Differentiation between supratentorial pilocytic astrocytoma and extraventricular ependymoma using multiparametric MRI.

BACKGROUND: The differentiation of supratentorial pilocytic astrocytomas (STPAs) and supratentorial extraventricular ependymomas (STEEs) is clinically pivotal because of distinct therapeutic management and prognosis, which is sometimes challenging to both neuroradiologists and pathologists. PURPOSE: To explore and compare the conventional and advanced magnetic resonance imaging (MRI) features between STPA and STEE. MATERIAL AND METHODS: A total of 23 patients with STPAs and 23 patients with STEEs were reviewed in this study. All patients performed conventional MRI, susceptibility-weighted imaging (SWI), and diffusion-weighted imaging (DWI), and 34 patients (17 with STPAs and 17 with STEEs) examined dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) in addition. Clinical data, conventional MRI features, minimum relative apparent diffusion coefficient ratio (rADCmin), and maximum relative cerebral blood volume ratio (rCBVmax) were compared between the two groups and subgroups. The optimal cutoff values of rADCmin and rCBVmax with sensitivity and specificity were calculated. RESULTS: STPA manifested similar to STEE as a solid-cystic mass but more frequently presented with a marked enhancing deep nodule (P = 0.02), no peritumoral edema (P = 0.036), higher rADCmin value (2.0 ± 0.5 vs. 0.9 ± 0.2; P < 0.001), and lower rCBVmax value (2.1 ± 0.4 vs. 14.4 ± 5.5; P < 0.001). The cutoff value of >1.39 for rADCmin and ≤ 2.81 for rCBVmax produced a high sensitivity of 95.65% and 100.0%, respectively, and all produced a specificity of 100.0% in differentiating STPAs from STEEs. CONCLUSION: Multiparametric MRI techniques including conventional MRI, DWI, and DSC-PWI contribute to the differential diagnosis of STPA and STEE.

A Review of Neuroimaging in Rare Neurodegenerative Diseases.

PURPOSE: Due to the variety of clinical symptoms that occur in rare neurodegenerative diseases and difficulties in the correct diagnosis, there is a need to learn their characteristic imaging findings by using conventional MRI. That knowledge helps to determine the appropriate differential diagnosis and avoid misdiagnosis. The aim of this review is to present the typical neuroimaging signs of the selected neurodegenerative disorders and to create a practical approach to imaging findings useful in everyday clinical practice. Images: Images of progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), Creutzfeldt-Jakob disease (CJD), Wilson's disease (WD), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) are provided to visualize and distinguish the typical features of those diseases and therefore to assist neurologists and neuroradiologists in decision-making process. CONCLUSIONS: It is important to know the characteristic MRI features of rare neurodegenerative diseases and to use them in everyday clinical practice. MRI is a valuable tool when considering the initial diagnosis because it is proven to be very useful in the differentiation of more advanced stages of the rare neurodegenerative diseases but also from other neurodegenerative disorders.

IDH genotypes differentiation in glioblastomas using DWI and DSC-PWI in the enhancing and peri-enhancing region.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation has diagnostic and prognostic values in glioblastomas. Peritumoral invasion of glioma cells is a cardinal feature of glioblastomas. PURPOSE: To evaluate the contribution of DWI and DSC-PWI in the enhancing and peri-enhancing region for discriminating glioblastomas IDH genotypes, and the diagnostic values of combining two techniques in the peri-enhancing region compared with those in the enhancing region. MATERIAL AND METHODS: We retrospectively reviewed the conventional MRI (cMRI), DWI and DSC-PWI obtained from 10 patients with IDH-mutated (IDH-m) glioblastomas and 65 patients with IDH wild-type (IDH-w) glioblastomas. Features of cMRI, relative minimum ADC in the enhancing region (rADCmin-t) and peri-enhancing area (rADCmin-p), and relative maximum CBV values in the enhancing region (rCBVmax-t) and peri-enhancing region (rCBVmax-p) were compared between two groups. Receiver operating characteristic curves and logistic regression analysis were used to assess diagnostic performance. RESULTS: IDH-m glioblastomas tended to present in frontal lobes and younger patients. The rADCmin-t (P = 0.042) were significantly lower in IDH-w than IDH-m. Both rCBVmax-t and rCBVmax-p showed significant differences between two subgroups (all P < 0.001). The optimal cutoff values in prediction of IDH-m were >0.98 for rADCmin-t, <7.27 for rCBVmax-t, and < 0.97 for rCBVmax-p. Multivariate logistic regression revealed that the combination of rADCmin-t and rCBVmax-t yielded the highest sensitivity and specificity. CONCLUSION: The rCBVmax-t or rCBVmax-p may serve as preferable and comparable imaging biomarkers for evaluation of glioblastomas IDH status. The combination of rADCmin-t and rCBVmax-t may yield the maximum predictive power for differentiating IDH status.

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas.

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models' performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.

Evaluation of subscapularis tendon tears of the anterosuperior aspect using radial-sequence magnetic resonance imaging.

BACKGROUND: Magnetic resonance imaging (MRI) is widely used to diagnose subscapularis tendon tears; however, it is difficult to assess the anterosuperior aspect of these tears. Radial-sequence MRI can reveal the fiber components of the anterosuperior aspect, from perpendicular, by overcoming the partial volume effect. We aimed to classify the insertion of subscapularis tendon tears on radial-sequence MRI and determine the effectiveness of radial-sequence MRI for subscapularis tendon tear assessments. METHODS: We retrospectively investigated 196 patients (mean age, 66.7 ± 9.0 years; 118 men, 78 women) who underwent 1.5 T MRI before arthroscopic rotator cuff repair. Radial-sequence MRI findings of the anterosuperior aspect insertion of the subscapularis tendon were classified into five grades, and intraoperative findings compared with preoperative conventional MRI and radial-sequence MRI. We calculated sensitivity, specificity, accuracy, and positive and negative predictive values. Interobserver and intraobserver reliability for radial-sequence MRI classification was calculated using kappa (κ). RESULTS: Conventional MRI sensitivity of subscapularis tendon tears was 45.3%; specificity, 95.8%; accuracy, 82.1%; positive predictive value, 80.0%; and negative predictive value, 82.5%. Radial-sequence MRI sensitivity was 92.5%; specificity, 88.1%; accuracy, 89.3%; positive predictive value, 74.2%; and negative predictive value, 96.9%. Sensitivity (P < .001), accuracy (P = .04), specificity (P = .02), and negative predictive values (P < .001) in radial-sequence MRI were significantly higher than those in conventional MRI. Intraobserver and interobserver reliabilities for radial-sequence MRI classification were κ = 0.78 and 0.65, respectively, corresponding to high reproducibility, and defined as good. CONCLUSION: We provide evidence that radial-sequence MRI is an effective tool to evaluate subscapularis tendon tears, especially before surgery.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Low-Functioning Autism and Nonsyndromic Intellectual Disability: Magnetic Resonance Imaging (MRI) Findings.

Previous neuroradiologic studies reported a high incidence of abnormalities in low-functioning autistic children. In this population, it is difficult to know which abnormality depends on autism itself and which is related to intellectual disability associated with autism. The aim of this study was to evaluate the frequency of neuroradiologic abnormalities in low-functioning autistic children compared to Intellectual Quotient and age-matched nonsyndromic children, using the same set of magnetic resonance imaging (MRI) sequences. MRI was rated as abnormal in 44% of autistic and 54% of children with intellectual disability. The main results were mega cisterna magna in autism and hypoplastic corpus callosum in intellectual disability. These abnormalities are morphologically visible signs of altered brain development. These findings, more frequent than expected, are not specific to the 2 conditions. Although MRI cannot be considered mandatory, it allows an in-depth clinical assessment in nonsyndromic intellectual-disabled and autistic children.

Clinical characteristics associated with corticospinal tract hyperintensity on magnetic resonance imaging in patients with amyotrophic lateral sclerosis.

OBJECTIVE: The usefulness of conventional magnetic resonance imaging (C-MRI) for diagnosing amyotrophic lateral sclerosis (ALS) remains controversial. The aim of this study was to investigate the utility of C-MRI in identifying ALS, specifically the association between corticospinal tract (CST) hyperintensity on C-MRI and clinical characteristics in patients with ALS. METHODS: Between June 2008 and April 2012, we retrospectively enrolled consecutive patients diagnosed with sporadic ALS who underwent C-MRI. Patients with ALS were classified as definite-phase ALS (D-ALS) and indefinite-phase ALS (ID-ALS). We focused on the hyperintensity of T2-weighted images in the CST in patients with ALS. Based on the MRI results, we divided patients into two groups: a positive CST group showing CST hyperintensity; and a negative CST group with no such findings. Clinical characteristics of the two groups were compared. RESULTS: Seventeen patients (median age, 62 years; 8 women, 9 men) were enrolled in this study, with D-ALS in eight (47%) and ID-ALS in nine (53%). Eight patients (47%) showed CST positivity. The rate of CST positivity was higher in patients with D-ALS (75%) than in patients with ID-ALS (22%, p=0.03). CONCLUSIONS: CST positivity appears significantly increased in D-ALS patients. C-MRI can play an important role in diagnosing ALS.

Neuroimaging findings in 41 low-functioning children with autism spectrum disorder: a single-center experience.

The data on the rate of brain imaging abnormalities in autistic spectrum disorders are still inconsistent. A recent study on patients with high-functioning autism found that approximately 90% of children had normal magnetic resonance imaging (MRI) scans whereas an unexpected high rate of MRI abnormalities was reported in 77 nonsyndromic autistic children with or without intellectual disability. The aim of this study was to evaluate the prevalence of neuroradiologic findings in low-functioning autistic children compared to controls matched for age. Minor brain abnormalities were found in 44% of patients and 22% of controls. Our main result is the high rate of mega cisterna magna in autistic patients. High rate of minor neuroradiologic abnormalities in low-functioning autistic patients could contribute to the research about the various endophenotypes and complete the clinical assessment of children with autistic spectrum disorder and intellectual disability.

Magnetic resonance imaging markers for early diagnosis of Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called "prodromal", where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging (MRI) and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.