Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient.

OBJECTIVES: To elucidate the differences between pleomorphic adenomas and schwannomas occurring in the parapharyngeal space by histogram analyses of apparent diffusion coefficient (ADC) values measured with diffusion-weighted MRI. METHODS: This retrospective study included 29 patients with pleomorphic adenoma and 22 patients with schwannoma arising in the parapharyngeal space or extending into the parapharyngeal space from the parotid region. Using pre-operative MR images, ADC values of tumor lesions showing the maximum diameter were measured. The regions of interest for ADC measurement were placed by contouring the tumor margin, and the histogram metrics of ADC values were compared between pleomorphic adenomas and schwannomas regarding the mean, skewness, and kurtosis by Wilcoxon's rank sum test. Subsequent to the primary analysis which included all lesions, we performed two subgroup analyses regarding b-values and magnetic field strength used for MRI. RESULTS: The mean ADC values did not show significant differences between pleomorphic adenomas and schwannomas for the primary and subgroup analyses. Schwannomas showed higher skewness (p = 0.0001) and lower kurtosis (p = 0.003) of ADC histograms compared with pleomorphic adenomas in the primary analysis. Skewness was significantly higher in schwannomas in all the subgroup analyses. Kurtosis was consistently lower in schwannomas but did not reach statistical significance in one subgroup analysis. CONCLUSIONS: Skewness and kurtosis showed significant differences between pleomorphic adenomas and schwannomas occupying the parapharyngeal space, but the mean ADC values did not. Our results suggest that the skewness and kurtosis of ADC histograms may be useful in differentiating these two parapharyngeal tumors.

Whole-lesion histogram analysis of apparent diffusion coefficient for the assessment of non-mass enhancement lesions on breast MRI.

Objectives: To investigate the application of apparent diffusion coefficient (ADC) histogram analysis in differentiating between benign and malignant breast lesions detected as non-mass enhancement on MRI. Materials and Methods: A retrospective study was conducted for 25 malignant and 26 benign breast lesions showing non-mass enhancement on breast MRI. An experienced radiologist without prior knowledge of the pathological results drew a region of interest (ROI) outlining the periphery of each lesion on the ADC map. A histogram was then made for each lesion. Following a univariate analysis of 18 summary statistics values, we conducted statistical discrimination after hierarchical clustering using Ward's method. A comparison between the malignant and the benign groups was made using multiple logistic regression analysis and the Mann-Whitney U test. A P -value of less than 0.05 was considered statistically significant. Results: Univariate analysis for the 18 summary statistics values showed the malignant group had greater entropy (P < 0.001) and lower uniformity (P < 0.001). While there was no significant difference in mean and skewness values, the malignant group tended to show a lower mean (P = 0.090) and a higher skewness (P = 0.065). Hierarchical clustering of the 18 summary statistics values identified four values (10th percentile, entropy, skewness, and uniformity) of which the 10th percentile values were significantly lower for the malignant group (P = 0.035). Conclusions: Whole-lesion ADC histogram analysis may be useful for differentiating malignant from benign lesions which show non-mass enhancement on breast MRI.

Texture Analysis in Brain Tumor MR Imaging.

Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors.However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples.To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.

MRI findings in posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) is a psychiatric condition that develops after a person experiences one or more traumatic events, characterized by intrusive recollection, avoidance of trauma-related events, hyperarousal, and negative cognitions and mood. Neurophysiological evidence suggests that the development of PTSD is ascribed to functional abnormalities in fear learning, threat detection, executive function and emotional regulation, and contextual processing. Magnetic resonance imaging (MRI) plays a primary role in both structural and functional neuroimaging for PTSD, demonstrating focal atrophy of the gray matter, altered fractional anisotropy, and altered focal neural activity and functional connectivity. MRI findings have implicated that brain regions associated with PTSD pathophysiology include the medial and dorsolateral prefrontal cortex, orbitofrontal cortex, insula, lentiform nucleus, amygdala, hippocampus and parahippocampus, anterior and posterior cingulate cortex, precuneus, cuneus, fusiform and lingual gyri, and the white matter tracts connecting these brain regions. Of these, alterations in the anterior cingulate, amygdala, hippocampus, and insula are highly reproducible across structural and functional MRI, supporting the hypothesis that abnormalities in fear learning and reactions to threat play an important role in the development of PTSD. In addition, most of these structures have been known to belong to one or more intrinsic brain networks regulating autobiographical memory retrieval and self-thought, salience detection and autonomic responses, or attention and emotional control. Altered functional brain networks have been shown in PTSD. Therefore, in PTSD MRI is expected to reflect disequilibrium among functional brain networks, malfunction within an individual network, and impaired brain structures closely interacting with the networks. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:380-396.

Differentiation between solitary fibrous tumors and schwannomas of the head and neck: an apparent diffusion coefficient histogram analysis.

METHODS:: Our study included 5 patients with SFT and 18 patients with schwannoma in the head and neck region for whom pre-operative ADC images were obtained using either 1.5 or 3.0 T MRI system with two b-values. An ADC image that showed the tumor at the largest major diameter was selected for each patient, and a region of interest was set circumscribing the tumor. The histogram distributions of ADC values within the region of interest were compared between SFTs and schwannomas with respect to the mean, standard deviation, median, skewness, kurtosis, and percentile. RESULTS:: The mean and the median ADC values were significantly higher for schwannomas than in SFTs (p = 0.007 in both). Skewness and kurtosis of ADC histograms were significantly lower for schwannomas than for SFTs (p = 0.002 and 0.005, respectively). ADC values differed greatest between the two tumor groups at the 90th percentile, and were significantly higher for schwannomas than for SFTs (p = 0.005). On receiver operating characteristic curve analysis, the area under the curve for kurtosis, skewness, and the 90th percentile ADC values was 0.92, 0.90, and 0.90, respectively. CONCLUSIONS:: Our study suggests that skewness on ADC histograms may be the most useful diagnostic factor, followed by kurtosis and the 90th percentile ADC values, for differentiation between SFTs and schwannomas.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T1-weighted images. METHODS: This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T1-weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. RESULTS: The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. CONCLUSIONS: Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis.

PURPOSE: To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features. METHODS: This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T1-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL. RESULTS: Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM. CONCLUSIONS: Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.

Skull Base Tumors and Tumor-Like Lesions: A Pictorial Review.

A number of tumors and tumor-like non-neoplastic lesions with different cell types on histology occur in the skull base. A wide variety in disease and lesion appearance often complicates the process of radiological diagnosis. The main role of radiographic imaging is the detection and characterization of skull base lesions, with evaluation of the extent of invasion or preservation of adjacent critical organs. Evaluation of the skull base anatomy and surgical planning by using image guidance are also important for surgeons. Computed tomography (CT) and magnetic resonance (MR) imaging are the preferred modalities for the evaluation of skull base lesions. CT and MR are used for lesion detection, tissue characterization and assessment of neurovascular and bone involvement by the lesions. Both modalities provide useful information, one sometimes of greater value than the other. T1-weighted MR imaging is useful in detecting skull base lesions, typically surrounded by abundant fatty bone marrow. T2-weighted MR imaging is generally useful for tumor tissue characterization. CT surpasses MR imaging in evaluating intratumoral calcification and bone destruction or hyperostosis. To date, imaging features have been well-reported in individual skull base tumors; however, correct diagnosis by imaging alone still presents a challenge. Knowledge of clinical issues and awareness of variants of skull base tumors are of help in making a diagnosis. The purpose of this article is to review pertinent clinical issues, typical imaging appearances and certain imaging variations of common skull base lesions.

Variants of meningiomas: a review of imaging findings and clinical features.

Meningiomas are common neoplasms that frequently occur in the brain and spine. Among the 15 histological subtypes of meningiomas in the WHO classification, the incidence of meningothelial meningiomas is the highest, followed by fibrous and transitional meningiomas. These three subtypes account for approximately 80 % of all meningiomas, and thus could be regarded as typical meningiomas. For this reason, other uncommon histological subtypes may be considered as imaging variants, and diagnosis is often challenging for radiologists solely based on imaging features of typical meningiomas. In addition to the histological subtypes, meningiomas arising in atypical locations could be easily mistaken for other lesions more commonly observed in those locations. The purpose of this article is to review characteristic clinical and imaging findings of uncommon meningiomas, including histological variants and meningiomas occurring in relatively rare locations.

Tract-specific analysis of white matter integrity disruption in schizophrenia.

Several studies have suggested that white matter integrity is disrupted in some brain regions in patients with schizophrenia. The purpose of this study was to assess the white matter integrity of the cingulum, uncinate fasciculus, fornix, and corpus callosum using diffusion tensor imaging (DTI). Participants comprised 39 patients with schizophrenia (19 males and 20 females) and 40 age-matched normal controls (20 males and 20 females). We quantitatively assessed the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) of the anterior cingulum, body of the cingulum, uncinate fasciculus, fornix, and corpus callosum on a tract-specific basis using diffusion tensor tractography (DTT). Group differences in FA and ADC between the patients and normal controls were sought. Additional exploratory analyses of the relationship between the FA or ADC and four clinical parameters (i.e., illness duration, positive symptom scores, negative symptom scores, and medication dosage) were performed. Results were analyzed in gender-combined and gender-separated group comparisons. FA was significantly lower on both sides of the anterior cingulum, uncinate fasciculus, and fornix in the schizophrenia patients irrespective of gender group separation. In the gender-combined analyses, significantly higher ADC values were demonstrated in the schizophrenia patients in both sides of the anterior cingulum, body of the cingulum and uncinate fasciculus, the left fornix, and the corpus callosum, compared with those of the normal controls. In the gender-separated analyses, the male patients showed higher ADC in the left anterior cingulum, the bilateral cingulum bodies, and the bilateral uncinate fasciculi. The female patients showed higher ADC in the right anterior cingulum, the left fornix, and the bilateral uncinate fasciculus. In correlation analyses, a significant negative correlation was found between illness duration and ADC in the right anterior cingulum in the gender-combined analyses. The gender-separated analyses found that the male patients had a significant negative correlation between negative symptom scores and FA in the right fornix, a positive correlation between illness duration and FA in the right anterior cingulum, and a negative correlation between illness duration and FA in the left uncinate fasciculus. Our DTI study showed that the integrity of white matter is disrupted in patients with schizophrenia. The results of our sub-analyses suggest that changes in FA and ADC may be related to negative symptom scores or illness duration.

Tract-specific analysis of the superior occipitofrontal fasciculus in schizophrenia.

Diffusion tensor imaging has been highlighted as a non-invasive tool to explore neural connectivity in vivo. Several studies have suggested disorganization of the neural network (circuitry) including the thalamo-prefrontal connection in schizophrenia. Recent research using post-mortem brains showed that the superior occipitofrontal fasciculus (SOFF) fibers extended to the thalamus. We postulated that the SOFF has some relationship with the anatomical structural components of the thalamo-prefrontal circuitry. We quantitatively assessed the diffusion abnormalities of the SOFF using diffusion tensor tractography (DTT) in schizophrenia. Nineteen male patients with schizophrenia and 20 age-matched normal controls were studied. DTT of the SOFF (DTT-SOFF) was visualized using free software (dTV II/VOLUME-ONE), and we performed tract-specific measurement of the fractional anisotropy (FA), then calculated the apparent diffusion coefficient (ADC) of the DTT-SOFF. Tractography and tract-specific analysis of the SOFF were successfully performed in all subjects. All tracts appeared to be connecting the prefrontal area to the thalamus. The mean FA value of patients with schizophrenia [0.376 (S.D. 0.030)] was significantly lower than that of controls [0.432 (S.D. 0.032)], and the ADC value of patients with schizophrenia [0.771 (x10(-3) mm(2)/s) (S.D. 0.041)] was significantly higher than that of controls [0.726 (x10(-3) mm(2)/s) (S.D. 0.027)]. Our results suggest that the so-called SOFF may be a structural component connecting the prefrontal area to the thalamus and that it is deteriorated in schizophrenia.

Utilization of diffusion tensor tractography in combination with spatial normalization to assess involvement of the corticospinal tract in capsular/pericapsular stroke: Feasibility and clinical implications.

PURPOSE: To investigate the feasibility of using spatial normalization in combination with diffusion tensor (DT) corticospinal tractography to assess corticospinal tract (CST) involvement in capsular or pericapsular stroke. MATERIALS AND METHODS: Corticospinal tractograms were created and segmented out using DT imaging (DTI) data from 10 normal volunteers. After spatial normalization was achieved with statistical parametric mapping (SPM), the whole ensemble of tractograms was used as a map of the CST. This was overlaid on the infarction site, which had also been spatially normalized from isotropic diffusion-weighted (DW) images of 14 patients with small symptomatic capsular or pericapsular infarction. We evaluated the extent of CST involvement within the infarction site. Differences were sought in relation to recovery of muscle strength. RESULTS: The CST was engulfed by the infarction in all patients. Muscle strength recovery occurred in 10 of the 14 patients. The extent of cross-sectional and longitudinal involvement in the infarction site was related to motor recovery (P = 0.041). CONCLUSION: An evaluation of the involvement of the CST in cases of capsular or pericapsular infarction utilizing DT fiber tractography in combination with spatial normalization was found to be feasible as it clearly visualized the extent of CST involvement consistent with the symptom.

A case of renal angioleiomyoma with rapid growing: CT findings with histopathological correlation.

We present a rare case of renal angioleiomyoma with rapid growing during one-year and seven-months' follow-up. The CT findings correlated well with the histopathological findings. The patient was a 62-year-old man with right renal mass that was incidentally found on CT. The first CT showed a solitary mass lesion that was 2.0 cm in diameter with a well-defined margin at the right renal medulla. It was gradually enhanced on contrast-enhanced CT. Follow-up CT was performed after one year and seven months, and the mass had grown to 3.8 cm in diameter and showed heterogeneous density on unenhanced CT. The mass had an early enhanced area in the corticomedullary phase, and this enhancement lasted into the nephrographic phase. Another area was gradually enhanced heterogeneously. Right radical nephrectomy was performed, and the histopathological diagnosis was renal angioleiomyoma. The early enhanced area of the mass corresponded to large vessels filled with blood, and the other area was mainly composed of bundles of smooth muscle, fibrous tissue, and minute blood vessels. It is important to know that the CT findings of real angioleiomyoma may show hypervascularity but are atypical for renal cell carcinoma (RCC), and a tendency for growth may be present.