Citations

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Background: Reliable methods for measuring range of motion is important for hand therapists. Currently, there is no gold standard for the measurement of thumb metacarpophalangeal joint (MCPJ) hyperextension. We hypothesised that visual and goniometric measurements of thumb MCPJ hyperextension vary greater than 10° from radiographic measurements, and between observers. Methods: Twenty-six fresh-frozen hands were measured by a senior orthopaedic resident and fellowship trained hand surgeon. Passive thumb MCPJ hyperextension was measured by visual estimation, goniometry and axis measurement on a lateral thumb radiograph. Raters were blinded to each other’s and their own prior measurements. Descriptive statistics were recorded for measurement type and inter-observer agreement using a two-way intra-class correlation coefficient (ICC). Intra-observer agreement was calculated using concordance correlation coefficient (CCC). Bland–Altman plots identified trends, systemic differences or potential outliers. Results: Mean measurements for both raters were similar for visual estimation and radiographic measurements. Mean goniometric measurements were twice as high for Rater B, and closer to radiographic measurements. For both raters, mean radiographic measurements were 10° greater than the other two methods. For inter-rater agreement, measurements were within 10° most frequently with radiographic measurement, then visual estimates, and least by goniometer measurements. Rater B had better agreement comparing visual and goniometric to radiographic measurements. Conclusions: Radiographic measurement has the best inter-observer agreement and precision for evaluating passive thumb MCPJ hyperextension, especially considering adjunct corrective procedures when performing a soft-tissue basal joint arthroplasty. Rater experience improves precision, but there is still poor agreement between visual estimates and goniometer measurements compared to radiographic measurements, as the former two underestimate hyperextension by 10°. Development of a standard method of clinical measurement is needed to improve reliability.

Abstract Background To protect minors’ future autonomy, professional organizations have historically discouraged returning predictive adult-onset genetic test results and carrier status to children. Recent clinical guidance diverges from this norm, suggesting that when minors have genomic sequencing performed for clinical purposes, parents and children should have the opportunity to learn secondary findings, including for some adult-onset conditions. While parents can currently opt in or out of receiving their child’s secondary findings, the American Society of Human Genetics Workgroup on Pediatric Genetic and Genomic Testing suggests including adolescents in the decision-making process. However, it is not clear what factors young people consider when given the opportunity to learn genetic findings for themselves. We are examining adolescents’, young adults’, and parents’ (if applicable) decisions about learning genomic information for the adolescent. Methods We are enrolling assenting (ages 13–17) adolescents and consenting (ages 18–21) young adults in a prospective genomic screening study to assess the choices they make about receiving individual genomic results. Participants use an online tool to indicate whether they want to learn their personal genetic risk for specific preventable, treatable, and adult-onset conditions, as well as carrier status for autosomal recessive conditions. We are examining 1) how choices differ between adolescent and young adult cohorts (as well as between adolescents/young adults and parents) and 2) decisional conflict and stability across study timepoints. Results are returned based on participants’ choices. Qualitative interviews with a subset of participants explore decisional stability, adolescent/young adult engagement with parents in decision-making, and the impact of learning pathogenic/likely pathogenic and carrier results. Discussion This study explores decision making and decision stability between adolescents and parents (where applicable), as well as the ethical implications and impact of return of clinical-grade genetic research results to adolescents and young adults. The results of this study will contribute empirical evidence to support best practices and guidance on engaging young people in genetic research studies and clinical care that offer return of results. Trial Registration: ClinicalTrials.gov Identifier: NCT04481061. Registered 22 July 2020.

AbstractBackgroundSmall coronary arteries containing stents pose a challenge in CT imaging due to metal‐induced blooming artifact. High spatial resolution imaging capability is as the presence of highly attenuating materials limits noninvasive assessment of luminal patency.PurposeThe purpose of this study was to quantify the effective lumen diameter within coronary stents using a clinical photon‐counting‐detector (PCD) CT in concert with a convolutional neural network (CNN) denoising algorithm, compared to an energy‐integrating‐detector (EID) CT system.MethodsSeven coronary stents of different materials and inner diameters between 3.43 and 4.72 mm were placed in plastic tubes of diameters 3.96–4.87 mm containing 20 mg/mL of iodine solution, mimicking stented contrast‐enhanced coronary arteries. Tubes were placed parallel with or perpendicular to the scanner's z‐axis in an anthropomorphic phantom emulating an average‐sized patient and scanned with a clinical EID‐CT and PCD‐CT. EID scans were performed using our standard coronary computed tomography angiography (cCTA) protocol (120 kV, 180 quality reference mAs). PCD scans were performed using the ultra‐high‐resolution (UHR) mode (120 × 0.2 mm collimation) at 120 kV with tube current adjusted so that CTDIvol was matched to that of EID scans. EID images were reconstructed per our routine clinical protocol (Br40, 0.6 mm thickness), and with the sharpest available kernel (Br69). PCD images were reconstructed at a thickness of 0.6 mm and a dedicated sharp kernel (Br89) which is only possible with the PCD UHR mode. To address increased image noise introduced by the Br89 kernel, an image‐based CNN denoising algorithm was applied to the PCD images of stents scanned parallel to the scanner's z‐axis. Stents were segmented based on full‐width half maximum thresholding and morphological operations, from which effective lumen diameter was calculated and compared to reference sizes measured with a caliper.ResultsSubstantial blooming artifacts were observed on EID Br40 images, resulting in larger stent struts and reduced lumen diameter (effective diameter underestimated by 41% and 47% for parallel and perpendicular orientations, respectively). Blooming artifacts were observed on EID Br69 images with 19% and 31% underestimation of lumen diameter compared to the caliper for parallel and perpendicular scans, respectively. Overall image quality was substantially improved on PCD, with higher spatial resolution and reduced blooming artifacts, resulting in the clearer delineation of stent struts. Effective lumen diameters were underestimated by 9% and 19% relative to the reference for parallel and perpendicular scans, respectively. CNN reduced image noise by about 50% on PCD images without impacting lumen quantification (<0.3% difference).ConclusionThe PCD UHR mode improved in‐stent lumen quantification for all seven stents as compared to EID images due to decreased blooming artifacts. Implementation of CNN denoising algorithms to PCD data substantially improved image quality.

AbstractBackgroundPhoton‐counting‐detector CT (PCD‐CT) enables the production of virtual monoenergetic images (VMIs) at a high spatial resolution (HR) via simultaneous acquisition of multi‐energy data. However, noise levels in these HR VMIs are markedly increased.PurposeTo develop a deep learning technique that utilizes a lower noise VMI as prior information to reduce image noise in HR, PCD‐CT coronary CT angiography (CTA).MethodsCoronary CTA exams of 10 patients were acquired using PCD‐CT (NAEOTOM Alpha, Siemens Healthineers). A prior‐information‐enabled neural network (Pie‐Net) was developed, treating one lower‐noise VMI (e.g., 70 keV) as a prior input and one noisy VMI (e.g., 50 keV or 100 keV) as another. For data preprocessing, noisy VMIs were reconstructed by filtered back‐projection (FBP) and iterative reconstruction (IR), which were then subtracted to generate “noise‐only” images. Spatial decoupling was applied to the noise‐only images to mitigate overfitting and improve randomization. Thicker slice averaging was used for the IR and prior images. The final training inputs for the convolutional neural network (CNN) inside the Pie‐Net consisted of thicker‐slice signal images with the reinsertion of spatially decoupled noise‐only images and the thicker‐slice prior images. The CNN training labels consisted of the corresponding thicker‐slice label images without noise insertion. Pie‐Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy, and compared to a U‐net‐based method that did not include prior information.ResultsPie‐Net provided strong noise reduction, by 95 ± 1% relative to FBP and by 60 ± 8% relative to IR. For HR VMIs at different keV (e.g., 50 keV or 100 keV), Pie‐Net maintained spatial and spectral fidelity. The inclusion of prior information from the PCD‐CT data in the spectral domain was able to improve a robust deep learning‐based denoising performance compared to the U‐net‐based method, which caused some loss of spatial detail and introduced some artifacts.ConclusionThe proposed Pie‐Net achieved substantial noise reduction while preserving HR VMI's spatial and spectral properties.

Pathogenic variants in KMT5B , a lysine methyltransferase, are associated with global developmental delay, macrocephaly, autism, and congenital anomalies (OMIM # 617788). Given the relatively recent discovery of this disorder, it has not been fully characterized. Deep phenotyping of the largest ( n = 43) patient cohort to date identified that hypotonia and congenital heart defects are prominent features that were previously not associated with this syndrome. Both missense variants and putative loss-of-function variants resulted in slow growth in patient-derived cell lines. KMT5B homozygous knockout mice were smaller in size than their wild-type littermates but did not have significantly smaller brains, suggesting relative macrocephaly, also noted as a prominent clinical feature. RNA sequencing of patient lymphoblasts and Kmt5b haploinsufficient mouse brains identified differentially expressed pathways associated with nervous system development and function including axon guidance signaling. Overall, we identified additional pathogenic variants and clinical features in KMT5B -related neurodevelopmental disorder and provide insights into the molecular mechanisms of the disorder using multiple model systems.

ABSTRACT: Background: When adolescents present with symptoms of unexplained weight loss, underweight, or poor appetite, eating disorders (EDs) are commonly on the list of differential diagnoses. However, the relationship of these symptoms to other psychiatric disorders is often less clear. Methods: Using the Rochester Epidemiology Project database, a retrospective cohort study of adolescents (13–18 years) with billing diagnoses of weight loss, underweight, or loss of appetite was conducted between January 2005 and December 2017. Patients who presented with conditions commonly associated with weight loss, underweight, or poor appetite (e.g., cancer) were excluded. This study sought to examine the proportion of patients who received ED and psychiatric diagnoses within 5 years of the index visit and patient characteristics associated with these diagnoses. Results: Of 884 patients diagnosed with symptoms of unexplained weight loss, underweight, or poor appetite, 662 patients (M age = 15.8; SD = 1.6; 66.0% female) met study criteria. Within 5 years of the index visit, the lifetime prevalence of all psychiatric disorders was 70% (n = 461) and of EDs was 21% (n = 141). For both psychiatric disorders and EDs, sex and race were significantly associated with receiving a diagnosis within 5 years. Decrease in body mass index (BMI) percentile was associated with receiving an ED diagnosis, whereas the highest historical BMI percentile was associated with receiving a psychiatric diagnosis. Conclusion: Patients presenting with symptoms of unexplained weight loss, underweight, or poor appetite are at risk not only for EDs but also for other psychiatric disorders that may require further assessment and follow-up.