Evaluation of signal intensity changes in dentate nucleus and globus pallidus on magnetic resonance imaging after intrathecal gadolinium-based contrast agent administration.

PURPOSE: Gadolinium deposition has been reported in several normal anatomical structures in the brain after repeated administration of intravenous gadolinium-based contrast agents (GBCAs) used in magnetic resonance imaging (MRI). This study presents preliminary results to see if there is any gadolinium deposition in the dentate nucleus and globus pallidus after using intrathecal GBCAs. METHODS: Between November 2018 and November 2020, 29 patients who underwent intrathecal contrast-enhanced MR cisternography with the suspicion of rhinorrhea were included in this prospective study. In contrast-enhanced MR cisternography, gadoterate meglumine was administered by intrathecal injection at a dose of 1 ml. One month later, patients had a control MRI with 3D T1 SPACE fat-saturated (FS) and susceptibility weighted images (SWI) sequences. The ratio of dentate nucleus signal intensity to middle cerebellar peduncle signal intensity (DN/MCP ratio) and the ratio of globus pallidus signal intensity to thalamus signal intensity (GP/T ratio) were calculated using region of interest (ROI) on pre-contrast and control MRI sequences. RESULTS: There was no significant difference for DN/MCP ratio and GP/T ratio on 3D T1 SPACE FS and SWI sequences after intrathecal GBCAs administration compared to baseline MRI. CONCLUSION: Administration of intrathecal GBCAs did not cause a measurable change in the signal intensity of the dentate nucleus and globus pallidus after a single injection.

Machine Learning Models in Prediction of Treatment Response After Chemoembolization with MRI Clinicoradiomics Features.

PURPOSE: To evaluate machine learning models, created with radiomics and clinicoradiomics features, ability to predict local response after TACE. MATERIALS AND METHODS: 188 treatment-naïve patients (150 responders, 38 non-responders) with HCC who underwent TACE were included in this retrospective study. Laboratory, clinical and procedural information were recorded. Local response was evaluated by European Association for the Study of the Liver criteria at 3-months. Radiomics features were extracted from pretreatment pre-contrast enhanced T1 (T1WI) and late arterial-phase contrast-enhanced T1 (CE-T1) MRI images. After data augmentation, data were split into training and test sets (70/30). Intra-class correlations, Pearson's correlation coefficients were analyzed and followed by a sequential-feature-selection (SFS) algorithm for feature selection. Support-vector-machine (SVM) models were trained with radiomics and clinicoradiomics features of T1WI, CE-T1 and the combination of both datasets, respectively. Performance metrics were calculated with the test sets. Models' performances were compared with Delong's test. RESULTS: 1128 features were extracted. In feature selection, SFS algorithm selected 18, 12, 24 and 8 features in T1WI, CE-T1, combined datasets and clinical features, respectively. The SVM models area-under-curve was 0.86 and 0.88 in T1WI; 0.76, 0.71 in CE-T1 and 0.82, 0.91 in the combined dataset, with and without clinical features, respectively. The only significant change was observed after inclusion of clinical features in the combined dataset (p = 0.001). Higher WBC and neutrophil levels were significantly associated with lower treatment response in univariant analysis (p = 0.02, for both). CONCLUSION: Machine learning models created with clinical and MRI radiomics features, may have promise in predicting local response after TACE. LEVEL OF EVIDENCE: Level 4, Case-control study.

Adrenal lesion classification with abdomen caps and the effect of ROI size.

Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection and classification of lesions in medical imaging heavily rely on several key factors, including the specialist's level of experience, work intensity, and fatigue of the clinician. These factors are critical determinants of the accuracy and effectiveness of the diagnostic process, which in turn has a direct impact on patient health outcomes. With the spread of artificial intelligence, the use of computer-aided diagnosis (CAD) systems in disease diagnosis has also increased. In this study, adrenal lesion classification was performed using deep learning on MR images. The data set used was obtained from the Department of Radiology, Faculty of Medicine, Selcuk University, and all adrenal lesions were identified and reviewed in consensus by two radiologists experienced with abdominal MR. Studies were carried out on two different data sets created by T1- and T2-weighted MR images. The data set consisted of 112 benign and 10 malignant lesions for each mode. Experiments were performed with regions of interest (ROIs) of different sizes to increase the working performance. Thus, the effect of the selected ROI size on the classification performance was assessed. In addition, instead of the convolutional neural network (CNN) models used in deep learning, a unique classification model structure called Abdomen Caps was proposed. When the data sets used in classification studies are manually separated for training, validation, and testing, different results are obtained with different data sets for each stage. To eliminate this imbalance, tenfold cross-validation was used in this study. The best results obtained were 0.982, 0.999, 0.969, 0.983, 0.998, and 0.964 for accuracy, precision, recall, F1-score, area under the curve (AUC) score, and kappa score, respectively.

A different perspective on the dilemma of durotomy or duraplasty in Chiari type 1 malformation surgery.

PURPOSE: Symptomatic Chiari type 1 malformation (CIM) patients may elect to be treated; however, choosing the optimum surgical method remains problematic: posterior fossa decompression with duraplasty (PFDD) or without duraplasty (PFD). Many studies have compared these surgical methods from several perspectives. We looked at soft tissue density (STD) at the foramen magnum to add another perspective to the comparison of PFD with PFDD. MATERIALS AND METHODS: Eighty-six patients who underwent surgery in our clinic were included in the study. We examined preoperative and postoperative MR images taken in the 1st year. We obtained the foramen magnum area (FMA) and soft tissue at the level of the foramen magnum. The STD within the foramen magnum was calculated as the percentage ratio of soft tissue area (STA) to FMA. Anteroposterior diameters of the syrinx cavities in sagittal T2 sections were measured preoperatively and at 1-year postoperatively. Measurements were double-blind and were performed by the neurosurgeon and by the neuroradiologist. RESULTS: There was no statistically significant difference between the postoperative FMA increases, STA changes, STD changes in patients who underwent PFD and PFDD. In this study, there was no statistically significant difference between PFD and PFDD in terms of syrinx changes. CONCLUSIONS: The medium-term anatomical outcome following craniovertebral decompression for CIM, is no different whether performed PFD or PFDD.

Prediction of Response of Hepatocellular Carcinoma to Radioembolization: Machine Learning Using Preprocedural Clinical Factors and MR Imaging Radiomics.

PURPOSE: To create and evaluate the ability of machine learning-based models with clinicoradiomic features to predict radiologic response after transarterial radioembolization (TARE). MATERIALS AND METHODS: 82 treatment-naïve patients (65 responders and 17 nonresponders; median age: 65 years; interquartile range: 11) who underwent selective TARE were included. Treatment responses were evaluated using the European Association for the Study of the Liver criteria at 3-month follow-up. Laboratory, clinical, and procedural information were collected. Radiomic features were extracted from pretreatment contrast-enhanced T1-weighted magnetic resonance images obtained within 3 months before TARE. Feature selection consisted of intraclass correlation, followed by Pearson correlation analysis and finally, sequential feature selection algorithm. Support vector machine, logistic regression, random forest, and LightGBM models were created with both clinicoradiomic features and clinical features alone. Performance metrics were calculated with a nested 5-fold cross-validation technique. The performances of the models were compared by Wilcoxon signed-rank and Friedman tests. RESULTS: In total, 1,128 features were extracted. The feature selection process resulted in 12 features (8 radiomic and 4 clinical features) being included in the final analysis. The area under the receiver operating characteristic curve values from the support vector machine, logistic regression, random forest, and LightGBM models were 0.94, 0.94, 0.88, and 0.92 with clinicoradiomic features and 0.82, 0.83, 0.82, and 0.83 with clinical features alone, respectively. All models exhibited significantly higher performances when radiomic features were included (P = .028, .028, .043, and .028, respectively). CONCLUSIONS: Based on clinical and imaging-based information before treatment, machine learning-based clinicoradiomic models demonstrated potential to predict response to TARE.

Relationship between cervical spinal cord morphometry and clinical disability in patients with multiple sclerosis

SUMMARY OBJECTIVE: Multiple sclerosis is an autoimmune disease that commonly affects the cervical part of the spinal cord. The aim of this study was to evaluate the relationship between cervical spinal cord atrophy and clinical disability in multiple sclerosis patients. METHODS: We examined the cervical spinal cord area measurements of 64 multiple sclerosis patients and 64 healthy control groups over the images obtained by a T2-weighted magnetic resonance imaging device. RESULTS: The C2-3, C3-4, C4-5, and C6-7 axial cross-sectional surface area values of the patient group were statistically lower than those of the control group (p<0.05). A negative correlation was found between patients' Expanded Disability Status Scale scores and C4-5, C5-6, and C6-7 axial area (axial area p<0.05; r1=-0.472, r2=-0.513, and r3=-0.415). CONCLUSION: When all parameters were evaluated, the data of our control group were found to be higher than the multiple sclerosis groups. There appears to be a significant relationship between patients with cervical spinal cord atrophy and an increase in Expanded Disability Status Scale scores.

Can Dynamic Susceptibility Contrast Perfusion Imaging be Utilized to Detect Isocitrate Dehydrogenase Gene Mutation in Gliomas?

AIM: To explore the ability of dynamic susceptibility contrast perfusion imaging (DSC-PI) to detect isocitrate dehydrogenase (IDH) gene mutation in gliomas. MATERIAL AND METHODS: Preoperative DSC-PI data on histopathologically proven gliomas obtained between January 2015 and December 2019 were reviewed retrospectively. All magnetic resonance imaging (MRI) examinations were performed using a 1.5-T scanner. The maximum relative cerebral blood volume (rCBVmax), percentage signal recovery (PSR), and normalized PSR of tumor cores were calculated. Differences in these values between IDH-mutant and wild-type gliomas were compared, and receiver operating characteristic curves were generated. RESULTS: The patients (32 females, 47 males) were aged 21-76 years (mean 50.7 ± 15 years). The rCBVmax and all PSR values differed significantly between patients with IDH-mutant and those with wild-type tumors (p < 0.01 for all comparisons). CONCLUSION: The rCBVmax and PSR values obtained by DSC-PI may facilitate noninvasive detection of the IDH mutation status of gliomas. PSR provided more reliable values for differentiation of IDH-mutant gliomas from wild-type gliomas.

Does clozapine really affect bone mineral density? An experimental study.

PURPOSE: The aim of this study was to investigate the effect of clozapine use on bone tissue by applying computerized tomography, dual-energy X-ray absorptiometry, and histological and biomechanical analyses in an experimental rat model. METHODS: Sixteen female Wistar Albino rats were included in this study. These animals were divided into two groups: the control group and the clozapine group. The animals in the clozapine group received 10 mg/kg clozapine, and the animals in the control group received tap water by oral gavage daily for 28 days. After sacrification, the femurs of the rats were used for radiologic, histologic, dual-energy X-ray absorptiometry, and biomechanical evaluations. RESULTS: Although the mean values of the clozapine group were higher in terms of histological, bone mineral density, and biomechanical evaluations, the statistical analyses were not significantly different. CONCLUSION: Clozapine use did not affect bone density in the rats. Clozapine can be the preferred treatment for patients with schizophrenia to avoid osteoporosis. It will be necessary to conduct further long-term follow-up and controlled studies in animals and humans to confirm these findings.

Human and animal cystic echinococcosis in Konya, Turkey: molecular identification and the first report of E. equinus from human host in Turkey.

Cystic echinococcosis is a neglected, zoonotic disease in Turkey. The disease is commonly seen in rural areas where the local population is in close contact with livestock and dogs. This research aimed to molecularly identify of hydatid cysts in cattle and human isolates from Konya, Turkey. Following sample collection, direct microscopy was performed. After direct examination, total DNA was extracted, and positive PCR products of cox 1 mitochondrial gene (~ 875 bp) were sequenced. A total of 83 hydatid cysts (cattle n = 57 and human n = 26), 82 were identified as Echinococcus granulosus sensu stricto (G1-G3 genotypes), and one human isolate was characterized as Echinococcus equinus (G4 genotype). Fertility rates of cysts belonging to cattle for liver and lung cysts were 93.3% and 80%, respectively. Out of 26 human originated isolates, 18 (69.2%) of cysts were found to be fertile. To the best of our knowledge, this is the first report of E. equinus from human host in Turkey.

Precise discrimination between meningiomas and schwannomas using time-to-signal intensity curves and percentage signal recoveries obtained from dynamic susceptibility perfusion imaging.

BACKGROUND AND PURPOSE: Meningiomas and schwannomas are common extra-axial brain tumors. Discrimination is challenging in some locations when characteristic imaging features are absent. This study investigated the accuracy of percentage signal recoveries obtained from dynamic susceptibility contrast perfusion imaging (DSC-PI) in discriminating meningiomas and schwannomas. MATERIAL AND METHODS: Retrospective database research was conducted. Sixty nine meningioma and 15 schwannoma having DSC-PI between January 2016 and February 2020 were included. Time to signal intensity curves (TSIC) were analyzed and grouped as T1-dominant leakage, T2*-dominant leakage and return to baseline. Relative cerebral blood volume (rCBV), relative mean transit time (rMTT), percentage signal recovery 1 (PSR 1) and PSR 2 values were calculated. The differences between the groups were investigated. Receiver operating characteristic curves were operated. RESULTS: rCBV, rMTT, PSR 1 and PSR 2 values were statistically different between meningiomas and schwannomas. PSR 2 provided the best discrimination. With the cut off value of 1.08 for PSR 2, meningiomas and schwannomas were differentiated with 95.7% sensitivity and 93.3% specificity. TSICs were also different between two groups. Most of meningiomas showed T2*-dominant leakage (78.2%), whereas most of shwannomas showed T1-dominant leakage (93.3%). CONCLUSION: DSC-PI is a useful imaging tool for non-invasive discrimination of meningiomas and schwannomas. Particularly, percentage signal recoveries discriminates meningiomas and schwannomas with high sensitivity and specificity.

Evaluation of benign parotid gland tumors with superb microvascular imaging and shear wave elastography.

Aim of the study: This study aimed to examine the role of superb microvascular imaging and shear wave elastography for the pre-surgical evaluation of common parotid tumors. Material and methods: This single-center prospective study included 37 patients with parotid gland lesions. After institutional review board approval, grayscale, shear wave elastography and superb microvascular imaging ultrasound examinations were performed prior to biopsy or operation. The diagnosis of the lesions was based on cytological/pathological evaluation after the ultrasound examinations. Pleomorphic adenomas and Warthin tumors were compared using the Mann-Whitney U test. A receiver operating characteristic curve analysis was performed to obtain a cut-off value. A multivariate regression analysis was carried out. Results: The mean age of the patients (11 female, 26 male) was 48.2 ± 18. The shear wave elastography parameters of the lesions were not significantly different between pleomorphic adenomas and Warthin tumors, while the vascular index obtained by using superb microvascular imaging was significantly different (p = 0.012). The mean vascular index was 2.9 ± 3.1 in pleomorphic adenomas, and 9.5 ± 9.5 in Warthin tumors. A cut-off value of 4.05 for the vascular index discriminated pleomorphic adenoma and Warthin tumors with 68% sensitivity and 72% specificity (the area under the curve was 0.768). Conclusion: Superb microvascular imaging is a novel ultrasound imaging technique which is useful for the discrimination of pleomorphic adenomas and Warthin tumors.

Clinical, Radiological, and Electrodiagnostic Diagnosis of Pronator Syndrome Concurrent With Carpal Tunnel Syndrome.

PURPOSE: This is a retrospective observational study that assessed the prevalence of positive diagnostic imaging and electrodiagnostic (EDX) findings in patients diagnosed with pronator syndrome (PS), who previously had a carpal tunnel syndrome (CTS) surgery. The other purpose of our study was to determine how often PS occurred and was missed in patients treated surgically for CTS. METHODS: The files of 180 patients who underwent CTS surgery were reviewed retrospectively. We assessed all patients for a diagnosis of PS. We accepted the clinical findings and patient history as the reference standard for the diagnosis of PS. Anteroposterior and lateral radiographs of the elbow, bilateral upper limb magnetic resonance imaging (MRI) studies, and bilateral dynamic forearm ultrasound (US) were performed on patients with clinical symptoms and physical examinations that indicated PS. Bilateral upper limb EDX was also performed for these patients. One patient refused additional tests. RESULTS: A total of 174 extremities in 146 patients were included in the study. Pronator syndrome was diagnosed by 2 hand surgeons in 22 extremities (19 patients) through a clinical evaluation that included a history and physical examination. Diagnostic testing was positive for findings of PS in 24% of extremities (5 of 21) tested by EDX, in 57% of extremities (12 of 21) tested by US, and 5% of extremities (1 of 21) tested by MRI. There was no lower humeral spur that could cause median nerve compression on any plain radiographs. CONCLUSIONS: With clinical evaluation as the reference standard, EDX, US, and MRI are not helpful in making a diagnosis of PS concurrent with CTS. TYPE OF STUDY/LEVEL OF EVIDENCE: Diagnostic IV.

An extensive study for binary characterisation of adrenal tumours.

On adrenal glands, benign tumours generally change the hormone equilibrium, and malign tumours usually tend to spread to the nearby tissues and to the organs of the immune system. These features can give a trace about the type of adrenal tumours; however, they cannot be observed all the time. Different tumour types can be confused in terms of having a similar shape, size and intensity features on scans. To support the evaluation process, biopsy process is applied that includes injury and complication risks. In this study, we handle the binary characterisation of adrenal tumours by using dynamic computed tomography images. Concerning this, the usage of one more imaging modalities and biopsy process is wanted to be excluded. The used dataset consists of 8 subtypes of adrenal tumours, and it seemed as the worst-case scenario in which all handicaps are available against tumour classification. Histogram, grey level co-occurrence matrix and wavelet-based features are investigated to reveal the most effective one on the identification of adrenal tumours. Binary classification is proposed utilising four-promising algorithms that have proven oneself on the task of binary-medical pattern classification. For this purpose, optimised neural networks are examined using six dataset inspired by the aforementioned features, and an efficient framework is offered before the use of a biopsy. Accuracy, sensitivity, specificity, and AUC are used to evaluate the performance of classifiers. Consequently, malign/benign characterisation is performed by proposed framework, with success rates of 80.7%, 75%, 82.22% and 78.61% for the metrics, respectively. Graphical abstract.

Adrenal tumor segmentation method for MR images.

BACKGROUND AND OBJECTIVE: Adrenal tumors, which occur on adrenal glands, are incidentally determined. The liver, spleen, spinal cord, and kidney surround the adrenal glands. Therefore, tumors on the adrenal glands can be adherent to other organs. This is a problem in adrenal tumor segmentation. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. METHODS: This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The proposed hybrid method incorporates many image processing methods, which include active contour, adaptive thresholding, contrast limited adaptive histogram equalization (CLAHE), image erosion, and region growing. RESULTS: The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven metrics: sensitivity, specificity, accuracy, precision, Dice Coefficient, Jaccard Rate, and structural similarity index (SSIM). The proposed method eliminates some of the discussed problems with success rates of 74.84%, 99.99%, 99.84%, 93.49%, 82.09%, 71.24%, 99.48% for the metrics, respectively. CONCLUSIONS: This study presents a new method for adrenal tumor segmentation, and avoids some of the problems preventing accurate segmentation, especially for cyst-based tumors.

Incidentally Detected Kaposi Sarcoma of Adrenal Gland with Anaplastic Features in an HIV Negative Patient.

Kaposi sarcoma (KS), a vascular tumor caused by infection with human herpesvirus 8 (HHV8), is a systemic disease that can present with cutaneous lesions with or without visceral involvement. Very few cases of KS, most of which were associated with AIDS, have been reported in the adrenal gland. Anaplastic transformation of KS is a rare clinical presentation known as an aggressive disease with local recurrence and metastatic potential. We report here a 47-year-old HIV negative male presented with extra-adrenal symptoms and had an incidentally detected anaplastic adrenal KS exhibited aggressive clinical course. To the best of our knowledge, this is the first case of anaplastic primary adrenal KS without mucocutaneous involvement but subsequently developed other side adrenal metastases in an HIV negative patient.