Qualitative MR features to identify non-enhancing tumors within glioblastoma's T2-FLAIR hyperintense lesions.

PURPOSE: To identify qualitative MRI features of non-(contrast)-enhancing tumor (nCET) in glioblastoma's T2-FLAIR hyperintense lesion. METHODS: Thirty-three histologically confirmed glioblastoma patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were included in this study. Met-PET was utilized as a surrogate for tumor burden. Imaging features for identifying nCET were searched by qualitative examination of 156 targets. A new scoring system to identify nCET was established and validated by two independent observers. RESULTS: Three imaging features were found helpful for identifying nCET; "Bulky gray matter involvement", "Around the rim of contrast-enhancement (Around-rim)," and "High-intensity on T1WI and low-intensity on T2WI (HighT1LowT2)" resulting in an nCET score = 2 × Bulky gray matter involvement - 2 × Around-rim + HighT1LowT2 + 2. The nCET score's classification performances of two independent observers measured by AUC were 0.78 and 0.80, with sensitivities and specificities using a threshold of four being 0.443 and 0.771, and 0.916 and 0.768, respectively. The weighted kappa coefficient for the nCET score was 0.946. CONCLUSION: The current investigation demonstrated that qualitative assessments of glioblastoma's MRI might help identify nCET in T2/FLAIR high-intensity lesions. The novel nCET score is expected to aid in expanding treatment targets within the T2/FLAIR high-intensity lesions.

Neurite orientation dispersion and density imaging and diffusion tensor imaging to facilitate distinction between infiltrating tumors and edemas in glioblastoma.

BACKGROUND: Glioblastomas are highly infiltrative tumors, and differentiating between non-enhancing tumors (NETs) and vasogenic edema (Edemas) occurring in the non-enhancing T2-weighted hyperintense area is challenging. Here, we differentiated between NETs and Edemas in glioblastomas using neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI). MATERIALS AND METHODS: Data were collected retrospectively from 21 patients with primary glioblastomas, three with metastasis, and two with meningioma as controls. MRI data included T2 weighted images and contrast enhanced T1 weighted images, NODDI, and DTI. Three neurosurgeons manually assigned volumes of interest (VOIs) to the NETs and Edemas. The DTI and NODDI-derived parameters calculated for each VOI were fractional anisotropy (FA), apparent diffusion coefficient (ADC), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index. RESULTS: Sixteen and 14 VOIs were placed on NETs and Edemas, respectively. The ICVF, ISOVF, FA, and ADC values of NETs and Edemas differed significantly (p < 0.01). Receiver operating characteristic curve analysis revealed that using all parameters allowed for improved differentiation of NETs from Edemas (area under the curve = 0.918) from the use of NODDI parameters (0.910) or DTI parameters (0.899). Multiple logistic regression was performed with all parameters, and a predictive formula to differentiate between NETs and Edemas could be created and applied to the edematous regions of the negative control-group images; the tumor prediction degree was well below 0.5, confirming differentiation as edema. CONCLUSIONS: Using NODDI and DTI may prove useful in differentiating NETs from Edemas in the non-contrast T2 hyperintensity region of glioblastomas.

Nonbrain metastases seen on magnetic resonance imaging during metastatic brain tumor screening.

Although metastases found during head magnetic resonance imaging (MRI) are not limited to metastatic brain tumors, the MRI is a very common method for "brain metastasis screening," a modality that is being increasingly performed. In this review, we describe MRI findings of nonbrain metastases and discuss ways to avoid missing these lesions. Metastatic cranial bone tumors are among the most common nonbrain metastatic lesions found on head MRI, followed by leptomeningeal carcinomatosis. The other less-frequent metastatic lesions include those in the ventricle/choroid plexus, the pituitary gland and stalk, and the pineal gland. Metastases in the head and neck area, as well as cranial and intracranial lesions, should be carefully evaluated. Furthermore, direct geographical invasion, perineural spread, and double cancers should also be considered. While it is important to recognize these metastatic lesions on MRI, because they may necessitate a change in treatment strategy that could lead to an improvement in prognosis due to early introduction of therapy, nonbrain lesions should also be given greater attention, given the increasing survival of patients with cancer and advances in MRI technology, such as contrast-enhanced-3D T1-weighted imaging.

Correlation of T1- to T2-weighted signal intensity ratio with T1- and T2-relaxation time and IDH mutation status in glioma.

The current study aimed to test whether the ratio of T1-weighted to T2-weighted signal intensity (T1W/T2W ratio: rT1/T2) derived from conventional MRI could act as a surrogate relaxation time predictive of IDH mutation status in histologically lower-grade gliomas. Strong exponential correlations were found between rT1/T2 and each of T1- and T2-relaxation times in eight subjects (rT1/T2 = 1.63exp-0.0005T1-relax + 0.30 and rT1/T2 = 1.27exp-0.0081T2-relax + 0.48; R2 = 0.64 and 0.59, respectively). In a test cohort of 25 patients, mean rT1/T2 (mrT1/T2) was significantly higher in IDHwt tumors than in IDHmt tumors (p < 0.05) and the optimal cut-off of mrT1/T2 for discriminating IDHmt was 0.666-0.677, (AUC = 0.75, p < 0.05), which was validated in an external domestic cohort of 29 patients (AUC = 0.75, p = 0.02). However, this result was not validated in an external international cohort derived from TCIA/TCGA (AUC = 0.63, p = 0.08). The t-Distributed Stochastic Neighbor Embedding analysis revealed a greater diversity in image characteristics within the TCIA/TCGA cohort than in the two domestic cohorts. The failure of external validation in the TCIA/TCGA cohort could be attributed to its wider variety of original imaging characteristics.

Ultrafast cervcial spine MRI protocol using deep learning-based reconstruction: Diagnostic equivalence to a conventional protocol.

PURPOSE: A major drawback of magnetic resonance imaging (MRI) is its limited imaging speed. This study proposed an ultrafast cervical spine MRI protocol (2 min 57 s) using deep learning-based reconstruction (DLR) and compared the diagnostic results to those of conventional MRI protocols (12 min 54 s). METHODS: Fifty patients who underwent cervical spine MRI using both conventional and ultrafast protocols, including sagittal T1-weighted, T2-weighted, short-TI inversion recovery, and axial T2*-weighted imaging were included in this study. The ultrafast protocol shortened the acquisition time to approximately-one-fourth of that of the conventional protocol by reducing the phase matrix, oversampling rate, and number of excitations, and by applying compressed sensing. To compensate for the decreased signal-to-noise ratio caused by acceleration, noise reduction using DLR was performed. For image interpretation, three neuroradiologists graded or classified degenerative changes, including central canal stenosis, foraminal stenosis, endplate degeneration, disc degeneration, and disc hernia. The presence of other pathologies was also recorded. Given the absence of a reference standard, we tested the interchangeability of the two protocols by calculating the 95% confidence interval (CI) of the individual equivalence index. We also assessed the inter-protocol intra-reader agreement using kappa statistics. RESULTS: Except for endplate degeneration, the 95 % CI of the individual equivalence index for all variables did not exceed 5 %, indicating interchangeability between the two protocols. The kappa values ranged from 0.600 to 0.977, indicating substantial to almost perfect agreement. CONCLUSIONS: The proposed ultrafast MRI protocol yielded almost equivalent diagnostic results compared as the conventional protocol.

Prediction and Visualization of Non-Enhancing Tumor in Glioblastoma via T1w/T2w-Ratio Map.

One of the challenges in glioblastoma (GBM) imaging is to visualize non-enhancing tumor (NET) lesions. The ratio of T1- and T2-weighted images (rT1/T2) is reported as a helpful imaging surrogate of microstructures of the brain. This research study investigated the possibility of using rT1/T2 as a surrogate for the T1- and T2-relaxation time of GBM to visualize NET effectively. The data of thirty-four histologically confirmed GBM patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were collected for analysis. Two of them also underwent MR relaxometry with rT1/T2 reconstructed for all cases. Met-PET was used as ground truth with T2-FLAIR hyperintense lesion, with >1.5 in tumor-to-normal tissue ratio being NET. rT1/T2 values were compared with MR relaxometry and Met-PET. rT1/T2 values significantly correlated with both T1- and T2-relaxation times in a logarithmic manner (p < 0.05 for both cases). The distributions of rT1/T2 from Met-PET high and low T2-FLAIR hyperintense lesions were different and a novel metric named Likeliness of Methionine PET high (LMPH) deriving from rT1/T2 was statistically significant for detecting Met-PET high T2-FLAIR hyperintense lesions (mean AUC = 0.556 ± 0.117; p = 0.01). In conclusion, this research study supported the hypothesis that rT1/T2 could be a promising imaging marker for NET identification.

Whole-body MRI: detecting bone metastases from prostate cancer.

Whole-body magnetic resonance imaging (WB-MRI) is currently used worldwide for detecting bone metastases from prostate cancer. The 5-year survival rate for prostate cancer is > 95%. However, an increase in survival time may increase the incidence of bone metastasis. Therefore, detecting bone metastases is of great clinical interest. Bone metastases are commonly located in the spine, pelvis, shoulder, and distal femur. Bone metastases from prostate cancer are well-known representatives of osteoblastic metastases. However, other types of bone metastases, such as mixed or inter-trabecular type, have also been detected using MRI. MRI does not involve radiation exposure and has good sensitivity and specificity for detecting bone metastases. WB-MRI has undergone gradual developments since the last century, and in 2004, Takahara et al., developed diffusion-weighted Imaging (DWI) with background body signal suppression (DWIBS). Since then, WB-MRI, including DWI, has continued to play an important role in detecting bone metastases and monitoring therapeutic effects. An imaging protocol that allows complete examination within approximately 30 min has been established. This review focuses on WB-MRI standardization and the automatic calculation of tumor total diffusion volume (tDV) and mean apparent diffusion coefficient (ADC) value. In the future, artificial intelligence (AI) will enable shorter imaging times and easier automatic segmentation.

Magnetic Resonance Relaxometry for Tumor Cell Density Imaging for Glioma: An Exploratory Study via 11C-Methionine PET and Its Validation via Stereotactic Tissue Sampling.

One of the most crucial yet challenging issues for glioma patient care is visualizing non-contrast-enhancing tumor regions. In this study, to test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and 11C-methionine (MET) on positron emission tomography (PET). Moreover, we compared the T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. Regions that presented a T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated a high MET uptake. In addition, the stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850-3200 ms significantly indicated a higher TCD (p = 0.04). However, ADC was unable to show a significant correlation with MET uptake or with TCD. Finally, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET.

Cerebellar preference of luminal A and B type and basal ganglial preference of HER2-positive type breast cancer-derived brain metastases.

The purpose of the current study was to investigate the hypothesis that the spatial distribution of brain metastases could be affected by the biological subtypes of breast cancer. CT (n=1) or MRI (n=66) images of 67 patients with a total of 437 treatment-naive brain metastases from breast cancer were retrospectively reviewed. Patients were grouped according to the biological subtype of the tumor [luminal A, 28; luminal B, 9; human epidermal growth factor receptor 2 (HER2) positive, 14; triple-negative breast cancer (TNBC), 16]. All images were standardized to the human brain MRI atlas provided by the Montreal Neurological Institute 152 database. The distribution pattern of brain metastases after image standardization was analyzed. The cerebellum and the frontal lobe were more commonly affected by breast cancer brain metastases. Brain metastases from luminal A and B types of breast cancer arose more often in the cerebellum. Brain metastases from HER2-positive type breast cancer occurred more often in the putamen and the thalamus and less frequently in the cerebellum than other types (P=0.0057). The subtypes of breast cancer are related to differences in the spatial distributions of their brain metastases. These differences may be utilized to plan different cranial irradiation strategies according to the breast cancer subtypes.

MRI imaging features of HIV-related central nervous system diseases: diagnosis by pattern recognition in daily practice.

With the advent of antiretroviral therapy (ART), the prognosis of people infected with human immunodeficiency virus (HIV) has improved, and the frequency of HIV-related central nervous system (CNS) diseases has decreased. Nevertheless, mortality from HIV-related CNS diseases, including those associated with ART (e.g., immune reconstitution inflammatory syndrome) remains significant. Magnetic resonance imaging (MRI) can improve the outlook for people with HIV through early diagnosis and prompt treatment. For example, HIV encephalopathy shows a diffuse bilateral pattern, whereas progressive multifocal leukoencephalopathy, HIV-related primary CNS lymphoma, and CNS toxoplasmosis show focal patterns on MRI. Among the other diseases caused by opportunistic infections, CNS cryptococcosis and CNS tuberculosis have extremely poor prognoses unless diagnosed early. Immune reconstitution inflammatory syndrome shows distinct MRI findings from the offending opportunistic infections. Although distinguishing between HIV-related CNS diseases based on imaging alone is difficult, in this review, we discuss how pattern recognition approaches can contribute to their early differentiation.

Efficacy of endovascular intratumoral embolization for meningioma: assessment using dynamic susceptibility contrast-enhanced perfusion-weighted imaging.

BACKGROUND: In preoperative embolization for intracranial meningioma, endovascular intratumoral embolization is considered to be more effective for the reduction of tumorous vascularity than proximal feeder occlusion. In this study, we aimed to reveal different efficacies for reducing tumor blood flow in meningiomas by comparing endovascular intratumoral embolization and proximal feeder occlusion using dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI). METHODS: 28 consecutive patients were included. DSC-PWI was performed before and after embolization for intracranial meningiomas. Normalized tumor blood volume (nTBV) of voxels of interest of whole tumors were measured from the DSC-PWI data before and after embolization. ΔnTBV% was compared between the cases that received intratumoral embolization and proximal feeder occlusion. RESULTS: ΔnTBV% in the intratumoral embolization group (42.4±29.8%) was higher than that of the proximal feeder occlusion group (15.3±14.3%, p=0.0039). We used three types of embolic materials and ΔnTBV% did not differ between treatments with or without the use of each material: 42.8±42.4% vs 28.7±20.1% for microspheres (p=0.12), 36.1±20.6% vs 28.1±41.1% for n-butyl cyanoacrylate (p=0.33), and 32.3±37.3% vs 34.1±19.0% for bare platinum coils (p=0.77). CONCLUSIONS: The flow reduction effect of intratumoral embolization was superior to that of proximal feeder occlusion in preoperative embolization for intracranial meningioma in an assessment using DSC-PWI.

T2-FLAIR Mismatch Sign Is Caused by Long T1 and T2 of IDH-mutant, 1p19q Non-codeleted Astrocytoma.

T2-fluid-attenuated inversion recovery images (FLAIR) mismatch sign is now known to be a specific yet insensitive image feature for IDH-mutant, 1p19q non-codeleted astrocytoma. The current study revealed that lesion presenting T2-FLAIR mismatch exhibited extremely long T1- and T2-relaxation time while T2-FLAIR matched lesions showed low to moderate values. On the other hand, IDH-wildtype tumors presented noticeably short T1- and T2-relaxation time. These different relaxation time characteristics seemed to render T2-FLAIR mismatch sign of becoming such a unique and specific image feature for IDH-mutant, 1p19q non-codeleted astrocytoma.

Impact of Inversion Time for FLAIR Acquisition on the T2-FLAIR Mismatch Detectability for IDH-Mutant, Non-CODEL Astrocytomas.

The current research tested the hypothesis that inversion time (TI) shorter than 2,400 ms under 3T for FLAIR can improve the diagnostic accuracy of the T2-FLAIR mismatch sign for identifying IDHmt, non-CODEL astrocytomas. We prepared three different cohorts; 94 MRI from 76 IDHmt, non-CODEL Lower-grade gliomas (LrGGs), 33 MRI from 31 LrGG under the restriction of FLAIR being acquired with TI < 2,400 ms for 3T or 2,016 ms for 1.5T, and 112 MRI from 112 patients from the TCIA/TCGA dataset for LrGG. The presence or absence of the "T2-FLAIR mismatch sign" was evaluated, and we compared diagnostic accuracies according to TI used for FLAIR acquisition. The T2-FLAIR mismatch sign was more frequently positive when TI was shorter than 2,400 ms under 3T for FLAIR acquisition (p = 0.0009, Fisher's exact test). The T2-FLAIR mismatch sign was positive only for IDHmt, non-CODEL astrocytomas even if we confined the cohort with FLAIR acquired with shorter TI (p = 0.0001, Fisher's exact test). TCIA/TCGA dataset validated that the sensitivity, specificity, PPV, and NPV of the T2-FLAIR mismatch sign to identify IDHmt, non-CODEL astrocytomas improved from 31, 90, 79, and 51% to 67, 94, 92, and 74%, respectively and the area under the curve of ROC improved from 0.63 to 0.87 when FLAIR was acquired with shorter TI. We revealed that TI for FLAIR impacts the T2-FLAIR mismatch sign's diagnostic accuracy and that FLAIR scanned with TI < 2,400 ms in 3T is necessary for LrGG imaging.

Validation of magnetic resonance imaging-based automatic high-grade glioma segmentation accuracy via 11C-methionine positron emission tomography.

Brain Tumor Image Analysis (BraTumIA) is a fully automated segmentation tool dedicated to detecting brain tumors imaged by magnetic resonance imaging (MRI). BraTumIA has recently been applied to several clinical investigations; however, the validity of this novel method has not yet been fully examined. The present study was conducted to validate the quality of tumor segmentation with BraTumIA in comparison with results from 11C-methionine positron emission tomography (MET-PET). A total of 45 consecutive newly diagnosed high-grade gliomas imaged by MRI and MET-PET were analyzed. Automatic tumor segmentation was conducted by BraTumIA and the resulting segmentation images were registered to MET-PET. Three-dimensional conformal association between these two modalities was calculated, considering MET-PET as the gold standard. High underestimation and overestimation errors were observed in tumor segmentation calculated by BraTumIA compared with MET-PET. Furthermore, when the tumor/normal ratio threshold was set at 1.3 from MET-PET, the BraTumIA false-positive fraction was ~0.4 and the false-negative fraction was 0.9. By tightening this threshold to 2.0, the BraTumIA false-positive fraction was 0.6 and the false-negative fraction was 0.6. Following comparison of segmentation performance with BraTumIA with regard to glioblastoma (GBM) and World Health Organization (WHO) grade III glioma, GBM exhibited better segmentation compared with WHO grade III glioma. Although BraTumIA may be able to detect enhanced tumors, non-enhancing tumors and necrosis, the spatial concordance rate with MET-PET was relatively low. Careful interpretation is therefore required when using this technique.

Preservation of Motor Function After Resection of Lower-Grade Glioma at the Precentral Gyrus and Prediction by Presurgical Functional Magnetic Resonance Imaging and Magnetoencephalography.

BACKGROUND: Intra-axial brain tumors located at anatomically eloquent areas are challenging conditions. On one hand, it is often difficult to pursue maximum extent of resection of tumor in these locations. On the other hand, neuroplasticity occurs in some patients with low-grade glioma, and the primary neural functions are known to sometimes shift from conventional "eloquent cortices." CASE DESCRIPTION: In a patient with a lower-grade glioma located at the precentral gyrus, shift of primary motor function from the precentral gyrus to the postcentral gyrus was detected on magnetoencephalography and functional magnetic resonance imaging. Aggressive removal of the pathologic precentral gyrus was accomplished via awake craniotomy without causing obvious motor function deficit. CONCLUSIONS: This case highlights the importance of preoperative multimodal neurophysiologic imaging in patients with low-grade gliomas in eloquent areas.

Voxel-Based Lesion Mapping of Cryptogenic Stroke in Patients with Advanced Cancer: A Detailed Magnetic Resonance Imaging Analysis of Distribution Pattern.

BACKGROUND: Ischemic stroke is one form of cancer-associated thrombosis that can greatly worsen a patient's performance status. The present investigation aimed to elucidate the characteristic distribution pattern(s) of cryptogenic stroke lesions using a voxel-based lesion-mapping technique and examine the differences in clinical manifestations between cryptogenic and conventional strokes in patients with advanced cancer. METHODS: Data from 43 patients with advanced cancer who developed acute ischemic stroke were retrospectively collected. Stroke etiology was grouped into either cryptogenic or conventional stroke etiology according to the ASCO stroke score. Clinical data were reviewed, and voxel-based lesion mapping using diffusion-weighted imaging (DWI) was performed to visualize the cross-patient spatial distribution of the lesions. RESULTS: Of the 43 patients, 25 were classified as having cryptogenic stroke etiology and 18 were classified as having conventional stroke etiology. Median survival time of patients from stroke onset was 96 days for cryptogenic stroke etiology and 570 days for conventional stroke etiology (P = .01). D-dimer of patients was significantly higher in cryptogenic stoke etiology than in conventional stroke etiology (P = .006). Voxel-based lesion mapping showed that DWI hyperintense lesions accumulated at cortical and internal watershed areas of the cerebrum and at the vascular border zone of the superior cerebellar and posterior inferior cerebellar arteries at the cerebellum. CONCLUSIONS: Voxel-based lesion mapping for cryptogenic stroke in patients with advanced cancer showed that lesions accumulated at vascular border zones within the brain both at the cerebrum and at the cerebellum, but not at perforating arterial territories.

Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas.

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma.

Feasibility of non-contrast-enhanced four dimensional (4D) MRA in head and neck tumors, comparison with contrast-enhanced 4D MRA.

BACKGROUND: Information of tumor vascular architecture and hemodynamics is important in treating patients with head and neck tumors (HNTs). The purpose of this study is to investigate the feasibility of non-contrast-enhanced four-dimensional magnetic resonance angiography (non-CE 4DMRA) using arterial spin labeling for anatomical and hemodynamic evaluation of vascularity of head and neck tumors. RESULTS: Non-CE 4DMRA images of 15 patients with HNTs were compared with those of contrast-enhanced 4DMRA (CE 4DMRA) by two independent observers. For qualitative evaluation, overall image quality, visualization of arterial branches and main arterial tumor feeders were assessed. For hemodynamic evaluation, signal-intensity-over-time curves within the tumors were compared. The sensitivity of non-CE 4DMRA for the identification of arterial branches and the main arterial tumor feeders was 75 and 20 %, respectively (interobserver agreement, κ = 0.56 and 0.54, respectively), while that of CE 4DMRA was 99 and 95 %, respectively (interobserver agreement, κ = 0.62 and 0.70, respectively). All three arterial/hypervascularized tumors determined on CE 4DMRA showed distinct signal-intensity-over-time curve pattern on non-CE 4DMRA, with distinct peak and wash out phases. Other tumors showed no wash out on non-CE 4DMRA. CONCLUSIONS: Use of non-CE 4DMRA for the anatomical and hemodynamic evaluation of vascularity of head and neck tumors is feasible, although the technique needs to be improved.

Different spatial distributions of brain metastases from lung cancer by histological subtype and mutation status of epidermal growth factor receptor.

BACKGROUND: The purpose of this study was to test the hypothesis that the genetic backgrounds of lung cancers could affect the spatial distribution of brain metastases. METHODS: CT or MR images of 200 patients with a total of 1033 treatment-naive brain metastases from lung cancer were retrospectively reviewed (23 by CT and 177 by MRI). All images were standardized to the human brain MRI atlas provided by the Montreal Neurological Institute 152 database. Locations, depths from the brain surface, and sizes of the lesions after image standardization were analyzed. RESULTS: The posterior fossa, the anatomic "watershed areas," and the gray-white matter junction were confirmed to be more commonly affected by lung cancer brain metastases, and brain metastases with epidermal growth factor receptor (EGFR) L858R mutation occurred more often in the caudate, cerebellum, and temporal lobe than those with exon 19 deletion of EGFR. Median depths of the lesions from the brain surface were 13.7 mm (range, 8.6-21.9) for exon 19 deleted EGFR, 11.5 mm (6.6-16.8) for L858R mutated, and 15.0 mm (10.0-20.7) for wild-type EGFR. Lesions with L858R mutated EGFR were located significantly closer to the brain surface than lesions with exon 19 deleted or wild-type EGFR (P = .0032 and P < .0001, respectively). Furthermore, brain metastases of adenocarcinoma lung cancer patients with a history of chemotherapy but not molecular targeted therapy were located significantly deeper from the brain surface (P = .0002). CONCLUSION: This analysis is the first to reveal the relationship between EGFR mutation status and the spatial distribution of brain metastases of lung cancer.

Slow-growing primary marginal zone B-cell lymphoma arising in the chest wall in a patient without a history of tuberculosis.

A 57-year-old man with a 15-year history of a right chest wall mass lesion without follow-up for 5 years was admitted to our hospital without any symptoms or evidence of malignancy. On MRI, an additional small subcutaneous mass lesion was found. Histology of both lesions revealed marginal zone B-cell lymphomas. Primary chest wall marginal zone B-cell lymphomas are rare; this report discusses the case and the literature on chest wall lymphomas.