of bone invasion, with areas under the curve in the training dataset of 0.714 [95 % CI, 0.660−0.768] and 0.722 [95 % CI, 0.668−0.776] and in the test datasets of 0.715 [95 % CI, 0.632−0.798] and 0.713 [95 % CI, 0.628−0.798], respectively. ConclusionsThe radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network. Four hundred two brain tumor cases were retrieved, yielding associations between 182 computed tomography (CT) and T1-weighted MRI (T1) scans, 180 CT and contrast-enhanced T1-weighted MRI (T1-Gd) scans, and 40 CT, T1, and T1-Gd scans. A 3D CNN was used to map T1 or T1-Gd onto CT scans and evaluate the importance of different components. First, the training set size's influence on testing set accuracy was assessed. Moreover, we evaluated the MRI sequence impact, using T1-only and T1-Gd-only cohorts. We then investigated 4 MRI standardization approaches (histogram-based, zero-mean/unit-variance, white stripe, and no standardization) based on training, validation, and testing cohorts composed of 242, 81, and 79 patients cases, respectively, as well as a bias field correction influence. Finally, 2 networks, namely HighResNet and 3D UNet, were compared to evaluate the architecture's impact on the pCT quality. The mean absolute error, gamma indices, and dose-volume histograms were used as evaluation metrics. Generating models using all the available cases for training led to higher pCT quality. The T1 and T1-Gd models had a maximum difference in gamma index means of 0.07 percentage point. The mean absolute error obtained with white stripe was 78 ± 22 Hounsfield units, which slightly outperformed histogram-based, zero-mean/unit-variance, and no standardization (P < .0001). Regarding the network architectures, 3%/3 mm gamma indices of 99.83% ± 0.19% and 99.74% ± 0.24% were obtained for HighResNet and 3D UNet, respectively. Our best pCTs were generated using more than 200 samples in the training data set. Training with T1 only and T1-Gd only did not significantly affect performance. Regardless of the preprocessing applied, the dosimetry quality remained equivalent and relevant for potential use in clinical practice.

Survival, Prognostic Factors, and Volumetric Analysis of Extent of Resection for Anaplastic Gliomas.

PurposeThe aim of this study is to evaluate the survival rate and prognostic factors of anaplastic gliomas according to the 2016 World Health Organization classification, including extent of resection (EOR) as measured by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) and the T2-weighted MRI.Materials and MethodsThe records of 113 patients with anaplastic glioma who were newly diagnosed at our institute between 2000 and 2013 were retrospectively reviewed. There were 62 cases (54.9%) of anaplastic astrocytoma, isocitrate dehydrogenase (IDH) wild-type (AAw), 18 cases (16.0%) of anaplastic astrocytoma, IDH-mutant, and 33 cases (29.2%) of anaplastic oligodendroglioma, IDH-mutant and 1p/19q-codeleted.ResultsThe median overall survival (OS) was 48.4 months in the whole anaplastic glioma group and 21.5 months in AAw group. In multivariate analysis, age, preoperative Karnofsky Performance Scale score, O6-methylguanine-DNA methyltransferase (MGMT) methylation status, postoperative tumor volume, and EOR measured from the T2 MRI sequence were significant prognostic factors. The EOR cut-off point for OS measured in contrast-enhanced T1-weighted MRI and T2-weighted MRI were 99.96% and 85.64%, respectively.ConclusionWe found that complete resection of the contrast-enhanced portion (99.96%) and more than 85.64% resection of the non-enhanced portion of the tumor have prognostic impacts on patient survival from anaplastic glioma.

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

Imaging Features Differentiating Vestibular Ganglion from Intracanalicular Schwannoma on Single-Sequence Non-Contrast Magnetic Resonance Imaging Study

This study aimed to identify imaging features on single-sequence noncontrast magnetic resonance imaging (MRI) that differentiate the vestibular ganglion from small intracanalicular schwannomas. Ninety patients (42 men and 48 women; age: 24‒87 years old) with 102 internal auditory canal (IAC) nodules (59 vestibular ganglia and 43 intracanalicular schwannoma) who underwent both single-sequence T2-weighted (T2W) non-contrast enhanced MRI studies and contrast-enhanced T1-weighted (T1W) MRI studies between May 2012 and April 2017 were evaluated. The length, width, distance to the IAC fundus and length/width ratios for all lesions were obtained and compared among groups. Diagnostic performance and cutoff values of the parameters were evaluated with receiver operating characteristics curve analysis. Area under the curve (AUC) value was calculated. Vestibular ganglia have significantly smaller lengths and widths compared to intracanalicular vestibular schwannomas (1.7 ± 0.4 mm and 1.0 ± 0.2 mm versus 5.6 ± 3.0 mm and 3.7 ± 1.5 mm). They are more fusiform in shape compared to vestibular schwannomas (length/width ratio: 1.8 ± 0.4 versus 1.5 ± 0.4). The lesion width demonstrated the highest diagnostic performance (AUC: 0.998). Using a cutoff width of <1.3 mm, the sensitivity, specificity and overall accuracy for diagnosing vestibular ganglia were 97% (57/59), 100% (43/43) and 98% (100/102), respectively. Vestibular ganglia may mimic intracanalicular vestibular schwannomas on a single-sequence T2W MRI. However, a fusiform shape and width <1.3 mm increases confidence in the diagnosis of ganglia. Identifying the vestibular ganglion on single-sequence T2W MRI studies may obviate the need for a contrast-enhanced MRI, reducing the risks of contrast administration, additional scanning time and cost.