A guided network estimation approach using multi-omic information.

INTODUCTION: In systems biology, an organism is viewed as a system of interconnected molecular entities. To understand the functioning of organisms it is essential to integrate information about the variations in the concentrations of those molecular entities. This information can be structured as a set of networks with interconnections and with some hierarchical relations between them. Few methods exist for the reconstruction of integrative networks. OBJECTIVE: In this work, we propose an integrative network reconstruction method in which the network organization for a particular type of omics data is guided by the network structure of a related type of omics data upstream in the omic cascade. The structure of these guiding data can be either already known or be estimated from the guiding data themselves. METHODS: The method consists of three steps. First a network structure for the guiding data should be provided. Next, responses in the target set are regressed on the full set of predictors in the guiding data with a Lasso penalty to reduce the number of predictors and an L2 penalty on the differences between coefficients for predictors that share edges in the network for the guiding data. Finally, a network is reconstructed on the fitted target responses as functions of the predictors in the guiding data. This way we condition the target network on the network of the guiding data. CONCLUSIONS: We illustrate our approach on two examples in Arabidopsis. The method detects groups of metabolites that have a similar genetic or transcriptomic basis.

Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia.

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins (proximity extension-based (PEA) immunoassays) in a deeply-phenotyped mixed-memory clinic cohort (n=502, mean age (sd) = 64.1 [8.7] years, 181 female [35.4%]), including patients with Alzheimer's disease (AD, n=213), dementia with Lewy bodies (DLB, n=50) and frontotemporal dementia (FTD, n=93), and controls (n=146). Validation was assessed in independent cohorts (n=99 PEA platform, n=198, MRM-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P=1.65e-08), ZCWPW1-PILRB (rs1476679, P=2.73e-32), CTSH-CTSH (rs3784539, P=2.88e-24) and HESX1-RETN (rs186108507, P=8.39e-08), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90e-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for frontotemporal dementia, either for the total sample as for analyses performed within FTD only. pQTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia.

CSF proteome profiling reveals biomarkers to discriminate dementia with Lewy bodies from Alzheimer´s disease.

Diagnosis of dementia with Lewy bodies (DLB) is challenging and specific biofluid biomarkers are highly needed. We employed proximity extension-based assays to measure 665 proteins in the cerebrospinal fluid (CSF) from patients with DLB (n = 109), Alzheimer´s disease (AD, n = 235) and cognitively unimpaired controls (n = 190). We identified over 50 CSF proteins dysregulated in DLB, enriched in myelination processes among others. The dopamine biosynthesis enzyme DDC was the strongest dysregulated protein, and could efficiently discriminate DLB from controls and AD (AUC:0.91 and 0.81 respectively). Classification modeling unveiled a 7-CSF biomarker panel that better discriminate DLB from AD (AUC:0.93). A custom multiplex panel for six of these markers (DDC, CRH, MMP-3, ABL1, MMP-10, THOP1) was developed and validated in independent cohorts, including an AD and DLB autopsy cohort. This DLB CSF proteome study identifies DLB-specific protein changes and translates these findings to a practicable biomarker panel that accurately identifies DLB patients, providing promising diagnostic and clinical trial testing opportunities.

Psychiatric symptoms of frontotemporal dementia and subcortical (co-)pathology burden: new insights.

Three subtypes of distinct pathological proteins accumulate throughout multiple brain regions and shape the heterogeneous clinical presentation of frontotemporal lobar degeneration (FTLD). Besides the main pathological subtypes, co-occurring pathologies are common in FTLD brain donors. The objective of this study was to investigate how the location and burden of (co-)pathology correlate to early psychiatric and behavioural symptoms of FTLD. Eighty-seven brain donors from The Netherlands Brain Bank cohort (2008-2017) diagnosed with FTLD were included: 46 FTLD-TAR DNA-binding protein 43 (FTLD-TDP), 34 FTLD-tau, and seven FTLD-fused-in-sarcoma (FTLD-FUS). Post-mortem brain tissue was dissected into 20 standard regions and stained for phosphorylated TDP-43, phosphorylated tau, FUS, amyloid-ß, and α-synuclein. The burden of each pathological protein in each brain region was assessed with a semi-quantitative score. Clinical records were reviewed for early psychiatric and behavioural symptoms. Whole-brain clinico-pathological partial correlations were calculated (local false discovery rate threshold = 0.01). Elaborating on the results, we validated one finding using a quantitative assessment of TDP-43 pathology in the granular layer of the hippocampus in FTLD-TDP brain donors with (n = 15) and without (n = 15) hallucinations. In subcortical regions, the presence of psychiatric symptoms showed positive correlations with increased hippocampal pathology burden: hallucinations with TDP-43 in the granular layer (R = 0.33), mania with TDP-43 in CA1 (R = 0.35), depression with TDP-43 in CA3 and with parahippocampal tau (R = 0.30 and R = 0.23), and delusions with CA3 tau (R = 0.26) and subicular amyloid-ß (R = 0.25). Behavioural disinhibition showed positive correlations with tau burden in the thalamus (R = 0.29) and with both TDP-43 and amyloid-ß burden in the subthalamus (R = 0.23 and R = 0.24). In the brainstem, the presence of α-synuclein co-pathology in the substantia nigra correlated with disinhibition (R = 0.24), tau pathology in the substantia nigra correlated with depression (R = 0.25) and in the locus coeruleus with both depression and perseverative/compulsive behaviour (R = 0.26 and R = 0.32). The quantitative assessment of TDP-43 in the granular layer validated the higher burden of TDP-43 pathology in brain donors with hallucinations compared to those without hallucinations (P = 0.007). Our results show that psychiatric symptoms of FTLD are linked to subcortical pathology burden in the hippocampus, and hallucinations are linked to a higher burden of TDP-43 in the granular layer. Co-occurring non-FTLD pathologies in subcortical regions could contribute to configuring the clinical phenotype of FTLD.

Psychoneurological Symptoms and Biomarkers of Stress and Inflammation in Newly Diagnosed Head and Neck Cancer Patients: A Network Analysis.

Psychoneurological symptoms are commonly reported by newly diagnosed head and neck cancer (HNC) patients, yet there is limited research on the associations of these symptoms with biomarkers of stress and inflammation. In this article, pre-treatment data of a multi-center cohort of HNC patients were analyzed using a network analysis to examine connections between symptoms (poor sleep quality, anxiety, depression, fatigue, and oral pain), biomarkers of stress (diurnal cortisol slope), inflammation markers (c-reactive protein [CRP], interleukin [IL]-6, IL-10, and tumor necrosis factor alpha [TNF-α]), and covariates (age and body mass index [BMI]). Three centrality indices were calculated: degree (number of connections), closeness (proximity of a variable to other variables), and betweenness (based on the number of times a variable is located on the shortest path between any pair of other variables). In a sample of 264 patients, poor sleep quality and fatigue had the highest degree index; fatigue and CRP had the highest closeness index; and IL-6 had the highest betweenness index. The model yielded two clusters: a symptoms-cortisol slope-CRP cluster and a IL-6-IL-10-TNF-α-age-BMI cluster. Both clusters were connected most prominently via IL-6. Our findings provide evidence that poor sleep quality, fatigue, CRP, and IL-6 play an important role in the interconnections between psychoneurological symptoms and biomarkers of stress and inflammation in newly diagnosed HNC patients.

Semi-supervised empirical Bayes group-regularized factor regression.

The features in a high-dimensional biomedical prediction problem are often well described by low-dimensional latent variables (or factors). We use this to include unlabeled features and additional information on the features when building a prediction model. Such additional feature information is often available in biomedical applications. Examples are annotation of genes, metabolites, or p-values from a previous study. We employ a Bayesian factor regression model that jointly models the features and the outcome using Gaussian latent variables. We fit the model using a computationally efficient variational Bayes method, which scales to high dimensions. We use the extra information to set up a prior model for the features in terms of hyperparameters, which are then estimated through empirical Bayes. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predicts oral cancer metastasis from RNAseq data.

Confirmatory factor analysis including MRI-derived adipose tissues quantification improves associations of metabolic dysregulation to diastolic dysfunction.

AIMS: To quantify metabolic impairment via a one-factor approach with confirmatory factor analysis (CFA) including MRI-derived visceral and subcutaneous adipose tissues and to associate it with diastolic dysfunction. METHODS: In this cross-sectional analysis, 916 participants (53% female, mean age (SD): 56 (6)) underwent abdominal and cardiovascular MRI. With CFA a metabolic-load factor of metabolic-syndrome variables and visceral and subcutaneous adipose tissues was constructed. A piecewise structural equation model approach with adjustment for confounding factors was used to determine associations with left-ventricular diastolic function, cardiac morphology and hemodynamics. RESULTS: Model fitting excluding blood pressure and waist circumference but including visceral and subcutaneous adipose tissues, fasting glucose, HDL-c and triglycerides was used to construct the metabolic-load factor. Evaluating measurement invariance demonstrated sex-specificity. Change in mitral early/late peak filling rate ratio was -0.12 for both males [-0.20; -0.05, p > 0.05] and females [-0.17; -0.07, p > 0.001] per SD of metabolic-load factor. Change in deceleration time of mitral early filling was -11.83 ms in females [-17.38; -6.27] per SD of metabolic-load factor. CONCLUSION: A single latent metabolic-load factor via CFA including MRI-derived adipose tissues increased sensitivity for metabolic impairment obsoleting waist circumference and is associated with a decreased left-ventricular diastolic function, more apparent in females than in males.

Effect of eight-week online cognitive training in Parkinson's disease: A double-blind, randomized, controlled trial.

INTRODUCTION: Cognitive training (CT) has been proposed as a treatment option for cognitive impairment in Parkinson's disease (PD). We aimed to assess the efficacy of adaptive, computerized CT on cognitive function in PD. METHODS: In this double-blind, randomized controlled trial we enrolled PD patients that experienced substantial subjective cognitive complaints. Over a period of eight weeks, participants underwent 24 sessions of computerized multi-domain CT or an active control intervention for 45 min each (randomized 1:1). The primary outcome was the accuracy on the Tower of London task; secondary outcomes included effects on other neuropsychological outcomes and subjective cognitive complaints. Outcomes were assessed before and after training and at six-months follow-up, and analyzed with multivariate mixed-model analyses. RESULTS: The intention-to-treat population consisted of 136 participants (n = 68 vs. n = 68, age M: 62.9y, female: 39.7%). Multivariate mixed-model analyses showed no group difference on the Tower of London accuracy corrected for baseline performance (n = 130): B: -0.06, 95% CI: -0.27 to 0.15, p = 0.562. Participants in the CT group were on average 0.30 SD (i.e., 1.5 s) faster on difficulty load 4 of this task (secondary outcome): 95% CI: -0.55 to -0.06, p = 0.015. CT did not reduce subjective cognitive complaints. At follow-up, no group differences were found. CONCLUSIONS: This study shows no beneficial effect of eight-week computerized CT on the primary outcome (i.e., planning accuracy) and only minor improvements on secondary outcomes (i.e., processing speed) with limited clinical impact. Personalized or ecologically valid multi-modal intervention methods could be considered to achieve clinically meaningful and lasting effects.

CSF proteome profiling across the Alzheimer's disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels.

Development of disease-modifying therapies against Alzheimer's disease (AD) requires biomarkers reflecting the diverse pathological pathways specific for AD. We measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aß+): n = 50), AD-dementia (n = 230), non-AD dementias (n = 322) and cognitively unimpaired controls (n = 195) using proximity ligation-based immunoassays. Here we identified >100 CSF proteins dysregulated in MCI(Aß+) or AD compared to controls or non-AD dementias. Proteins dysregulated in MCI(Aß+) were primarily related to protein catabolism, energy metabolism and oxidative stress, whereas those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled biomarker panels discriminating clinical groups with high accuracies (area under the curve (AUC): 0.85-0.99), which were translated into custom multiplex assays and validated in external and independent cohorts (AUC: 0.8-0.99). Overall, this study provides novel pathophysiological leads delineating the multifactorial nature of AD and potential biomarker tools for diagnostic settings or clinical trials.

Symptom clusters among cancer survivors: what can machine learning techniques tell us?

PURPOSE: Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set. METHODS: Data consisted of self-reports of cancer survivors who used a fully automated online application 'Oncokompas' that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately. RESULTS: When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues. CONCLUSION: There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.

Profound Pathogen-Specific Alterations in Intestinal Microbiota Composition Precede Late-Onset Sepsis in Preterm Infants: A Longitudinal, Multicenter, Case-Control Study.

BACKGROUND: The role of intestinal microbiota in the pathogenesis of late-onset sepsis (LOS) in preterm infants is largely unexplored but could provide opportunities for microbiota-targeted preventive and therapeutic strategies. We hypothesized that microbiota composition changes before the onset of sepsis, with causative bacteria that are isolated later in blood culture. METHODS: This multicenter case-control study included preterm infants born under 30 weeks of gestation. Fecal samples collected from the 5 days preceding LOS diagnosis were analyzed using a molecular microbiota detection technique. LOS cases were subdivided into 3 groups: gram-negative, gram-positive, and coagulase-negative Staphylococci (CoNS). RESULTS: Forty LOS cases and 40 matched controls were included. In gram-negative LOS, the causative pathogen could be identified in at least 1 of the fecal samples collected 3 days prior to LOS onset in all cases, whereas in all matched controls, this pathogen was absent (P = .015). The abundance of these pathogens increased from 3 days before clinical onset. In gram-negative and gram-positive LOS (except CoNS) combined, the causative pathogen could be identified in at least 1 fecal sample collected 3 days prior to LOS onset in 92% of the fecal samples, whereas these pathogens were present in 33% of the control samples (P = .004). Overall, LOS (expect CoNS) could be predicted 1 day prior to clinical onset with an area under the curve of 0.78. CONCLUSIONS: Profound preclinical microbial alterations underline that gut microbiota is involved in the pathogenesis of LOS and has the potential as an early noninvasive biomarker.

Adaptive group-regularized logistic elastic net regression.

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) $p$-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical-variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.

Circulating metabolites are associated with brain atrophy and white matter hyperintensities.

INTRODUCTION: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH). METHODS: We studied associations of 143 plasma-based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta-analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures. RESULTS: Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH. DISCUSSION: Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials.

Multiparametric functional MRI and 18F-FDG-PET for survival prediction in patients with head and neck squamous cell carcinoma treated with (chemo)radiation.

OBJECTIVES: To assess (I) correlations between diffusion-weighted (DWI), intravoxel incoherent motion (IVIM), dynamic contrast-enhanced (DCE) MRI, and 18F-FDG-PET/CT imaging parameters capturing tumor characteristics and (II) their predictive value of locoregional recurrence-free survival (LRFS) and overall survival (OS) in patients with head and neck squamous cell carcinoma (HNSCC) treated with (chemo)radiotherapy. METHODS: Between 2014 and 2018, patients with histopathologically proven HNSCC, planned for curative (chemo) radiotherapy, were prospectively included. Pretreatment clinical, anatomical, and functional imaging parameters (obtained by DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT) were extracted for primary tumors (PT) and lymph node metastases. Correlations and differences between parameters were assessed. The predictive value of LRFS and OS was assessed, performing univariable, multivariable Cox and CoxBoost regression analyses. RESULTS: In total, 70 patients were included. Significant correlations between 18F-FDG-PET parameters and DWI-/DCE volume parameters were found (r > 0.442, p < 0.002). The combination of HPV (HR = 0.903), intoxications (HR = 1.065), PT ADCGTV (HR = 1.252), Ktrans (HR = 1.223), and Ve (HR = 1.215) was predictive for LRFS (C-index = 0.546; p = 0.023). N-stage (HR = 1.058), HPV positivity (HR = 0.886), hypopharyngeal tumor location (HR = 1.111), ADCGTV (HR = 1.102), ADCmean (HR = 1.137), D* (HR = 0.862), Ktrans (HR = 1.106), Ve (HR = 1.195), SUVmax (HR = 1.094), and TLG (HR = 1.433) were predictive for OS (C-index = 0.664; p = 0.046). CONCLUSIONS: Functional imaging parameters, performing DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT, yielded complementary value in capturing tumor characteristics. More specific, intoxications, HPV-negative status, large tumor volume-related parameters, high permeability (Ktrans), and high extravascular extracellular space (Ve) parameters were predictive for adverse locoregional recurrence-free survival and adverse overall survival. Low cellularity (high ADC) and high metabolism (high SUV) were additionally predictive for decreased overall survival. These different predictive factors added to estimated locoregional and overall survival. KEY POINTS: • Parameters of DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT were able to capture complementary tumor characteristics. • Multivariable analysis revealed that intoxications, HPV negativity, large tumor volume and high vascular permeability (Ktrans), and extravascular extracellular space (Ve) were complementary predictive for locoregional recurrence. • In addition to predictive parameters for locoregional recurrence, also high cellularity (low ADC) and high metabolism (high SUV) were complementary predictive for overall survival.

Annual malignant transformation rate of oral leukoplakia remains consistent: A long-term follow-up study.

OBJECTIVES: Numerous clinical and histopathological characteristics have been associated with malignant transformation (MT) of oral leukoplakia (OL), including classic and differentiated epithelial dysplasia, but MT predictions remain suboptimal. The objective of this study was to determine the annual MT rate of OL and to identify clinicopathological risk factors associated with MT. PATIENTS AND METHODS: 170 patients with OL were included in this retrospective cohort study, 117 females and 53 males. Follow-up ranged from 12 to 219 months (median 54). The analyzed variables included age, gender, smoking habits, clinical presentation, subsite, size and treatment. In a subgroup of 140 patients, histopathological diagnoses were reviewed with regard to the presence of dysplasia, discerning both classic dysplasia and differentiated dysplasia. RESULTS: MT occurred in 23% of the patients, resulting in an annual MT rate of 4.9% (95% CI: 3.5 - 6.6) which remained consistent. High-risk subsite (tongue and floor of mouth) was the only clinical predictor for MT (Hazard Ratio = 2.7, 95% CI: 1.3 - 5.5, p = 0.007). In 140 patients, classic dysplasia (Hazard Ratio = 7.2, 95% CI: 1.6 - 33.1, p = 0.012) and differentiated dysplasia (Hazard Ratio = 6.6, 95% CI: 1.2 - 25.4, p = 0.026) were predictors for MT. Binary grading between dysplasia and no dysplasia was significant for predicting MT (Hazard Ratio = 6.4, 95% CI: 1.5 - 27.5, p = 0.013). CONCLUSION: Since annual MT rate of OL remains stable during follow-up, regular long-term or even life-long follow-up is advocated. Specific oral subsites and epithelial dysplasia are predictors for MT of OL.

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma.

BACKGROUND: Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. METHODS: Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome. RESULTS: Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). CONCLUSIONS: Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. TRIAL REGISTRATION: Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.

Network Analysis of the Participation and Activity Inventory for Children and Youth (PAI-CY) 7-12 Years with Visual Impairment.

Purpose: Children with visual impairment often experience more difficulties regarding participation compared to sighted peers. The Participation and Activity Inventory for Children and Youth (PAI-CY) has recently been developed to assess their participation needs. A novel application in the field of questionnaires is the use of network analysis to explore interrelations between items in order to capture their complex interactions as a reflection of the overall construct of measurement. This study aimed to apply network modeling for the PAI-CY 7-12 from the perspectives of children and their parents. Methods: Children and their parents (n = 195) completed the 55-item PAI-CY via face-to-face interviews and a web-based survey, respectively. Internal consistency, test-retest reliability, and concordance between children and parents were investigated. Two networks were created, along with visualizations of shared and differential connections between children and parents. Results: Eight items were deleted. Network structures were dissimilar; for children, connections evolved around social contacts and school items, whereas for parents, mobility, leisure time, acceptance, self-reliance, and communication items prevailed. In the children's network, playing imaginary games, inviting a friend to play at home, and estimating the distance from others were most connected to other items. Conclusions: This study uniquely identifies connections between items of the PAI-CY 7-12, highlighting the different perspectives parents and children have on what defines participation, possibly implying that they perceive the relevance of various rehabilitation programs differently. Translational Relevance: Rehabilitation programs aimed at improving the most connected items might positively affect other items in the network, possibly improving children's participation.

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.

OBJECTIVES: Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND METHODS: Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). RESULTS: In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). CONCLUSIONS: MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

Cross-cultural validation of the Functional Vision Questionnaire for Children and Young People (FVQ_CYP) with visual impairment in the Dutch population: challenges and opportunities.

BACKGROUND: To assess cross-cultural validity between Dutch and English versions of the FVQ_CYP, a patient-reported outcome measure developed in the United Kingdom (UK) for children and adolescents with (severe) visual impairment or blindness (VI for brevity) to measure functional vision. METHODS: The 36-item FVQ_CYP was translated and adapted into Dutch using standard guidelines. The questionnaire was administered to Dutch children and adolescents aged 7-17 years (N = 253) with impaired vision (no restrictions regarding acuity). Data were compared to existing UK data of children and adolescents aged 10-15 years (N = 91) with VI (acuity LogMar worse than 0.48). As with the original UK FVQ_CYP validation, a rating scale model (RSM) was applied to the Dutch data. RESULTS: Minor adaptations were needed in translation-rounds. Significant differences in item responses were found between the Dutch and UK data. Item response theory assumptions were met, but fit to the RSM was unsatisfactory. Therefore, psychometric properties of the Dutch FVQ_CYP were analysed irrespective of the original model and criteria used. A graded response model led to the removal of 12 items due to missing data, low information, overlapping content and limited relevance to Dutch children. Fit indices for the remaining 24 items were adequate. CONCLUSIONS: Differences in population characteristics, distribution of responses, non-invariance at the model level and small sample sizes challenged the cross-cultural validation process. However, the Dutch adapted FVQ_CYP showed high measurement precision and broad coverage of items measuring children's functional vision. The underlying reasons for differences between countries in instrument performance are discussed with implications for future studies.

Voxelwise statistical methods to localize practice variation in brain tumor surgery.

PURPOSE: During resections of brain tumors, neurosurgeons have to weigh the risk between residual tumor and damage to brain functions. Different perspectives on these risks result in practice variation. We present statistical methods to localize differences in extent of resection between institutions which should enable to reveal brain regions affected by such practice variation. METHODS: Synthetic data were generated by simulating spheres for brain, tumors, resection cavities, and an effect region in which a likelihood of surgical avoidance could be varied between institutions. Three statistical methods were investigated: a non-parametric permutation based approach, Fisher's exact test, and a full Bayesian Markov chain Monte Carlo (MCMC) model. For all three methods the false discovery rate (FDR) was determined as a function of the cut-off value for the q-value or the highest density interval, and receiver operating characteristic and precision recall curves were created. Sensitivity to variations in the parameters of the synthetic model were investigated. Finally, all these methods were applied to retrospectively collected data of 77 brain tumor resections in two academic hospitals. RESULTS: Fisher's method provided an accurate estimation of observed FDR in the synthetic data, whereas the permutation approach was too liberal and underestimated FDR. AUC values were similar for Fisher and Bayes methods, and superior to the permutation approach. Fisher's method deteriorated and became too liberal for reduced tumor size, a smaller size of the effect region, a lower overall extent of resection, fewer patients per cohort, and a smaller discrepancy in surgical avoidance probabilities between the different surgical practices. In the retrospective patient data, all three methods identified a similar effect region, with lower estimated FDR in Fisher's method than using the permutation method. CONCLUSIONS: Differences in surgical practice may be detected using voxel statistics. Fisher's test provides a fast method to localize differences but could underestimate true FDR. Bayesian MCMC is more flexible and easily extendable, and leads to similar results, but at increased computational cost.