Associations between femoral 3D curvature and sagittal imbalance of spine.

Background: The sagittal imbalance (SI) of spine triggers compensatory mechanisms (CMs) of lower extremity (LE) to restore trunk balance. These CMs can cause long-period stress on the femur and may possibly alter the femoral morphology. This cross-sectional observational study aimed to answer the following questions: (a) Do SI subjects exhibit greater femoral bowing compared to subjects with sagittal balance? (b) Are there associations between femoral bowing and CMs of LE in SI subjects? Methods: Subjects who underwent biplanar full body radiographs with the EOS imaging system between January 2016 and September 2021 were recruited. Sagittal parameters included T1-pelvic angle (TPA), pelvic incidence (PI), pelvic tilt (PT), sacral slope, lumbar lordosis (LL), PI-LL, and PT/PI ratio. LE parameters were femoral obliquity angle (FOA), knee flexion angle (KA), and ankle dorsiflexion angle. Femoral bowing was quantified as 3D radius of femoral curvature (RFC). Associations between 3D RFC and the radiographic parameters were analyzed. Results: A total of 105 subjects were included, classified into balance group (TPA < 14°, n = 40), SI group (TPA ≥ 14° and KA <5°, n = 30), and SI with knee flexion group (TPA ≥ 14° and KA ≥ 5°, n = 35). 3D RFC was significantly lower in SI with knee flexion group compared to the other two groups (both p < 0.001). Stepwise linear regression showed that age, SI and knee flexion, femoral length (FL), FOA, and KA were independent predictors for 3D RFC. Conclusion: Greater femoral bowing is observed in subjects with SI and knee flexion compared to the balanced population. CM parameters, including KA and FOA, are associated with 3D RFC. Further longitudinal study is needed to investigate the cause-and-effect relationship between SI, CMs of LE, and femoral bowing.

Evidence of neuroinflammation in fibromyalgia syndrome: a [ 18 F]DPA-714 positron emission tomography study.

ABSTRACT: This observational study aimed to determine whether individuals with fibromyalgia (FM) exhibit higher levels of neuroinflammation than healthy controls (HCs), as measured with positron emission tomography using [ 18 F]DPA-714, a second-generation radioligand for the translocator protein (TSPO). Fifteen women with FM and 10 HCs underwent neuroimaging. Distribution volume (V T ) was calculated for in 28 regions of interest (ROIs) using Logan graphical analysis and compared between groups using multiple linear regressions. Group (FM vs HC) was the main predictor of interest and TSPO binding status (high- vs mixed-affinity) was added as a covariate. The FM group had higher V T in the right postcentral gyrus ( b = 0.477, P = 0.033), right occipital gray matter (GM; b = 0.438, P = 0.039), and the right temporal GM ( b = 0.466, P = 0.042). The FM group also had lower V T than HCs in the left isthmus of the cingulate gyrus ( b = -0.553, P = 0.014). In the subgroup of high-affinity binders, the FM group had higher V T in the bilateral precuneus, postcentral gyrus, parietal GM, occipital GM, and supramarginal gyrus. Group differences in the right parietal GM were associated with decreased quality of life, higher pain severity and interference, and cognitive problems. In support of our hypothesis, we found increased radioligand binding (V T ) in the FM group compared with HCs in several brain regions regardless of participants' TSPO binding status. The ROIs overlapped with prior reports of increased TSPO binding in FM. Overall, increasing evidence supports the hypothesis that FM involves microglia-mediated neuroinflammation in the brain.

Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models.

Background: Class I echocardiographic guidelines in primary mitral regurgitation (PMR) risks left ventricular ejection fraction (LVEF) < 50% after mitral valve surgery even with pre-surgical LVEF > 60%. There are no models predicting LVEF < 50% after surgery in the complex interplay of increased preload and facilitated ejection in PMR using cardiac magnetic resonance (CMR). Objective: Use regression and machine learning models to identify a combination of CMR LV remodeling and function parameters that predict LVEF < 50% after mitral valve surgery. Methods: CMR with tissue tagging was performed in 51 pre-surgery PMR patients (median CMR LVEF 64%), 49 asymptomatic (median CMR LVEF 63%), and age-matched controls (median CMR LVEF 64%). To predict post-surgery LVEF < 50%, least absolute shrinkage and selection operator (LASSO), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed and validated in pre-surgery PMR patients. Recursive feature elimination and LASSO reduced the number of features and model complexity. Data was split and tested 100 times and models were evaluated via stratified cross validation to avoid overfitting. The final RF model was tested in asymptomatic PMR patients to predict post-surgical LVEF < 50% if they had gone to mitral valve surgery. Results: Thirteen pre-surgery PMR had LVEF < 50% after mitral valve surgery. In addition to LVEF (P = 0.005) and LVESD (P = 0.13), LV sphericity index (P = 0.047) and LV mid systolic circumferential strain rate (P = 0.024) were predictors of post-surgery LVEF < 50%. Using these four parameters, logistic regression achieved 77.92% classification accuracy while RF improved the accuracy to 86.17%. This final RF model was applied to asymptomatic PMR and predicted 14 (28.57%) out of 49 would have post-surgery LVEF < 50% if they had mitral valve surgery. Conclusions: These preliminary findings call for a longitudinal study to determine whether LV sphericity index and circumferential strain rate, or other combination of parameters, accurately predict post-surgical LVEF in PMR.

Prediction of MCI-to-AD progression with atrophy-weighted standard uptake value ratios of 18 F-Florbetapir PET.

Accurate prediction of MCI-to-AD progression is an important yet challenging task. We introduce a new quantitative parameter: the atrophy-weighted standard uptake value ratio (awSUVR), defined as the PET SUVR divided by the hippocampal volume measured with MR, and evaluate whether it may provide better prediction of the MCI-to-AD progression. MATERIALS AND METHODS: We used ADNI data to evaluate the prediction performances of the awSUVR against SUVR. 571, 363 and 252 18-F-Florbetaipir scans were selected based on criteria of conversion at the third, fifth and seventh year after the PET scans, respectively. Corresponding MR scans were segmented with Freesurfer and applied on PET for SUVR and awSUVR computation. We also searched for the optimal combination of target and reference regions. In addition to evaluating the overall prediction performances, we also evaluated the prediction for APOE4 carriers and non-carriers. For the scans with false predictions, we used 18-F-Flortaucipir scans to investigate the potential source of error. RESULTS: awSUVR provides more accurate prediction than the SUVR in all three progression criteria. The 5-year prediction accuracy/sensitivity/specificity is 90/81/93% for awSUVR and 86/81/88% for SUV. awSUVR also yields good 3- and 7-year prediction accuracy/sensitivity/specificity of 91/57/96 and 92/89/93, respectively. APOE4 carriers generally are slightly more difficult to predict for the progression. False negative prediction is found to either due to a near-cutoff mis-classification or potentially non-AD dementia pathology. False positive prediction is mainly due to the slightly delayed progression than the expected progression time. CONCLUSION: We demonstrated with ADNI data that 18-F-Florbetapir SUVR weighted with hippocampus volume may provide good prediction power with over 90% accuracy in MCI-to-AD progression.

Brain and Systemic Inflammation in De Novo Parkinson's Disease.

OBJECTIVE: To assess the presence of brain and systemic inflammation in subjects newly diagnosed with Parkinson's disease (PD). BACKGROUND: Evidence for a pathophysiologic role of inflammation in PD is growing. However, several key gaps remain as to the role of inflammation in PD, including the extent of immune activation at early stages, potential effects of PD treatments on inflammation and whether pro-inflammatory signals are associated with clinical features and/or predict more rapid progression. METHODS: We enrolled subjects with de novo PD (n = 58) and age-matched controls (n = 62). Subjects underwent clinical assessments, including the Movement Disorder Society-United Parkinson's Disease rating scale (MDS-UPDRS). Comprehensive cognitive assessment meeting MDS Level II criteria for mild cognitive impairment testing was performed. Blood was obtained for flow cytometry and cytokine/chemokine analyses. Subjects underwent imaging with 18 F-DPA-714, a translocator protein 18kd ligand, and lumbar puncture if eligible and consented. RESULTS: Baseline demographics and medical history were comparable between groups. PD subjects showed significant differences in University of Pennsylvania Smell Identification Test, Schwab and England Activities of Daily Living, Scales for Outcomes in PD autonomic dysfunction, and MDS-UPDRS scores. Cognitive testing demonstrated significant differences in cognitive composite, executive function, and visuospatial domain scores at baseline. Positron emission tomography imaging showed increased 18 F-DPA-714 signal in PD subjects. 18 F-DPA-714 signal correlated with several cognitive measures and some chemokines. CONCLUSIONS: 18 F-DPA-714 imaging demonstrated increased central inflammation in de novo PD subjects compared to controls. Longitudinal follow-up will be important to determine whether the presence of inflammation predicts cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A universal programmable Gaussian boson sampler for drug discovery.

Gaussian boson sampling (GBS) has the potential to solve complex graph problems, such as clique finding, which is relevant to drug discovery tasks. However, realizing the full benefits of quantum enhancements requires large-scale quantum hardware with universal programmability. Here we have developed a time-bin-encoded GBS photonic quantum processor that is universal, programmable and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Leveraging our processor, we successfully executed clique finding on a 32-node graph, achieving approximately twice the success probability compared to classical sampling. As proof of concept, we implemented a versatile quantum drug discovery platform using this GBS processor, enabling molecular docking and RNA-folding prediction tasks. Our work achieves GBS circuitry with its universal and programmable architecture, advancing GBS toward use in real-world applications.

Generative Adversarial Network (GAN) for Automatic Reconstruction of the 3D Spine Structure by Using Simulated Bi-Planar X-ray Images.

In this study, we modified the previously proposed X2CT-GAN to build a 2Dto3D-GAN of the spine. This study also incorporated the radiologist's perspective in the adjustment of input signals to prove the feasibility of the automatic production of three-dimensional (3D) structures of the spine from simulated bi-planar two-dimensional (2D) X-ray images. Data from 1012 computed tomography (CT) studies of 984 patients were retrospectively collected. We tested this model under different dataset sizes (333, 666, and 1012) with different bone signal conditions to observe the training performance. A 10-fold cross-validation and five metrics-Dice similarity coefficient (DSC) value, Jaccard similarity coefficient (JSC), overlap volume (OV), and structural similarity index (SSIM)-were applied for model evaluation. The optimal mean values for DSC, JSC, OV, SSIM_anteroposterior (AP), and SSIM_Lateral (Lat) were 0.8192, 0.6984, 0.8624, 0.9261, and 0.9242, respectively. There was a significant improvement in the training performance under empirically enhanced bone signal conditions and with increasing training dataset sizes. These results demonstrate the potential of the clinical implantation of GAN for automatic production of 3D spine images from 2D images. This prototype model can serve as a foundation in future studies applying transfer learning for the development of advanced medical diagnostic techniques.

Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method.

There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors.

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast-enhanced MRI (DCE-MRI)-derived perfusion parameters. The clinical data and preoperative DCE-MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning-based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.

Dynamic Amyloid PET: Relationships to 18F-Flortaucipir Tau PET Measures.

Measuring amyloid and predicting tau status using a single amyloid PET study would be valuable for assessing brain AD pathophysiology. We hypothesized that early-frame amyloid PET (efAP) correlates with the presence of tau pathology because the initial regional brain concentrations of radioactivity are determined primarily by blood flow, which is expected to be decreased in the setting of tau pathology. Methods: The study included 120 participants (63 amyloid-positive and 57 amyloid-negative) with dynamic 18F-florbetapir PET and static 18F-flortaucipir PET scans obtained within 6 mo of each other. These subjects were predominantly cognitively intact in both the amyloid-positive (63%) and the amyloid-negative (93%) groups. Parameters for efAP quantification were optimized for stratification of tau PET positivity, assessed by either a tauopathy score or Braak regions. The ability of efAP to stratify tau positivity was measured using receiver-operating-characteristic analysis of area under the curve (AUC). Pearson r and Spearman ρ were used for parametric and nonparametric comparisons between efAP and tau PET, respectively. Standardized net benefit was used to evaluate improvement in using efAP as an additional copredictor over hippocampal volume in predicting tau PET positivity. Results: Measuring efAP within the hippocampus and summing the first 3 min of brain activity after injection showed the strongest discriminative ability to stratify for tau positivity (AUC, 0.67-0.89 across tau PET Braak regions) in amyloid-positive individuals. Hippocampal efAP correlated significantly with a global tau PET tauopathy score in amyloid-positive participants (r = -0.57, P < 0.0001). Compared with hippocampal volume, hippocampal efAP showed a stronger association with tau PET Braak stage (ρ = -0.58 vs. -0.37) and superior stratification of tau PET tauopathy score (AUC, 0.86 vs. 0.66; P = 0.002). Conclusion: Hippocampal efAP can provide additional information to conventional amyloid PET, including estimation of the likelihood of tau positivity in amyloid-positive individuals.

Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets.

The aim of the study was to use a previously proposed mask region-based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation.

Development and validation of a prognostic model incorporating [18F]FDG PET/CT radiomics for patients with minor salivary gland carcinoma.

OBJECTIVES: The aim of this study was to develop and validate a prognostic model incorporating [18F]FDG PET/CT radiomics for patients of minor salivary gland carcinoma (MSGC). METHODS: We retrospectively reviewed the pretreatment [18F]FDG PET/CT images of 75 MSGC patients treated with curative intent. Using a 1.5:1 ratio, the patients were randomly divided into a training and validation group. The main outcome measurements were overall survival (OS) and relapse-free survival (RFS). All of the patients were followed up for at least 30 months or until death. Following segmentation of tumors and lymph nodes on PET images, radiomic features were extracted. The prognostic significance of PET radiomics and clinical parameters in the training group was examined using receiver operating characteristic curve analysis. Variables showing a significant impact on OS and RFS were entered into multivariable Cox regression models. Recursive partitioning analysis was subsequently implemented to devise a prognostic index, whose performance was examined in the validation group. Finally, the performance of the index was compared with clinical variables in the entire cohort and nomograms for surgically treated cases. RESULTS: The training and validation groups consisted of 45 and 30 patients, respectively. The median follow-up time in the entire cohort was 59.5 months. Eighteen relapse, 19 dead, and thirteen relapse, eight dead events were found in the training and validation cohorts, respectively. In the training group, two factors were identified as independently associated with poor OS, i.e., (1) tumors with both high maximum standardized uptake value (SUVmax) and discretized intensity entropy and (2) poor performance status or N2c-N3 stage. A prognostic model based on the above factors was devised and showed significant higher concordance index (C-index) for OS than those of AJCC stage and high-risk histology (C-index: 0.83 vs. 0.65, P = 0.005; 0.83 vs. 0.54, P < 0.001, respectively). This index also demonstrated superior performance than nomogram for OS (C-index: 0.88 vs. 0.70, P = 0.017) and that for RFS (C-index: 0.87 vs. 0.72, P = 0.004). CONCLUSIONS: We devised a novel prognostic model that incorporates [18F]FDG PET/CT radiomics and may help refine outcome prediction in patients with MSGC.

Prognostic Value of Tumor Heterogeneity and SUVmax of Pretreatment 18F-FDG PET/CT for Salivary Gland Carcinoma With High-Risk Histology.

PURPOSE: Previous studies have shown that SUVmax on F-FDG PET/CT predicts prognosis in patients with salivary gland carcinoma (SGC). Here, we sought to evaluate whether texture features extracted from F-FDG PET/CT images may provide additional prognostic information for SGC with high-risk histology. METHODS: We retrospectively examined pretreatment F-FDG PET/CT images obtained from 85 patients with nonmetastatic SGC showing high-risk histology. All patients were treated with curative intent. We used the fixed threshold of 40% of SUVmax for tumor delineation. PET texture features were extracted by using histogram analysis, normalized gray-level co-occurrence matrix, and gray-level size zone matrix. Optimal cutoff points for each PET parameter were derived from receiver operating characteristic curve analyses. Recursive partitioning analysis was used to construct a prognostic model for overall survival (OS). RESULTS: Receiver operating characteristic curve analyses revealed that SUVmax, SUV entropy, uniformity, entropy, zone-size nonuniformity, and high-intensity zone emphasis were significantly associated with OS. The strongest associations with OS were found for high SUVmax (>6.67) and high SUV entropy (>2.50). Multivariable Cox analysis identified high SUVmax, high SUV entropy, performance status, and N2c-N3 stage as independent predictors of survival. A prognostic model derived from multivariable analysis revealed that patients with high SUVmax and SUV entropy or with the presence of poor performance status or N2c-N3 were associated with worse OS. CONCLUSIONS: A prognostic model that includes SUVmax and SUV entropy is useful for risk stratification and supports the additional benefit of texture analysis for SGC with high-risk histology.

Magnetic Resonance Elastography in the Assessment of Acute Effects of Kinesio Taping on Lumbar Paraspinal Muscles.

BACKGROUND: Kinesio tape (KT) is an elastic therapeutic tape used for treating sports-related injuries and a number of other disorders. To date, the objective evidence to link pathophysiological effects and actual reactions triggered by KT is limited. PURPOSE: To explore the effect of KT on the lumbar paraspinal muscles by magnetic resonance (MR) elastography. STUDY TYPE: Prospective observational study. POPULATION: Sixty-six asymptomatic volunteers with 31 women and 35 men. FIELD STRENGTH/SEQUENCE: 3.0T MRI and elastography with vibration frequency of 120 Hz. ASSESSMENT: The 5-cm-width KT with full tension was placed on a single side of the lumbar paraspinal muscle. The taping side and adhering direction were randomly decided. Two rectangular regions of interest (ROIs) of 5- and 2.5-cm-width were positioned at the bilateral paraspinal regions from the L2 to L4 level on the confidence map of MR elastography before and after KT taping. The mean shear stiffness values of the ROIs at the superficial, middle, and deep depths were recorded; then the differences between the taping and reference sides were calculated. STATISTICAL TESTS: Paired t-test and Pearson correlations were used to evaluate the stiffness changes after KT application and intraoperator errors of the stiffness measures on the reference side, respectively. RESULTS: A significant decrease in the muscle stiffness value between taping and reference sides (-0.71 kPa ± 0.60 with KT and -0.25 kPa ± 0.78 without KT, P < 0.0001 for 5-cm ROI; -0.67 kPa ± 1.12 with KT and -0.16 kPa ± 1.17 without KT, P = 0.0004 for 2.5-cm ROI) was found in the superficial depth, but no significant differences in the middle and deep depths (P = 0.25 and P = 0.79 for 5-cm ROI; P = 0.09 and P = 0.67 for 2.5-cm ROI, respectively). There were no significant differences of muscle stiffness differences between gender (P = 0.11 for superficial, P = 0.37 for middle, P = 0.78 for deep) and taping direction (P = 0.18 for superficial, P = 0.13 for middle, P = 0.15 for deep). DATA CONCLUSION: Our results demonstrate that KT can reduce the MR elastography-derived shear stiffness in the superficial depth of paraspinal muscles. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1039-1045.

Presurgical Identification of Uterine Smooth Muscle Malignancies through the Characteristic FDG Uptake Pattern on PET Scans.

The unidentified presence of uterine smooth muscle malignancies poses a tremendous risk in women planning surgery for presumed benign leiomyomas. We sought to investigate whether preoperative FDG PET may be useful to identify leiomyosarcomas (LMS) and smooth muscle tumors of uncertain malignant potential (STUMP). Methods. We investigated patients with rapidly growing uterine masses which were suspected of being malignant on ultrasound or MRI. Among the 21 patients who underwent FDG PET, we identified 7 LMS, 1 STUMP, and 13 leiomyomas. PET-derived parameters and FDG uptake patterns were analyzed retrospectively. Results. The SUVmax values of LMS/STUMP (range: 3.7-11.8) were significantly higher than those observed in leiomyomas (range: 2.0-9.4; P=0.003) despite a significant overlap. The metabolic tumor/necrosis ratio was significantly higher in LMS/STUMP than in leiomyomas (P < 0.001), with no significant intergroup overlaps. All LMS/STUMP revealed a characteristic pattern of FDG uptake, identifying a specific "hollow ball" sign (corresponding to areas of coagulative tumor necrosis). In contrast, this sign was invariably absent in patients with leiomyomas. Conclusion. The characteristic FDG uptake pattern instead of SUV on PET images allows identifying LMS/STUMP in patients with rapidly growing uterine masses, avoiding the deleterious consequences of regular surgery for presumed benign leiomyomas.

Quantitative positron emission tomography-guided magnetic resonance imaging postprocessing in magnetic resonance imaging-negative epilepsies.

OBJECTIVE: Detection of focal cortical dysplasia (FCD) is of paramount importance in epilepsy presurgical evaluation. Our study aims at utilizing quantitative positron emission tomography (QPET) analysis to complement magnetic resonance imaging (MRI) postprocessing by a morphometric analysis program (MAP) to facilitate automated identification of subtle FCD. METHODS: We retrospectively included a consecutive cohort of surgical patients who had a negative preoperative MRI by radiology report. MAP was performed on T1-weighted volumetric sequence and QPET was performed on PET/computed tomographic data, both with comparison to scanner-specific normal databases. Concordance between MAP and QPET was assessed at a lobar level, and the significance of concordant QPET-MAP+ abnormalities was confirmed by postresective seizure outcome and histopathology. QPET thresholds of standard deviations (SDs) of -1, -2, -3, and -4 were evaluated to identify the optimal threshold for QPET-MAP analysis. RESULTS: A total of 104 patients were included. When QPET thresholds of SD = -1, -2, and -3 were used, complete resection of the QPET-MAP+ region was significantly associated with seizure-free outcome when compared with the partial resection group (P = 0.023, P < 0.001, P = 0.006) or the no resection group (P = 0.002, P < 0.001, P = 0.001). The SD threshold of -2 showed the best combination of positive rate (55%), sensitivity (0.68), specificity (0.88), positive predictive value (0.88), and negative predictive value (0.69). Surgical pathology of the resected QPET-MAP+ areas revealed mainly FCD type I. Multiple QPET-MAP+ regions were present in 12% of the patients at SD = -2. SIGNIFICANCE: Our study demonstrates a practical and effective approach to combine quantitative analyses of functional (QPET) and structural (MAP) imaging data to improve identification of subtle epileptic abnormalities. This approach can be readily adopted by epilepsy centers to improve postresective seizure outcomes for patients without apparent lesions on MRI.

Heterogeneity and irregularity of pretreatment 18F-fluorodeoxyglucose positron emission tomography improved prognostic stratification of p16-negative high-risk squamous cell carcinoma of the oropharynx.

OBJECTIVES: Human papillomavirus-negative oropharyngeal squamous cell carcinoma (OPSCC) has unfavorable survival outcomes. Two outcomes have been identified based on smoking history and tumor stage. We investigate the prognostic role of pre-treatment positron emission tomography (PET) in high-risk OPSCC. MATERIALS AND METHODS: We enrolled 147 M0 OPSCC patients with p16-negative staining and a history of heavy smoking (>10 pack-years) or T4 disease. All patients completed primary chemoradiotherapy, and 42% maximum standard uptake values (SUVmax) were used as the threshold for primary tumor. Patients were classified into training and validation cohorts with a ratio of 1:1.5 according to the PET date. Heterogeneity and irregularity indices were obtained. PET parameters with significant impact on progression-free survival (PFS) in receiver operating characteristic curves and univariate Cox models were identified and included in recursive partitioning analysis (RPA) for constructing a prognostic model. The RPA-based prognostic model was further tested in the validation cohort using multivariate Cox models. RESULTS: Fifty-eight and 89 patients were in the training and validation groups, respectively. Heterogeneity parameter, SUV-entropy (derived from histogram analysis), and irregularity index, and asphericity were significantly associated with PFS. The RPA model revealed that patients with both high SUV-entropy and high asphericity experienced the worst PFS. Results were confirmed in the validation group. The overall concordance index for PFS of the model was 0.75, which was higher than the clinical stages, performance status, SUVmax, and metabolic tumor volume of PET. CONCLUSIONS: PET prognostic model provided useful prediction of PFS for patients with high-risk OPSCC.

Corrigendum to "Effect of Glycyrrhiza on the Diuretic Function of Euphorbia kansui: An Ascites Mouse Model".

[This corrects the article DOI: 10.1155/2016/7620817.].

Detecting Triple-Vessel Disease with Cadmium Zinc Telluride-Based Single-Photon Emission Computed Tomography Using the Intensity Signal-to-Noise Ratio between Rest and Stress Studies.

The purpose of this study was to investigate if a novel parameter, the stress-to-rest ratio of the signal-to-noise ratio (RSNR) obtained with a cadmium zinc telluride (CZT) SPECT scanner, could be used to distinguish triple-vessel disease (TVD) patients. Methods. One hundred and two patients with suspected coronary artery disease were retrospectively involved. Each subject underwent a Tl-201 SPECT scan and subsequent coronary angiography. Subjects were separated into TVD (n = 41) and control (n = 61) groups based on coronary angiography results using 50% as the stenosis cutoff. The RSNR was calculated by dividing the stress signal-to-noise ratio (SNR) by the rest SNR. Summed scores were calculated using quantitative perfusion SPECT (QPS) for all subjects. Results. The RSNR in the TVD group was found to be significantly lower than that in the control group (0.83 ± 0.15 and 1.06 ± 0.17, resp.; P < 0.01). Receiver-operating characteristic (ROC) analysis showed that RSNR can detect TVD more accurately than the summed difference score with higher sensitivity (85% versus 68%), higher specificity (90% versus 72%), and higher accuracy (88% versus 71%). Conclusion. The RSNR may serve as a useful index to assist the diagnosis of TVD when a fully automatic quantification method is used in CZT-based SPECT studies.

Single-scan rest/stress imaging with 99mTc-Sestamibi and cadmium zinc telluride-based SPECT for hyperemic flow quantification: A feasibility study evaluated with cardiac magnetic resonance imaging.

INTRODUCTION: We aimed to evaluate whether the hyperemic myocardial blood flow (MBF) can be estimated using cadmium zinc telluride (CZT)-based single-photon emission computed tomography (SPECT) cameras with a single, rapid rest/stress dynamic scan. Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (MRI) was used as a reference modality for flow measurement. MATERIALS AND METHODS: The proposed protocol included both the rest and stress acquisitions within a 24-min scan. Patients were first injected with 99mTc-Sestamibi at the resting state. Sixty minutes after the first injection, the subject was positioned via scintigraphy, after which the list-mode data acquisition was initiated and continued for 24 minutes. Five minutes after data acquisition was initiated, a stressed state was induced via dipyridamole infusion, after which a second dose of 99mTc-Sestamibi was injected. Dynamic SPECT images were reconstructed for all subjects, who also underwent T1-weighted cardiac DCE-MRI performed on days other than those of the SPECT studies. MBF values were estimated for the rest and stress MRI studies, and for the stress portion of the SPECT study. The SPECT-measured hyperemic MBF was compared with the MR-measured hyperemic MBF and coronary flow reserve (CFR), based on the regions of interest. RESULTS: A total of 30 subjects were included in this study. The hyperemic MBF estimated from SPECT showed a strong correlation with the MR-measured hyperemic MBF (r2 = 0.76) and a modest correlation with the MR-measured CFR (r2 = 0.56). Using MR-measured CFR <1.3 as a cutoff for coronary stenosis, we found that the SPECT-measured hyperemic MBF served as a useful clinical index with 94% sensitivity, 90% specificity, and 93% accuracy. CONCLUSIONS: Hyperemic MBF can be measured with a rapid, single-scan rest/stress study with CZT-based SPECT cameras.