Dural and Leptomeningeal Diseases: Anatomy, Causes, and Neuroimaging Findings.

Meningeal lesions can be caused by various conditions and pose diagnostic challenges. The authors review the anatomy of the meninges in the brain and spinal cord to provide a better understanding of the localization and extension of these diseases and summarize the clinical and imaging features of various conditions that cause dural and/or leptomeningeal enhancing lesions. These conditions include infectious meningitis (bacterial, tuberculous, viral, and fungal), autoimmune diseases (vasculitis, connective tissue diseases, autoimmune meningoencephalitis, Vogt-Koyanagi-Harada disease, neuro-Behçet syndrome, Susac syndrome, and sarcoidosis), primary and secondary tumors (meningioma, diffuse leptomeningeal glioneuronal tumor, melanocytic tumors, and lymphoma), tumorlike diseases (histiocytosis and immunoglobulin G4-related diseases), medication-induced diseases (immune-related adverse effects and posterior reversible encephalopathy syndrome), and other conditions (spontaneous intracranial hypotension, amyloidosis, and moyamoya disease). Although meningeal lesions may manifest with nonspecific imaging findings, correct diagnosis is important because the treatment strategy varies among these diseases. ©RSNA, 2023 Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.

A Review of Clinical and Imaging Findings in Tumefactive Demyelination.

OBJECTIVE. Tumefactive demyelination mimics primary brain neoplasms on imaging, often necessitating brain biopsy. This article reviews the literature for the clinical and radiologic findings of tumefactive demyelination in various disease processes to facilitate identification of tumefactive demyelination on imaging. CONCLUSION. Both clinical and radiologic findings must be integrated to distinguish tumefactive demyelinating lesions from similarly appearing lesions on imaging. Further research on the immunopathogenesis of tumefactive demyelination and associated conditions will elucidate their interrelationship.

Texture analysis of T2-weighted MRI predicts SDH mutation in paraganglioma.

PURPOSE: Texture analysis can quantify sophisticated imaging characteristics. We hypothesized that 2D textures computed with T2-weighted and post-contrast T1-weighted MRI can predict succinate dehydrogenase (SDH) mutation status in head and neck paragangliomas. METHODS: Our retrospective study included 21 patients (1 to 4 tumors/patient) with 24 pathologically proven paragangliomas in the head and neck. Fourteen lesions (58%) were SDH mutation-positive. All patients underwent T2-weighted and post-contrast T1-weighted MRI sequences. Three 2D texture features of dependence non-uniformity normalized (DNN), small dependence high gray level emphasis (SDHGLE), and small dependence low gray level emphasis (SDLGLE) were calculated. Computed textures between SDH mutants and non-mutants were compared using Mann-Whitney U test. Area under the receiver operating characteristic (AUROC) curve was used to quantify the predictive power of each texture. RESULTS: Only T2-based SDLGLE was statistically significant (p = 0.048), and AUROC was 0.71. Diagnostic accuracy was 70.8%. CONCLUSION: 2D texture parameter of T2-based SDLGLE predicts SDH mutation in head and neck paragangliomas. This noninvasive technique can potentially facilitate further genetic workup.

Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis.

OBJECTIVES: To determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH). METHODS: NECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels. The reference standard was the histological result of a liver biopsy. A predictive model for NASH was developed using parameters derived from the learning dataset that demonstrated areas under the receiver operating characteristic curve (AUC) of >0.65. The resulting model was then applied to the validation dataset. RESULTS: In patients without suspected fibrosis, the texture parameter mean without filter and skewness with a 2-mm filter were selected for the NASH prediction model. The AUC of the predictive model for the validation dataset was 0.94 and the accuracy was 94%. In patients with suspicion of fibrosis, the mean without filtration and kurtosis with a 4-mm filter were selected for the NASH prediction model. The AUC for the validation dataset was 0.60 and the accuracy was 42%. CONCLUSIONS: In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. KEY POINTS: • In patients without suspicion of fibrosis, NECT texture analysis effectively predicted NASH. • The mean without filtration and skewness with a 2-mm filter were modest predictors of NASH in patients without suspicion of liver fibrosis. • Hepatic fibrosis masks the characteristic texture features of NASH.

Usefulness of T2 star-weighted imaging in ovarian cysts and tumors.

AIM: The aim of this study was to investigate the prevalence of hypointensity on T2 star-weighted imaging (T2*WI), which is useful for detecting hemosiderin, in endometriomas and other ovarian tumors. The efficacy of detecting adhesions around ovarian tumors was also investigated. METHODS: Pelvic magnetic resonance (MR) examinations, including T2*WI, were carried out. The inclusion criteria were female patients with ovarian surgical treatments. One hundred seventeen patients with a total of 147 lesions were enrolled. Two radiologists retrospectively evaluated MR imaging (MRI) to predict ovarian pathology and the presence of adhesions. T2*WI hypointensity of the inside and outside along ovarian cysts/tumors was utilized to predict pathological diagnoses and the presence of adhesions, respectively. The kappa scores were calculated to measure interobserver agreement on MRI findings. The MRI interpretations were compared with the results of pathological investigation and surgical observations. RESULTS: Hypointensity inside along the cyst walls on T2*WI was observed in 100 out of 106 lesions of endometriomas (94.3%), and three out of 41 non-endometrial ovarian cysts/tumors (7.3%). Four different patterns of T2*WI were observed in ovarian cysts/tumors. The kappa score regarding T2*WI hypointensity inside along the cyst walls was 0.633. Using conventional routine pelvic MRI, the sensitivity for detecting adhesions around ovarian cysts was 84.5%. By adding T2*WI, the sensitivity improved to 91.4% (P < 0.01). With conventional methods to predict adhesions, the kappa score was 0.660. After adding T2*WI to the conventional methods, the kappa score was 0.767. CONCLUSION: Hypointensity on T2*WI was observed frequently in endometrioma. T2*WI also improved the sensitivity for detecting adhesions around ovarian cysts/tumors.

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography.

Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant breast masses identified by elastography on ultrasound screening. A data set of the elastography images of 245 breast masses (146 benign, 99 malignant) in 239 consecutive patients was retrospectively obtained. The data set was randomly split into training (55%), validation (25%) and test (20%) cohorts. A deep learning model predicting the probability of malignancy was constructed using GoogLeNet architectures (pre-trained by ImageNet) with 50 epochs. The model was then applied to the test data, and the results were compared with those obtained by evaluating the fat-to-lesion ratio (FLR) and by a 5-point visual color assessment (elasticity score). The receiver operating characteristic (ROC) curve was calculated to evaluate the performance of the model. The DeLong test was used to compare the areas under the ROC curve (AUCs). The CNN, FLR and elasticity score had a sensitivity of 0.800, 0.800 and 0.350; specificity of 0.966, 0.586 and 0.931; accuracy of 0.898, 0.673 and 0.694; positive predictive value of 0.941, 0.571 and 0.778; negative predictive value of 0.875, 0.810 and 0.675; and AUC of 0.895, 0.693 and 0.641, respectively. The AUC of the CNN was significantly higher than that of the FLR or elasticity score (p < 0.001). A CNN-based deep learning model for predicting benign or malignant breast masses revealed better diagnostic performance than did FLR or elasticity score-based estimations on ultrasound elastography. The CNN-based model also increased the positive predictive value from 57%-78% to 94%. Therefore, this model may reduce unnecessary biopsy recommendations for masses detected on breast ultrasound screening.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.

OBJECTIVE: H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imaging (MRI) studies have reported variable rates of tumoral enhancement, necrotic changes, and peritumoral edema in H3K27M-mutant gliomas, with no distinguishing imaging features compared with wild-type gliomas. We aimed to construct an MRI machine learning (ML)-based radiomic model to predict H3K27M mutation in midline gliomas. METHODS: A total of 109 patients from 3 academic centers were included in this study. Fifty patients had H3K27M mutation and 59 were wild-type. Conventional MRI sequences (T1-weighted, T2-weighted, T2-fluid-attenuated inversion recovery, postcontrast T1-weighted, and apparent diffusion coefficient maps) were used for feature extraction. A total of 651 radiomic features per each sequence were extracted. Patients were randomly selected with a 7:3 ratio to create training (n = 76) and test (n = 33) data sets. An extreme gradient boosting algorithm (XGBoost) was used in ML-based model development. Performance of the model was assessed by area under the receiver operating characteristic curve. RESULTS: Pediatric patients accounted for a larger proportion of the study cohort (60 pediatric [55%] vs. 49 adult [45%] patients). XGBoost with additional feature selection had an area under the receiver operating characteristic curve of 0.791 and 0.737 in the training and test data sets, respectively. The model achieved accuracy, precision (positive predictive value), recall (sensitivity), and F1 (harmonic mean of precision and recall) measures of 72.7%, 76.5%, 72.2%, and 74.3%, respectively, in the test set. CONCLUSIONS: Our multi-institutional study suggests that ML-based radiomic analysis of multiparametric MRI can be a promising noninvasive technique to predict H3K27M mutation status in midline gliomas.

Comprehensive review of Wernicke encephalopathy: pathophysiology, clinical symptoms and imaging findings.

Wernicke's encephalopathy (WE) is a severe and life-threatening illness resulting from vitamin B1 (thiamine) deficiency. The prevalence of WE has been estimated from 0.4 to 2.8%. If not treated properly, severe neurologic disorders such as Korsakoff psychosis and even death may occur. The classical triad of clinical symptoms (abnormal mental state, ataxia, and ophthalmoplegia) is found in only 16-33% of patients on initial examination. The originally described underlying condition of WE is alcoholism, but it accounts for about 50% of causes of WE. Nonalcoholic patients are also affected by WE and likely to present symptoms and radiological imaging findings different from patients with alcoholism, which further complicates the diagnosis of WE. Being familiar with predisposing causes, symptoms and radiological imaging findings of WE is important for radiologists and clinicians when making the diagnosis to start immediate treatment. This review discusses pathophysiologies, underlying causes, clinical symptoms, imaging findings and their mimics.

Vaginal delivery-related changes in the pelvic organ position and vaginal cross-sectional area in the general population.

PURPOSE: Evaluate the effect of vaginal delivery on pelvic organ positions and vaginal cross-sectional areas. METHODS: MRI of 119 premenopausal women were grouped according to the number of deliveries. The distances from the three 3-reference points (bladder, uterus, and rectum) to two 2-lines (pubococcygeal-line (PCL) and midpubic-line (MPL)), length of H- and M-lines and vaginal cross-sectional area were compared between the groups. RESULTS: With increasing parity, distance from the rectum to PCL tended to increase (nullipara vs. bipara; p<0.01). Vaginal cross-sectional area was larger in bipara and tripara than in nullipara (p<0.01). CONCLUSIONS: Rectal position is more caudally located and vaginal cross-sectional area is larger in bipara than in nullipara.

Role of delayed-time-point imaging during abdominal and pelvic cancer screening using FDG-PET/CT in the general population.

Although delayed-time-point imaging is expected to improve the results of [F]-fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT), how examinees will benefit from dual-time-point imaging versus initial-time-point imaging alone, remains unclear. This study investigated the role of delayed-time-point imaging in improving the results of abdominal and pelvic cancer screening using FDG-PET/CT.This retrospective review included 3131 screening results (average subject age: 55.5 years, range: 40-88 years). First, 2 nuclear medicine physicians tentatively evaluated whole-body initial-time-point PET/CT scans. Subsequently, delayed-time-point imaging of the abdomen and pelvis was performed approximately 150 min after FDG injection, followed by re-evaluation for necessary changes. All changed records were retrospectively reviewed and classified as either lesions that were found in initial-time-point images but were changed into negative by adding delayed scan or newly detected findings of suspected malignancy on delayed-time-point images; lesions suspected to be malignant were subjected to further pathologic review. Diagnostic performance according to sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated and compared between initial-time-point and dual-time-point imaging.Fifty-four records were changed after addition of the delayed-time-point imaging. Of the 105 suspected malignancies on initial-time-point images, 10 were changed into negative following the delayed scan. In addition, 44 lesions were newly detected as suspected malignancies on delayed-time-point images. Thirty-six lesions were proven to be pathologically malignant. Of these, 26 were detected on initial-time-point images, and 8 lesions (gastrointestinal adenocarcinoma, 6; prostate adenocarcinoma, 2) were observed on delayed-time-point images. The sensitivity of dual-time-point imaging (58.6% [34/58]) was significantly higher than that of initial-time-point imaging only (44.8% [26/58]) (P = .005); however, specificity and accuracy of dual-time-point imaging (96.6% [2968/3073] and 95.9% [3002/3131], respectively) were significantly lower than those of initial-time-point imaging only (97.4% [2994/3073] and 96.5% [3020/3131], respectively) (P < .0001 and P = .013, respectively). There were no significant differences in PPV (initial-time-point imaging: 24.8% [26/105], dual-time-point imaging: 24.5% [34/139]) and NPV (98.9% [2994/3026] and 99.2% [2968/3073], respectively).The inclusion of delayed PET/CT in screening examinations facilitated the detection of pathologically malignant lesions, particularly in the gastrointestinal tract, while also detecting benign and false-negative lesions.