Craniofacial Collaboration UK: Developmental Outcomes in 5-Year-Old Children With Sagittal Synostosis.

ABSTRACT: The Craniofacial Collaboration UK (CC-UK) is a joint initiative that seeks to address some of the limitations of previous developmental research with this patient group by providing systematically collected, robust data from clinically and chronologically homogenous representative samples of children. The current paper outlines the developmental outcomes at the age of 5 for children who had previously undergone primary surgery for single-suture sagittal synostosis (SS). It shows broad consistencies with the previous CC-UK work, indicating that the majority of children with SS will perform within the average range compared to peers across a number of developmental, behavioral and emotional domains. However, the group mean for children with SS indicates significantly greater difficulties with fine motor skills and hyperactivity, relative to normative data. Unexpectedly, children with SS had significantly better problem solving skills. While it is reassuring that the majority of children are broadly developing in line with their unaffected peers, these small but significant differences may be early indicators of some of the subtle difficulties documented in older children with craniosynostosis. Longitudinal follow up is therefore important to understand the developmental trajectory for children with SS and identification of potentially 'at risk' sub groups within this diagnostic cohort.

Dynamic contrast-enhanced CT for the assessment of tumour response in malignant pleural mesothelioma: a pilot study.

OBJECTIVES: The aim of this pilot study was to investigate the utility of haemodynamic parameters derived from dynamic contrast-enhanced computed tomography (DCE-CT) scans in the assessment of tumour response to treatment in malignant pleural mesothelioma (MPM) patients. METHODS: The patient cohort included nine patients undergoing chemotherapy and five patients on observation. Each patient underwent two DCE-CT scans separated by approximately 2 months. The DCE-CT parameters of tissue blood flow (BF) and tissue blood volume (BV) were obtained within the dynamically imaged tumour. Mean relative changes in tumour DCE-CT parameters between scans were compared between the on-treatment and on-observation cohorts. DCE-CT parameter changes were correlated with relative change in tumour bulk evaluated according to the modified RECIST protocol. RESULTS: Differing trends in relative change in BF and BV between scans were found between the two patient groups (p = 0.19 and p = 0.06 for BF and BV, respectively). No significant rank correlations were found when comparing relative changes in DCE-CT parameters with relative change in tumour bulk. CONCLUSIONS: Differing trends in the relative change of BF and BV between patients on treatment and on observation indicate the potential of DCE-CT for the assessment of pharmacodynamic endpoints with respect to treatment in MPM. A future study with a larger patient cohort and unified treatment regimens should be undertaken to confirm the results of this pilot study. KEY POINTS: • CT-derived haemodynamic parameters show differing trends between malignant pleural mesothelioma patients on treatment and patients off treatment • Changes in haemodynamic parameters do not correlate with changes in tumour bulk as measured according to the modified RECIST protocol • Differing trends across the two patient groups indicate the potential sensitivity of DCE-CT to assess pharmacodynamic endpoints in the treatment of MPM.

Preliminary Analysis From the Craniofacial Collaboration United Kingdom Developmental Outcomes in Children With Sagittal Synostosis.

The Craniofacial Collaboration United Kingdom (CC-UK) was established across the 4 Highly Specialized Craniofacial Centres (HSCCs) in the UK in 2015. This is the first wave of data to be analyzed, looking at 3-year-old children with sagittal synostosis who have had primary corrective surgery. This is a comprehensive, homogenous dataset, looking at parental measures of development and behavior. The results indicate that the majority of children are rated by their parents as falling within one standard deviation of the mean for both developmental and behavioral measures. However, there was a trend across the age groups within the sample which, although not statistically significant, indicates that more developmental difficulties may become apparent as children get older. Behavior was rated as more problematic, and the areas of greatest parental concern were Conduct (challenging or confrontational behavior) and Hyperactivity domains, where 24% of children were rated as within the clinically significant range. Although the majority of children were rated as falling within the average range, the difference in the mean between the sagittal and the normative group was significant in 5 of the 6 behavioral domains. Further research is required to examine whether these findings are stable over time and to look at the mechanism which might be driving these changes. It is anticipated that future CC-UK analysis will elucidate this more clearly.

Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans.

We evaluated the image registration accuracy achieved using two deformable registration algorithms when radiation-induced normal tissue changes were present between serial computed tomography (CT) scans. Two thoracic CT scans were collected for each of 24 patients who underwent radiation therapy (RT) treatment for lung cancer, eight of whom experienced radiologically evident normal tissue damage between pre- and post-RT scan acquisition. For each patient, 100 landmark point pairs were manually placed in anatomically corresponding locations between each pre- and post-RT scan. Each post-RT scan was then registered to the pre-RT scan using (1) the Plastimatch demons algorithm and (2) the Fraunhofer MEVIS algorithm. The registration accuracy for each scan pair was evaluated by comparing the distance between landmark points that were manually placed in the post-RT scans and points that were automatically mapped from pre- to post-RT scans using the displacement vector fields output by the two registration algorithms. For both algorithms, the registration accuracy was significantly decreased when normal tissue damage was present in the post-RT scan. Using the Plastimatch algorithm, registration accuracy was 2.4 mm, on average, in the absence of radiation-induced damage and 4.6 mm, on average, in the presence of damage. When the Fraunhofer MEVIS algorithm was instead used, registration errors decreased to 1.3 mm, on average, in the absence of damage and 2.5 mm, on average, when damage was present. This work demonstrated that the presence of lung tissue changes introduced following RT treatment for lung cancer can significantly decrease the registration accuracy achieved using deformable registration.

Lung texture in serial thoracic CT scans: assessment of change introduced by image registration.

PURPOSE: The aim of this study was to quantify the effect of four image registration methods on lung texture features extracted from serial computed tomography (CT) scans obtained from healthy human subjects. METHODS: Two chest CT scans acquired at different time points were collected retrospectively for each of 27 patients. Following automated lung segmentation, each follow-up CT scan was registered to the baseline scan using four algorithms: (1) rigid, (2) affine, (3) B-splines deformable, and (4) demons deformable. The registration accuracy for each scan pair was evaluated by measuring the Euclidean distance between 150 identified landmarks. On average, 1432 spatially matched 32 × 32-pixel region-of-interest (ROI) pairs were automatically extracted from each scan pair. First-order, fractal, Fourier, Laws' filter, and gray-level co-occurrence matrix texture features were calculated in each ROI, for a total of 140 features. Agreement between baseline and follow-up scan ROI feature values was assessed by Bland-Altman analysis for each feature; the range spanned by the 95% limits of agreement of feature value differences was calculated and normalized by the average feature value to obtain the normalized range of agreement (nRoA). Features with small nRoA were considered "registration-stable." The normalized bias for each feature was calculated from the feature value differences between baseline and follow-up scans averaged across all ROIs in every patient. Because patients had "normal" chest CT scans, minimal change in texture feature values between scan pairs was anticipated, with the expectation of small bias and narrow limits of agreement. RESULTS: Registration with demons reduced the Euclidean distance between landmarks such that only 9% of landmarks were separated by ≥1 mm, compared with rigid (98%), affine (95%), and B-splines (90%). Ninety-nine of the 140 (71%) features analyzed yielded nRoA > 50% for all registration methods, indicating that the majority of feature values were perturbed following registration. Nineteen of the features (14%) had nRoA < 15% following demons registration, indicating relative feature value stability. Student's t-tests showed that the nRoA of these 19 features was significantly larger when rigid, affine, or B-splines registration methods were used compared with demons registration. Demons registration yielded greater normalized bias in feature value change than B-splines registration, though this difference was not significant (p = 0.15). CONCLUSIONS: Demons registration provided higher spatial accuracy between matched anatomic landmarks in serial CT scans than rigid, affine, or B-splines algorithms. Texture feature changes calculated in healthy lung tissue from serial CT scans were smaller following demons registration compared with all other algorithms. Though registration altered the values of the majority of texture features, 19 features remained relatively stable after demons registration, indicating their potential for detecting pathologic change in serial CT scans. Combined use of accurate deformable registration using demons and texture analysis may allow for quantitative evaluation of local changes in lung tissue due to disease progression or treatment response.

A systematic review of adult attachment and social anxiety.

BACKGROUND: Attachment has been implicated in the development of social anxiety. Our aim was to synthesise the extant literature exploring the role of adult attachment in these disorders. METHOD: Search terms relating to social anxiety and attachment were entered into MEDLINE, PsycINFO and Web of Science. Risk of bias of included studies was assessed using and adapted version of the Agency for Healthcare Research and Quality assessment tool. Eligible studies employed validated social anxiety and attachment assessments in adult clinical and analogue samples. The review included cross sectional, interventional and longitudinal research. RESULTS: Of the 30 identified studies, 28 showed a positive association between attachment insecurity and social anxiety. This association was particularly strong when considering attachment anxiety. Cognitive variables and evolutionary behaviours were identified as potential mediators, concordant with psychological theory. LIMITATIONS: Due to a lack of longitudinal research, the direction of effect between attachment and social anxiety variables could not be inferred. There was substantial heterogeneity in the way that attachment was conceptualised and assessed across studies. CONCLUSIONS: The literature indicates that attachment style is associated with social anxiety. Clinicians may wish to consider attachment theory when working clinically with this population. In the future, it may be useful to target the processes that mediate the relationship between attachment and social anxiety.

Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis.

PURPOSE: To determine whether the addition of standardized uptake value (SUV) from PET scans to CT lung texture features could improve a radiomics-based model of radiation pneumonitis (RP) diagnosis in patients undergoing radiotherapy. METHODS AND MATERIALS: Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were collected including pre-therapy PET/CT scans, pre-/post-therapy diagnostic CT scans and RP status. Twenty texture features (first-order, fractal, Laws' filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. Classifier performance (texture, SUV, or combination) was assessed by calculating the area under the receiver operating characteristic curve (AUC). For each texture feature, logistic regression classifiers consisting of the average change in texture feature value and the pre-therapy SUV standard deviation (SUVSD ) were created and compared with the texture feature as a lone classifier using ANOVA with correction for multiple comparisons (P < 0.0025). RESULTS: While clinical parameters (mean lung dose, smoking history, tumor location) were not significantly different among patients with and without symptomatic RP, SUV and texture parameters were significantly associated with RP status. AUC for single-texture feature classifiers alone ranged from 0.58 to 0.81 and 0.53 to 0.71 in high-dose (≥ 30 Gy) and low-dose (< 10 Gy) regions of the lungs, respectively. AUC for SUVSD alone was 0.69 (95% confidence interval: 0.54-0.83). Adding SUVSD into a logistic regression model significantly improved model fit for 18, 14 and 11 texture features and increased the mean AUC across features by 0.08, 0.06, and 0.04 in the low-, medium-, and high-dose regions, respectively. CONCLUSIONS: Addition of SUVSD to a single-texture feature improves classifier performance on average, but the improvement is smaller in magnitude when SUVSD is added to an already effective classifier using texture alone. These findings demonstrate the potential for more accurate assessment of RP using information from multiple imaging modalities.

Social imitation of alcohol consumption and ingratiation motives in young adults.

Across 2 studies we tested the hypothesis that social ingratiation motives may be an important factor explaining social imitation of alcohol consumption. In Study 1, participants drank alcohol with a heavy versus light drinking confederate under conditions that were designed to heighten or reduce (participants believed they would not be judged) motivation for ingratiation. In Study 2 we manipulated the degree to which participants believed they had already successfully ingratiated themselves with a heavy or no (alcohol) drinking confederate. In Study 1, participants' alcohol consumption was most strongly influenced by the confederate's drinking behavior when they believed that they would later be judged by the confederate. In Study 2, participants' alcohol consumption was influenced by the confederate's drinking behavior and this effect was particularly pronounced if participants were unsure if the confederate had accepted them. The desire for social ingratiation may in part explain why people imitate the drinking behavior of those around them. (PsycINFO Database Record

Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development.

PURPOSE: To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). METHODS AND MATERIALS: A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. RESULTS: For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84). CONCLUSIONS: A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.

Effect of deformable registration on the dose calculated in radiation therapy planning CT scans of lung cancer patients.

PURPOSE: To characterize the effects of deformable image registration of serial computed tomography (CT) scans on the radiation dose calculated from a treatment planning scan. METHODS: Eighteen patients who received curative doses (≥ 60 Gy, 2 Gy/fraction) of photon radiation therapy for lung cancer treatment were retrospectively identified. For each patient, a diagnostic-quality pretherapy (4-75 days) CT scan and a treatment planning scan with an associated dose map were collected. To establish correspondence between scan pairs, a researcher manually identified anatomically corresponding landmark point pairs between the two scans. Pretherapy scans then were coregistered with planning scans (and associated dose maps) using the demons deformable registration algorithm and two variants of the Fraunhofer MEVIS algorithm ("Fast" and "EMPIRE10"). Landmark points in each pretherapy scan were automatically mapped to the planning scan using the displacement vector field output from each of the three algorithms. The Euclidean distance between manually and automatically mapped landmark points (dE) and the absolute difference in planned dose (|ΔD|) were calculated. Using regression modeling, |ΔD| was modeled as a function of dE, dose (D), dose standard deviation (SD(dose)) in an eight-pixel neighborhood, and the registration algorithm used. RESULTS: Over 1400 landmark point pairs were identified, with 58-93 (median: 84) points identified per patient. Average |ΔD| across patients was 3.5 Gy (range: 0.9-10.6 Gy). Registration accuracy was highest using the Fraunhofer MEVIS EMPIRE10 algorithm, with an average dE across patients of 5.2 mm (compared with >7 mm for the other two algorithms). Consequently, average |ΔD| was also lowest using the Fraunhofer MEVIS EMPIRE10 algorithm. |ΔD| increased significantly as a function of dE (0.42 Gy/mm), D (0.05 Gy/Gy), SD(dose) (1.4 Gy/Gy), and the algorithm used (≤ 1 Gy). CONCLUSIONS: An average error of <4 Gy in radiation dose was introduced when points were mapped between CT scan pairs using deformable registration, with the majority of points yielding dose-mapping error <2 Gy (approximately 3% of the total prescribed dose). Registration accuracy was highest using the Fraunhofer MEVIS EMPIRE10 algorithm, resulting in the smallest errors in mapped dose. Dose differences following registration increased significantly with increasing spatial registration errors, dose, and dose gradient (i.e., SDdose). This model provides a measurement of the uncertainty in the radiation dose when points are mapped between serial CT scans through deformable registration.

Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy.

This study examines the correlation between the radiologist-defined severity of normal tissue damage following radiation therapy (RT) for lung cancer treatment and a set of mathematical descriptors of computed tomography (CT) scan texture ('texture features'). A pre-therapy CT scan and a post-therapy CT scan were retrospectively collected under IRB approval for each of the 25 patients who underwent definitive RT (median dose: 66 Gy). Sixty regions of interest (ROIs) were automatically identified in the non-cancerous lung tissue of each post-therapy scan. A radiologist compared post-therapy scan ROIs with pre-therapy scans and categorized each as containing no abnormality, mild abnormality, moderate abnormality, or severe abnormality. Twenty texture features that characterize gray-level intensity, region morphology, and gray-level distribution were calculated in post-therapy scan ROIs and compared with anatomically matched ROIs in the pre-therapy scan. Linear regression and receiver operating characteristic (ROC) analysis were used to compare the percent feature value change (ΔFV) between ROIs at each category of visible radiation damage. Most ROIs contained no (65%) or mild abnormality (30%). ROIs with moderate (3%) or severe (2%) abnormalities were observed in 9 patients. For 19 of 20 features, ΔFV was significantly different among severity levels. For 12 features, significant differences were observed at every level. Compared with regions with no abnormalities, ΔFV for these 12 features increased, on average, by 1.5%, 12%, and 30%, respectively, for mild, moderate, and severe abnormalitites. Area under the ROC curve was largest when comparing ΔFV in the highest severity level with the remaining three categories (mean AUC across features: 0.84). In conclusion, 19 features that characterized the severity of radiologic changes from pre-therapy scans were identified. These features may be used in future studies to quantify acute normal lung tissue damage following RT.

Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions.

PURPOSE: The aim of this study was to compare three demons registration-based methods to identify spatially matched regions in serial computed tomography (CT) scans for use in texture analysis. METHODS: Two thoracic CT scans containing no lung abnormalities and acquired during serial examinations separated by at least one week were retrospectively collected from 27 patients. Over 1000 regions of interest (ROIs) were randomly placed in the lungs of each baseline scan. Anatomically matched ROIs in the corresponding follow-up scan were placed by mapping the baseline scan ROI center pixel to (1) the original follow-up scan, (2) the follow-up scan resampled to match the baseline scan voxel size, and (3) the follow-up scan aligned to the baseline scan through affine registration. Mappings used the vector field obtained through demons deformable registration of each follow-up scan variant to the baseline scan. 140 texture features distributed among five feature classes were calculated in all ROIs. Feature value differences between paired ROIs were evaluated using Bland-Altman 95% limits of agreement. For each feature, (1) the mean feature value change and (2) the difference between the upper and lower limits of agreement were normalized to the mean feature value to obtain, respectively, the normalized bias and normalized range of agreement (nRoA). Nonparametric tests were used to evaluate differences in normalized bias and nRoA across the three methods. RESULTS: Because patient CT scans contained no pathology, minimal changes in feature values were expected (i.e., low nRoA and normalized bias). Seventy-five features with very large feature value variability (nRoA ≥ 100%) were excluded from further analysis. Across the remaining 65 features, significant differences in normalized bias were observed among the three methods. The lowest normalized bias (median: 0.06%) was achieved when feature values were calculated on original follow-up scans. The affine registration method achieved the lowest nRoA, though nRoA was not significantly increased using original follow-up scans. Features with low nRoA values also had low normalized bias, though the converse was not necessarily true. Using nRoA as a metric, a set of 20 features having both low nRoA and normalized bias were identified. CONCLUSIONS: Three methods to facilitate texture analysis of serial CT scans using demons registration for ROI placement were evaluated. The bias in feature value change between matched ROIs was minimized when feature values were calculated on original baseline and follow-up scans. A set of features that had both low bias and variability (nRoA) in feature value change using this method were identified. This texture analysis approach could facilitate future measurement of pathologic changes between CT scans without necessitating calculation of feature values on deformed scans.