Quantitative evaluation of 67Ga-citrate scintigraphy in the management of nephritis.

In 67Ga-citrate scintigraphy (Ga-S), visual assessment is used by evaluating renal-uptake comparison with liver and spine and is simple and objective. We adopted the standardized uptake value (SUV) for 67Ga-citrate and proposed two quantitative indices, active nephritis volume (ANV) and total nephritis uptake (TNU). This study clarified the utility of new Ga-S-based quantitative indices in nephritis management. Before SUV measurement, the Becquerel calibration factor of 67Ga-citrate was obtained using a phantom experiment. Seventy patients who underwent SPECT/CT imaging were studied. SUV, ANV, and TNU were calculated using a quantitative analysis software for bone SPECT. SUVmean, ANV, and TNU were analyzed using the (1) threshold method (set 40%) and constant-value method for (2) vertebral SUVmax, and (3) vertebral SUVmean. ROC analysis was used to evaluate SUV, ANV, and TNU diagnostic abilities to distinguish nephritis presence and absence as well as interstitial nephritis (IN) and non-IN. The area under the curve (AUC) for nephritis presence or absence had a good value (0.80) for SUVmean (1), ANV (3), and TNU (3). The AUC for differentiation between IN and non-IN groups had a good value (0.80) for SUVmean (1). Thus, the new Ga-S-based quantitative indices were useful to evaluate nephritis and distinguish IN and non-IN.

Characterization of carotid plaques using chemical exchange saturation transfer imaging.

PURPOSE: The preoperative assessment of carotid plaques is necessary to render revascularization safe and effective. The aim of this study is to evaluate the usefulness of chemical exchange saturation transfer (CEST)-MRI, particularly amide proton transfer (APT) imaging as a preoperative carotid plaque diagnostic tool. METHODS: We recorded the APT signal intensity on concentration maps of 34 patients scheduled for carotid endarterectomy. Plaques were categorized into group A (APT signal intensity ≥ 1.90 E-04; n = 12) and group B (APT signal intensity < 1.90 E-04; n = 22). Excised plaques were subjected to histopathological assessment and, using the classification promulgated by the American Heart Association, they were classified as intraplaque hemorrhage-positive [type VI-positive (tVI+)] and -negative [no intraplaque hemorrhage (tVI-)]. RESULTS: Of the 34 patients, 22 (64.7%) harbored tVI+- and 12 (35.3%) had tVI- plaques. The median APT signals were significantly higher in tVI+- than tIVI- patients (2.43 E-04 (IQR = 0.98-4.00 E-04) vs 0.54 E-04 (IQR = 0.14-1.09 E-04), p < .001). Histopathologically, the number of patients with tVI+ plaques was significantly greater in group A (100%, n = 12) than group B (45%, n = 22) (p < .01). The number of symptomatic patients or asymptomatic patients with worsening stenosis was also significantly greater in group A than group B (75% vs 36%, p < .01). CONCLUSION: In unstable plaques with intraplaque hemorrhage and in patients with symptoms or progressive stenosis, the ATP signals were significantly elevated. CEST-MRI studies has the potential for the preoperative assessment of the plaques' characteristics.

Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter.

OBJECTIVE: To clarify the relationship between myelin water fraction (MWF) and R1⋅R2* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWFQPM). MATERIALS AND METHODS: Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R1⋅R2* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R1·R2* and the two-pool model-MWF. The R1·R2* map was converted to MWFQPM map. MWF atlas template was generated using converted to MWF from R1·R2* per WM region. RESULTS: The mean MWF and R1·R2* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s-2, respectively. A non-linear relationship in 48 regions of the WM between MWF and R1·R2* values was observed by quadratic polynomial approximation (R2 ≥ 0.963, P < 0.0001). DISCUSSION: MWFQPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWFQPM may be generated with combined multiple WM regions.

Endometriosis, a common but enigmatic disease with many faces: current concept of pathophysiology, and diagnostic strategy.

Endometriosis is a benign, common, but controversial disease due to its enigmatic etiopathogenesis and biological behavior. Recent studies suggest multiple genetic, and environmental factors may affect its onset and development. Genomic analysis revealed the presence of cancer-associated gene mutations, which may reflect the neoplastic aspect of endometriosis. The management has changed dramatically with the development of fertility-preserving, minimally invasive therapies. Diagnostic strategies based on these recent basic and clinical findings are reviewed. With a focus on the presentation of clinical cases, we discuss the imaging manifestations of endometriomas, deep endometriosis, less common site and rare site endometriosis, various complications, endometriosis-associated tumor-like lesions, and malignant transformation, with pathophysiologic conditions.

Combined visual and quantitative assessment of somatostatin receptor scintigraphy for staging and restaging of neuroendocrine tumors.

PURPOSE: Somatostatin receptor scintigraphy (SRS) using 111In-DTPA-DPhe1-octreotide (pentetreotide) has become an integral part of neuroendocrine neoplasm management. The lack of precise quantification is a disadvantage of SRS. This study aimed to adapt the standardized uptake value (SUV) to SRS, establish the SUV range for physiological uptake in the liver, kidney, and spleen, and elucidate the utility of combined visual and quantitative SRS assessment for staging and restaging of neuroendocrine tumors (NETs). MATERIALS AND METHODS: This study included 21 patients with NETs who underwent 111In-pentetreotide SRS. The SUV of physiological and pathological uptake was calculated using bone single-photon emission computed tomography (SPECT) quantitative analysis software (GI-BONE). For visual analysis, the primary and metastatic lesions were scored visually on planar and SPECT images using a five-point scale. We assessed the relationships between the SUVs of the liver, kidney, and spleen in the dual phase, and among quantitative indices, visual score, and pathological lesions classification. RESULTS: Sixty-three NEN lesions were evaluated. The mean ± standard deviation maximum SUVs (SUVmax) were liver: 4 h, 2.6 ± 1.0; 24 h, 2.2 ± 1.0; kidney: 4 h, 8.9 ± 1.8; 24 h, 7.0 ± 2.0; and spleen; 4 h, 11.3 ± 4.5; 24 h, 11.5 ± 7.6. Higher SUVmax was significantly associated with higher visual scores on dual-phase SPECT (4 h, p < 0.001; 24 h, p < 0.001) (4 h: scores 3 and 4, p < 0.05; scores 3 and 5: p < 0.01; scores 4 and 5: p < 0.01; 24 h: scores 3 and 4, p = 0.0748; scores 3 and 5: p < 0.01; scores 4 and 5: p < 0.01). CONCLUSION: We adapted the SUV to SRS and established the range of SUV for physiological uptake in the liver, kidney, and spleen. Combined visual and quantitative assessment is useful for imaging individual lesions in greater detail, and may serve as a new tumor marker of SRS for staging and restaging of NETs.

Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis.

Objectives: In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects. Methods: Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T1-weghted (T1w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T1w of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter. Results: The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, R2 = 0.93; FA-RK, R2 = 0.89) and a strong negative correlation (FA-AK, R2 = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA. Conclusions: WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures. Advances in knowledge: Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.

Nonconvulsive status epilepticus in patients with acute subarachnoid hemorrhage is associated with negative arterial spin labeling on peri-ictal magnetic resonance images.

Purpose: Non-convulsive status epilepticus (NCSE) is characterized by repetitive or continuous seizures without convulsions. Arterial spin labeling (ASL) is useful for assessing hyperperfusion due to neurovascular unit coupling in patients with NCSE; subarachnoid hemorrhage (SAH) impairs the neurovascular unit. We hypothesized that the sensitivity of ASL in detecting NCSE is low in patients with SAH during the acute phase. Methods: Based on ASL findings obtained within 48 h after the clinical suspicion of focal-onset NCSE, we divided 34 patients into ASL-negative (no hyperperfusion; n = 10) and ASL-positive (confirmed hyperperfusion; n = 24) groups. We further divided the two groups according to the NCSE etiology: patients who were diagnosed with NCSE within 14 days after SAH onset (acute SAH, n = 11) and patients with NCSE due to factors other acute SAH (n = 23) and compared their characteristics. Results: In 10 of the 34 patients (29.4 %) the ASL findings were normal. The rate of acute SAH was significantly higher in ASL-negative- (n = 8, 80.0 %) than ASL-positive patients (n = 3, 12.5 %). The rate of patients in aphasic status was significantly lower in ASL-negative patients (n = 1, 10 %) than in ASL-positive patients (n = 12, 50.0 %). Conclusion: Normal ASL findings alone should not be used to exclude a diagnosis of NCSE particularly in patients in the acute phase of SAH with deterioration or no improvement in consciousness.

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atrophy were not considered by the machine-learning technique. PURPOSE: To develop a machine-learning system that can capture morphology features for determination of atrophy of brain tissue in early-stage AD and classification of healthy participants or patients. MATERIAL AND METHODS: Three-dimensional T1-weighted (3D-T1W) data were obtained from AD Neuroimaging Initiative (200 healthy controls and 200 patients with early-stage AD). Automated segmentation of 3D-T1W data was performed. Deep learning (DL) and support vector machine (SVM) were trained using 66-segmented volume values as input and AD diagnosis as output. DL was performed using 66 volume values or gray matter (GM) and white matter (WM) volume values. SVM learning was performed using 66 volume values and six regions with high variable importance. 3D convolutional neural network (3D-CNN) was trained using the segmented images. Accuracy and area under curve (AUC) were obtained. Variable importance was evaluated from logistic regression analysis. RESULTS: DL for GM and WM volume values, accuracy 0.6; SVM for all volume values, accuracy 0.82 and AUC 0.81; DL for all volume values, accuracy 0.82 and AUC 0.8; 3D-CNN using segmental images of the whole brain, accuracy 0.5 and AUC 0.51. SVM using volume values of six regions, accuracy 0.82; image-based 3D-CNN, highest accuracy 0.69. CONCLUSION: Our results show that atrophic features are more considerable than morphological features in the early detection of AD.

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma.

No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson's correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86-1.00]) as well as in the validation cohort (0.92 [95%CI 0.71-0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.