Mean Regions Of Interest Research Articles

Intraoperative in vivo confocal endomicroscopy of the glioma margin: performance assessment of image interpretation by neurosurgeon users.

Confocal laser endomicroscopy (CLE) is an intraoperative real-time cellular resolution imaging technology that images brain tumor histoarchitecture. Previously, we demonstrated that CLE images may be interpreted by neuropathologists to determine the presence of tumor infiltration at glioma margins. In this study, we assessed neurosurgeons' ability to interpret CLE images from glioma margins and compared their assessments to those of neuropathologists. In vivo CLE images acquired at the glioma margins that were previously reviewed by CLE-experienced neuropathologists were interpreted by four CLE-experienced neurosurgeons. A numerical scoring system from 0 to 5 and a dichotomous scoring system based on pathological features were used. Scores from assessments of hematoxylin and eosin (H&E)-stained sections and CLE images by neuropathologists from a previous study were used for comparison. Neurosurgeons' scores were compared to the H&E findings. The inter-rater agreement and diagnostic performance based on neurosurgeons' scores were calculated. The concordance between dichotomous and numerical scores was determined. In all, 4275 images from 56 glioma margin regions of interest (ROIs) were included in the analysis. With the numerical scoring system, the inter-rater agreement for neurosurgeons interpreting CLE images was moderate for all ROIs (mean agreement, 61%), which was significantly better than the inter-rater agreement for the neuropathologists (mean agreement, 48%) (p < 0.01). The inter-rater agreement for neurosurgeons using the dichotomous scoring system was 83%. The concordance between the numerical and dichotomous scoring systems was 93%. The overall sensitivity, specificity, positive predictive value, and negative predictive value were 78%, 32%, 62%, and 50%, respectively, using the numerical scoring system and 80%, 27%, 61%, and 48%, respectively, using the dichotomous scoring system. No statistically significant differences in diagnostic performance were found between the neurosurgeons and neuropathologists. Neurosurgeons' performance in interpreting CLE images was comparable to that of neuropathologists. These results suggest that CLE could be used as an intraoperative guidance tool with neurosurgeons interpreting the images with or without assistance of the neuropathologists. The dichotomous scoring system is robust yet simple and may streamline rapid, simultaneous interpretation of CLE images during imaging.

Abstract 11433: Elevated Vascular Endothelial Growth Factor A is Associated With Disruption of Default Network Connectivity

Introduction: Vascular Endothelial Growth Factor A (VEGF-A) is an angiogenic signaling protein involved in the maintenance of the cerebral vasculature. VEGF-A may play a role in the pathogenesis of Alzheimer’s disease and vascular dementia; however, whether elevated VEGF-A levels indicate a pathophysiolocal response or protective mechanism remains unclear. No prior study has explored whether VEGF-A levels may be associated with changes in functional connectivity among brain regions prior to the development of cognitive decline. Hypothesis: Disruption of the default network often precedes the development of cognitive changes. In a sample of older adults, we hypothesized that VEGF-A levels may be associated with default network connectivity, indicating early involvement of VEGF-A in disease processes. Methods: Forty-six independently living older adults (mean age = 72.0 years; SD = 6.4; 34.8% male) free of dementia or clinical stroke underwent venipuncture and brain MRI. Plasma was assayed for VEGF-A. Using resting state functional MRI, blood-oxygen level dependent (BOLD) time series from all voxels in each region of interest (ROI) was averaged to determine mean ROI activity. Medial prefrontal cortex, lateral parietal cortex and precuneus cortex ROIs comprised the default network. Results: Accounting for age and sex, multiple linear regression analysis revealed a significant negative association between VEGF-A levels and default mode network connectivity (B =-.0007, 95% CI (-.0012, -.0002), p = .0096). This association persisted after adjusting for APOE4 carrier status and Framingham score. A significant association was present in both APOE4 carriers (p = .019) and non-carriers (p = .029). Conclusions: Prior studies evaluating the association between VEGF-A levels and cognition have been conflicted. This study is the first to examine whether VEGF-A may be linked to early changes in brain network connectivity which precede cognitive decline. These findings suggest that VEGF-A may be elevated early in the progression of neurocognitive disorders. Whether higher levels of VEGF-A contribute to the pathogenesis of neurocognitive disorders or play a protective role in persevering cognitive function warrants further investigation.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis.

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Optimization of Image Capture Distance for Facial Thermograms in Dentistry

Thermography has not yet been validated for the screening of oral disorders and no clear guidelines or methodology for this purpose have been defined in the literature. The current pilot study was aimed as an initial step to evaluate the influence of shooting distances on the detection of the temperature asymmetries on the face for the purpose of dental thermography. Facial thermograms were taken in three views at three shooting distances for each participant. The mean temperature of the overall image and at the region of interest (ROI) were measured from the analysis software. Thirty adult volunteers participated in this study [n = 30, mean age: 25.8 ± 6.0]. The differences between the mean temperature values at the ROI for all different shooting distances within each profile view were statistically significant [Front: H(2) = 80.176, p &lt; 0.001; Left: H(2) = 21.399, p &lt; 0.001; Right: H(2) = 49.451, p &lt; 0.001]. The mean ROI temperature was influenced by personal effects (p &lt; 0.001), medications (p &lt; 0.001), undergoing dental treatments (p &lt; 0.001) as well as the consumption of food (p &lt; 0.001). This study concludes that the detection of temperature asymmetries on the face in adult volunteers are affected by the capture distance as well as factors such as medication, oral-care, and alimentation. Therefore, it is advised that personal as well as medical histories are obtained in detail prior to making the facial thermograms.

Diffusion-Weighted MRI in the Evaluation of Early-Stage Breast Cancer Treated with a Short Preoperative Radiotherapy: Preliminary Results.

To assess tumor response with diffusion-weighted MRI (DW-MRI) after a short preoperative radiotherapy in early-stage breast cancer (BCa). This was a prospective, single-center pilot study. 3T-MRI were performed before and after radiotherapy. The longest diameter (LD) and the apparent diffusion coefficient (ADC) value of a region of interest (ROI) of the tumors were recorded. Histopathology and immunohistochemistry, including the Ki-67 index of the core biopsy and of the surgical specimen, were the reference standards. Nineteen patients with 22 early-stage BCa were included. The mean ROI ADC value was 1.093 ± 0.278 × 10-3 mm2/s before radiotherapy and 1.490 ± 0.429 × 10-3 mm2/s (p-value < 0.001) after radiotherapy. The Ki-67 index was 9.2 ± 9.1% at the percutaneous biopsy before radiotherapy and 4.9 ± 7.5% (p-value = 0.005) after radiotherapy at the surgical specimen. After neoadjuvant radiotherapy, a 4.7% decrease in LD and a 36.3% increase in ROI-ADC of the tumors were measured at MRI and a 46.7% decrease in Ki-67 index was observed at histology of the surgical specimen in comparison with the percutaneous core biopsy. In early-stage BCa, a significant increase in ROI-ADC at DWI and a significant decrease in Ki-67 index were observed after a short preoperative radiotherapy, suggesting early tumor response.

Open-source pipeline for automatic segmentation and microstructural analysis of murine knee subchondral bone.

A segmented dataset of μCT scans from 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice was used to train a U-Net type architecture to automate segmentation of the subchondral trabecular bone. These segmentations were used in tandem with the original scans as input for microstructural analysis along with thresholded trabecular bone. Manually and U-Net segmented ROIs were fed into two available pipelines for microstructural analysis: the ITKBoneMorphometry library and CTan (SKYSCAN). Outcome parameters were compared between pipelines, including: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV). There was good agreement for all bone measures comparing the manual and U-Net pipelines utilizing ITK (R = 0.88-0.98) and CTAn (R = 0.91-0.98). ITK and CTAn showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R = 0.9-0.98). However, limited agreement was seen between TbN (R = 0.73) and TbSb (R = 0.59) due to methodological differences in how spacing is evaluated. Microstructural parameters generated from manual and automatic segmentations showed high correlation across all measures. Using the CTAn pipeline yielded strong R2 values (0.83-0.96) and very strong agreement based on ICC (0.90-0.98). The ITK pipeline yielded similarly high R2 values (0.91-0.96, except for TbN (0.77)), and ICC values (0.88-0.98). The automated segmentations yield lower average values for BV, TV and BV/TV (ranging from 14 % to 6.3 %), but differences were not found to be influenced by the mean ROI values. This integrated pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone microstructure. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of microstructural analysis of trabecular bone across different research groups.

Evidence of An Association Between Cerebral Blood Flow and Microstructural Integrity in Normative Aging Using a Holistic MRI Approach.

Cerebral tissue integrity decline and cerebral blood flow (CBF) alteration are major aspects of motor and cognitive dysfunctions and neurodegeneration. However, little is known about the association between blood flow and brain microstructural integrity, especially in normal aging. To assess the association between CBF and cerebral microstructural integrity. Cross sectional. A total of 94 cognitively unimpaired adults (mean age 50.7 years, age range between 22 and 88 years, 56 Men). A 3 T; pseudo-continuous arterial spin labeling (pCASL), diffusion tensor imaging (DTI), Bayesian Monte Carlo analysis of multicomponent driven equilibrium steady-state observation of T1 and T2 (BMC-mcDESPOT). Lobar associations between CBF derived from pCASL, and longitudinal relaxation rate (R1 ), transverse relaxation rate (R2 ) and myelin water fraction (MWF) derived from BMC-mcDESPOT, or radial diffusivity (RD), axial diffusivity (AxD), mean diffusivity (MD) and fractional anisotropy (FA) derived from DTI were assessed. Multiple linear regression models were used using the mean region of interest (ROI) values for MWF, R1 , R2 , FA, MD, RD, or AxD as the dependent variable and CBF, age, age2 , and sex as the independent variables. A two-sided P value of <0.05 defined statistical significance. R1 , R2 , MWF, FA, MD, RD, and AxD parameters were associated with CBF in most of the cerebral regions evaluated. Specifically, higher CBF values were significantly associated with higher FA, MWF, R1 and R2 , or lower MD, RD and AxD values. These findings suggest that cerebral tissue microstructure may be impacted by global brain perfusion, adding further evidence to the intimate relationship between cerebral blood supply and cerebral tissue integrity. 4 TECHNICAL EFFICACY: Stage 4.

An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images.

Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of , , , and , respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.

Augmentation of perfusion with simultaneous vasodilator and inotropic agents in experimental acute middle cerebral artery occlusion: a pilot study

BackgroundThis study tests the hypothesis that simultaneous cerebral blood pressure elevation and potent vasodilation augments perfusion to ischemic tissue in acute ischemic stroke and it varies by degree of pial...

Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.

Artificial Intelligence-Assisted Mapping of Proliferation Centers in Chronic Lymphocytic Leukemia/ Small Lymphocytic Lymphoma Identifies Patterns That Reliably Distinguish Accelerated Phase and Large Cell Transformation

BackgroundChronic lymphocytic leukemia (CLL) involving tissues is characterized by sheets of small lymphocytes and vague nodules of larger cells, known as proliferation centers (PCs). CLL can undergo progression to accelerated CLL (aCLL) or further progress to diffuse large B-cell lymphoma, also known as Richter transformation (RT). Distinguishing CLL with many PCs from aCLL or RT can be challenging, particularly in small needle-biopsy specimens. In this study, we sought to design an artificial intelligence (AI)-based tool to automate and enhance the delineation of PCs and provide an objective approach to CLL/SLL acceleration/transformation.Material & MethodsWe manually annotated 25, 28 and 21 regions of interest (ROIs) encompassing small round PCs and confluent/ expanded PCs of 10 CLL, 12 aCLL, and 8 RT digitized hematoxylin & eosin stained slides, respectively (Figure A1). We analyzed the ROIs with both length and width larger than 2,000 pixels and set the tile length and stride as 1,000 and 100 pixels, respectively (Figure A1), and were able to extract sufficient tiles from each ROI (Figure A2). To recreate PCs, after performing nuclear segmentation via convolutional neural network and quality control, we quantified the nuclear size/intensity of cells occupying each tile (Figure A3). Nuclear size varied from 8 to 108 square micrometers, and nuclear mean intensity varied from 0 to 255. We normalized both the values of nuclear size and mean intensity to 0.0 and 1.0, by subtracting the minimum value and dividing it by the value range length. Nuclear size and mean intensity were represented as S(celli) and Imean(celli), respectively. We estimated the heat value of one tile integrating nuclear and mean intensity using the equationin Figure A2.ResultsWe generated heatmaps based on the heat values per tile inside each ROI from the 3 disease entities (CLL, aCLL and RT), as illustrated in Figures B1-4. Areas with high heat values (in the yellow spectrum) correspond to tiles harboring cells with increased nuclear size and mean intensity (PCs in CLL cases and expanded/ confluent PCs in aCLL and RT cases). In contrast, areas with low heat values (in the blue spectrum) correspond to tiles with decreased nuclear size and mean intensity (small neoplastic lymphocytes surrounding PCs) (Figure B4). We then generated a heat value histogram per tile for each ROI (Figure B5). Based on these results, the two optimal thresholds isolated to obtain the highest separation value among the three disease entities based on the optimal F-score were: 0.228, below which the case was most likely to be CLL, and 0.288, above which the case was most likely to be RT. Cases with heat values ranging between 0.228 and 0.288 were most likely aCLL cases. We then plotted the mean heat value frequencies of the 3 entities: There was a significant difference in the ranges of mean heat value frequencies for CLL, aCLL, and RT, which were 0.168 to 0.233, 0.212 to 0.307, and 0.261 to 0.353, respectively (Figure C). The accuracy and area under the curve diagnostic predictive values using data from nuclear size alone were 0.658 (+/-0.115) and 0.771 (+/-0.096), respectively; and using mean nuclear intensity, 0.679 (+/-0.094) and 0.841 (+/-0.052), respectively; with a noticeable increase using heat value frequencies (integrating the nuclear size and mean nuclear intensity) reaching 0.813 (+/-0.0630) and 0.885 (+/-0.109), respectively.ConclusionWe describe a novel AI-based heatmap technique to objectively assess the extent of PCs in CLL, based on the integrative analysis of cell nuclear size and mean nuclear intensity. We suggest that an ROI mean heat value less than 0.228 is predictive of CLL, and more than 0.288 is predictive of RT. aCLL cases demonstrate a mean heat value ranging from 0.228 to 0.288. Using the mean heat value of all cases, we were able to reliably separate the three entities in question with robust diagnostic predictive values. [Display omitted] DisclosuresKhoury: Stemline Therapeutics: Research Funding; Kiromic: Research Funding; Angle: Research Funding.

Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder.

To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins's statistic, Shapiro-Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05). Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013). Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.

Parietal Lobe Reorganization and Widespread Functional Connectivity Integration in Upper-Limb Amputees: A rs-fMRI Study

The right parietal lobe plays an important role in body image, and disorders of body image emerge after lesions in the parietal lobe or with parietal lobe epilepsy. Body image disorder also often accompanies upper-limb amputation, in which the patient misperceives that their missing limb is still part of their body. Cortical reorganization is known to occur after upper-limb amputation, but it is not clear how widespread and to what degree functional connectivity (FC) is reorganized post-amputation, nor whether such changes might be related to misperceptions of body image. Twenty-four subjects who had a traumatically upper-limb amputees (ULAs) and 24 age-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. Regions of interest (ROIs) in the right superior parietal gyrus (SPG_R) and right inferior parietal lobule (IPL_R) were defined using BrainNet Viewer. We calculated the amplitude of low-frequency fluctuations (ALFF) in ROIs and correlated the ROI mean amplitude of low-frequency fluctuations (mALFF) and mean scores on the phantom limb sensation (PLS) scale and beck depression index (BDI). We also calculated ROIs and whole-brain FC. Compared to the HC group, we observed significantly increased activation (mALFF) in ROIs of the ULA group. Moreover, correlation analyses revealed a significant positive correlation between ROI mALFF and scores on the PLS. There was a significant negative correlation between the SPG_R mALFF and BDI scores. Seed-based, whole-brain FC analysis revealed that FC in the ULA group significantly decreased in many brain regions across the entire brain. The right parietal lobe appears to be involved in some aspect of body awareness and depression in amputation patients. Upper-limb amputation results not only in reorganization in the local brain area formerly representing the missing limb, but also results in more widespread reorganization through FC changes in whole brain.

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior.

IntroductionPhenomena related to reward responsiveness have been extensively studied in their associations with substance use and socioemotional functioning. One important task in this literature is the Monetary Incentive Delay (MID) task. By cueing and delivering performance‐contingent reward, the MID task has been demonstrated to elicit robust activation of neural circuits involved in different phases of reward responsiveness. However, systematic evaluations of common MID task contrasts have been limited to between‐study comparisons of group‐level activation maps, limiting their ability to directly evaluate how researchers’ choice of contrasts impacts conclusions about individual differences in reward responsiveness or brain‐behavior associations.MethodsIn a sample of 104 participants (Age Mean = 19.3, SD = 1.3), we evaluate similarities and differences between contrasts in: group‐ and individual‐level activation maps using Jaccard's similarity index, region of interest (ROI) mean signal intensities using Pearson's r, and associations between ROI mean signal intensity and psychological measures using Bayesian correlation.ResultsOur findings demonstrate more similarities than differences between win and loss cues during the anticipation contrast, dissimilarity between some win anticipation contrasts, an apparent deactivation effect in the outcome phase, likely stemming from the blood oxygen level‐dependent undershoot, and behavioral associations that are less robust than previously reported.ConclusionConsistent with recent empirical findings, this work has practical implications for helping researchers interpret prior MID studies and make more informed a priori decisions about how their contrast choices may modify results.

Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults.

To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV]. Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14-64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADCmin, ADCmean) regions of interest were sited in solid tumour and normal appearing white matter (ADCNAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2nd, 5th, 10th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test). The median interval from imaging to tissue diagnosis was 9 (range, 0-74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADCmin value was 0.84 (±0.15 standard deviation, SD) ×10-3 mm2/s. In the largest tumour cross-section (excluding necrosis), an average ADCmean value of 1.12 (±0.25)×10-3 mm2/s was observed. The mean ADCmin/NAWM ratio was 1.097 (±0.149), and the mean ADCmean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2nd centile, no statistical difference was observed between the regional and histogram derived ADC results. H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study.

Influence of X-ray computed tomography (CT) exposure and reconstruction parameters on positron emission tomography (PET) quantitation

BackgroundThe CT of PET CT provides diagnostic information, anatomic localisation and attenuation correction (AC). When only AC is required, very lose dose CT is desirable. CT iterative reconstruction (IR) improves image quality with lower exposures however there is little data on very low dose IR CT for AC of PET. This work assesses the impact of CT exposure and reconstruction algorithm on PET voxel values.MethodAn anthropomorphic torso phantom was filled with physiologically typical [18]F concentrations in heart, liver and background compartments. A 17-mm-diameter right lung “tumour” filled with [18]F was included (surrounding lung contained no 18[F]). PET was acquired followed by 24 CT acquisitions with varying CT exposures (15–50 mAs, 80–120 kVp, pitch 0.671 or 0.828). Each CT was reconstructed twice using filtered back projection (FBP) or IR and these used for AC of PET. The reference PET reconstruction (RR) used CT acquired at 50 mAs, 120 kVp, pitch 0.828, IR, all others were test PET reconstructions (TR). Regions of interest (ROIs) were drawn in the liver, soft tissue and over “tumour” on each TR and compared with the RR. Voxel values in each TR were compared to the RR using a paired t test and by calculating which and what proportion of voxels in each TR differed by a quantitatively significant difference (QSD) from the RR.ResultsTRs reconstructed using lower dose CTs underestimated mean and maximum ROI activity relative to the RR; greater with IR than FBP. Once CT dose index (CTDI) increased to 1 mGy, differences were less than QSD. On voxel analysis, all TRs were significantly different to the RR (p < 0.0001). TRs reconstructed at the lowest CT exposure with IR had 6% of voxels that differed by greater than QSD. Differences were reduced with increasing CTDI and FBP reconstruction. Voxels which exceeded the QSD were spatially localised to regions of high activity, interfaces between different attenuation and areas of CT beam hardening.ConclusionsVery low dose CT exposures are feasible for accurate PET AC. Scanner- and reconstruction-specific validation should be employed prior very low dose CT AC for PET.

QUANTIFICATION OF TMJ RHEUMATOID ARTHRITIS ON POSITRON EMISSION TOMOGRAPHY SCANS USING ROVER SOFTWARE

<h3>Background</h3> Inflammation of the temporomandibular joint (TMJ) has a relatively high correlation (> 17%) to the late stages of rheumatoid arthritis (RA). RA is a chronic inflammatory autoimmune disease that causes cartilage and bone destruction. Cell activity of TMJ was measured by using 2 radiotracers, [18 F]-fluoro-2-deoxy-D-glucose (FDG) and [18 F]-sodium fluoride (NaF), for staging and surveillance of RA. <h3>Objective</h3> The aim of this study was to quantify the region of interest (ROI) volumes of FDG- and NaF-based TMJ positron emission tomography (PET) images by using the ROVER software. <h3>Materials and Methods</h3> Institutional review board (IRB) approval was obtained for 20 previously diagnosed patients with RA. From this cohort, 17 anonymized patients with NaF and FDG-PET/CT scans were reviewed by 3 calibrated investigators. When there was disagreement, a fourth investigator who is an expert in PET/CT, was used. The standardized uptake values corrected (SUV-Mean Cor) were calculated and recorded on ROVER. The collected data and literature review findings were collated and analyzed to provide a holistic comparison table. The clinical applications, advantages, and disadvantages of 8 imaging techniques were reviewed. <h3>Results</h3> The average ROI volumes differed between FDG- and NaF-based PET images in every case, showing a higher quantification for NaF-based scans. In 1 case, ROI mean total corrected value was 23.25 when NaF-PET was used, as opposed to 6.7 when FDG-PET was used. There was a similar trend among other cases included in the study. The NaF-PET ROI values appeared higher than the standard FDG-PET ROI values in the cases where the RA activity was quantified. SUV can be obtained by dividing the ROI activity concentration (KBq/L) by the decay-corrected amount of injected FDG (KBq/L) times the weight of the patient in grams. ROVER automatically calculates the average and maximum SUVs for all patients. <h3>Discussion</h3> With the use of ROVER, it is possible to collect accurate measurements of TMJ disease activity from both FDG-PET and NaF-PET images. NaF-PET appeared to be a more sensitive technique.

Evaluation of Hepatic Steatosis with CT and Correlation with Anthropometric Measurements.

The aim of the study was to evaluate hepatic steatosis in an asymptomatic group of patients with unenhanced abdominal computed tomography (CT) and to compare the results with anthropometric measurements. The study included 617 patients aged 18-93 years, who underwent unenhanced abdominopelvic CT between January 2016 and December 2017. Three imaging criteria were used in the assessment of hepatic steatosis on CT: mean region of interest (ROI) value of measured liver lobe (40 HU ≥), mean ROI value of measured liver lobe / measured spleen mean ROI value (1 ≥), mean ROI value of measured liver lobe - mean ROI value of spleen (10 HU≥). The liver fat was quantitatively assessed both visually and using multidetector CT grading. The anthropometric measurements used were the size of the liver and spleen, abdominal anterior-posterior diameter, abdominal transverse diameter, abdominal circumference, subcutaneous adipose tissue area, and anterior, posterior, and posterolateral subcutaneous adipose tissue thickness. The prevalence of hepatic steatosis was 29.3% according to the visual evaluation, 29.8% according to the quantitative evaluation, 67.1% according to at least one criterion and 23.3% according to at least two criteria. A positive correlation was determined between hepatic steatosis and anthropometric measurements. Differences between the genders were observed in both hepatic steatosis and anthropometric measurements. By setting more objective criteria for evaluation, with the possibility of quantitative analysis in particular, non-contrast CT will have a more important role in assessing liver fat in the future.

Magnetic resonance elastography of the supraspinatus muscle: A preliminary study on test-retest repeatability and wave quality with different frequencies and image filtering

The purpose of this study was to determine an optimal condition (vibration frequency and image filtering) for stiffness estimation with high accuracy and stiffness measurement with high repeatability in magnetic resonance elastography (MRE) of the supraspinatus muscle. Nine healthy volunteers underwent two MRE exams separated by at least a 30 min break, on the same day. MRE acquisitions were performed with a gradient-echo type multi-echo MR sequence at 75, 100, and 125 Hz pneumatic vibration. Wave images were processed by a bandpass filter or filter combining bandpass and directional filters (bandpass-directional filter). An observer specified the region of interest (ROI) on clear wave propagation in the supraspinatus muscle, within which the observer measured the stiffness. This study assessed wave image quality according to two indices, as a substitute for the assessment of the accuracy of the stiffness estimation. One is the size of the clear wave propagation area (ROI size used to measure the stiffness) and the other is the qualitative stiffness resolution score in that area. These measurements made by the observer were repeated twice at least one month apart after each MRE exam.This study assessed the intra-examiner and observer repeatability of the stiffness value, ROI size and resolution score in each combination of vibration frequency and image filter. Repeatability of the data was analyzed using the intraclass correlation coefficient (ICC) and 95% limits-of-agreement (LOA) in Bland-Altman analysis. The analyses on intra-examiner and observer repeatability of stiffness indicated that the ICC and 95% LOA were not varied greatly depending on vibration frequency and image filter (intra-examiner repeatability, ICC range, 0.79 to 0.88; 95% LOA range, ±23.95 to ±32.42%, intra-observer repeatability, ICC range, 0.98 to 1.00; 95% LOA range, ±5.10 to ±10.99%). In the analyses on intra-examiner repeatability of ROI size, ICCs were rather low (ranging from: 0.03 to 0.69) while 95% LOA was large in all the combinations of vibration frequency and image filter (ranging from: ±62.66 to ±83.33%). In the analyses on intra-observer repeatability of ROI size, ICCs were sufficiently high in the total combination of vibration frequency and image filter (ranging from 0.80 to 0.87) while the 95% LOAs were better (lower) in the bandpass-directional filter than the bandpass filter (bandpass directional filter vs. bandpass filter, ±28.81 vs. ±54.83% at 75 Hz; ±25.63 vs. ±37.83% at 100 Hz; ±34.51 vs. ±43.36% at 125 Hz). In the analyses on intra-examiner and observer repeatability of resolution score, the mean difference (bias) between the two exams (or observations) was significantly low and there was almost no difference across all the combinations of vibration frequency and image filter (range of bias: −0.11–0.11 and −0.17–0.00, respectively).Additionally, effects of vibration frequency and image filter on wave image quality (ROI size and resolution score) were assessed separately in each exam. Both mean ROI size and resolution score in the bandpass-directional filter were larger than those in the bandpass filter. Among the data in the bandpass-directional filter, mean ROI size was larger at 75 and 100 Hz, and mean resolution score was larger at 100 and 125 Hz. Taking into consideration with the results of repeatability and wave image quality, the present results suggest that optimal vibration frequency and image filter for MRE of the supraspinatus muscles is 100 Hz and bandpass-directional filter, respectively.

133P - Evaluating lung ct density changes among patients with extensive stage small cell lung cancer (ES-SCLC) treated with thoracic radiotherapy (TRT) alone or TRT followed by combined ipilimumab (IPI) and nivolumab (NIVO)

BackgroundRadiation induced CT changes may be apparent following completion of TRT. We sought to quantify differences in radiation-associated densities on serial CT scans of patients with ES-SCLC treated with TRT alone versus TRT followed by combined IPI and NIVO. MethodsBetween 2016 and 2018, patients at a single institution with ES-SCLC who achieved stable disease or better following initial treatment with platinum doublet chemotherapy were offered TRT and prescribed a total dose of 30Gy in 10 fractions targeting initially involved thoracic tumor sites. Combined IPI 3mg/kg and NIVO 1mg/kg was administered every 3 weeks for up to 4 doses. We evaluated an irradiated region of interest (ROI) within the lungs and a volume of lung (outside the Planning Target Volume) receiving > 20Gy. Within the ROI, we calculated the Hounsfield unit (HU) mean for each patient prior to therapy and at subsequent follow-up CT thorax at least 60 days and closest to 120 days after commencing TRT. To quantify CT density change, we measured the difference in HU mean within the irradiated ROI before and after treatment. ResultsSeventeen patients enrolled on NCT03043599 received TRT followed by combined IPI/NIVO. Two additional patients received the same treatment off protocol. Eleven patients received TRT alone (no IPI/NIVO). The average increase in HU mean within 20Gy irradiated ROI before and after treatment was 9% (max 59%, min -19%) across the study cohort (n=30). CTCAE grade 3 or higher pulmonary toxicity (N=8 of 30) was significantly associated with increased CT density change within the ROI (mean 28% vs 2%, p=0.001). Treatment with TRT and IPI/NIVO (N=19 of 30) demonstrated a trend towards increased mean CT density change within the ROI compared to patients treated with TRT alone (mean 13% vs. 0%, p=0.1). ConclusionsQuantifying CT density change within irradiated lung parenchyma may offer a novel approach to predict radiation associated pulmonary toxicities. Measuring density changes across patient cohorts receiving TRT with novel systemic therapies may help to identify combined treatment strategies likely to be associated with diminished risk of toxicity. Legal entity responsible for the studyThe authors. FundingBristol-Myers Squibb. DisclosureS. Kim: Research grant / Funding (institution): Bristol-Myers Squibb. S.A. Rosenberg: Advisory / Consultancy: Novocure. J.E. Gray: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca; Honoraria (self), Advisory / Consultancy: Celgene; Honoraria (self), Advisory / Consultancy: Takeda; Honoraria (self), Advisory / Consultancy: Janssen; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Genentech; Honoraria (self), Advisory / Consultancy: Eli Lilly; Honoraria (self), Advisory / Consultancy: Triptych Health Partners. S.J. Antonia: Advisory / Consultancy: Bristol-Myers Squibb; Advisory / Consultancy, Research grant / Funding (institution): Novartis; Advisory / Consultancy: Merck; Advisory / Consultancy: CBMG; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy: AstraZeneca/MedImmune; Advisory / Consultancy: Memgen; Advisory / Consultancy: FLX Bio; Advisory / Consultancy: Nektar; Advisory / Consultancy: Venn. B. Perez: Advisory / Consultancy: Bristol-Myers Squibb; Advisory / Consultancy: AstraZeneca. All other authors have declared no conflicts of interest.