Consensus Dataset Research Articles

OpenEHR-to-FHIR: Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO).

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named "openEHR-to-FHIR" that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles. The tool provides a REST web service for ad hoc conversion of openEHR compositions to FHIR profiles.

Assessment of glomerular morphological patterns by deep learning algorithms

BackgroundCompilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology.MethodsFor this purpose, we defined nine classes of glomerular morphological patterns and trained twelve convolutional neuronal network (CNN) models on these. The two-step training process was done on a first dataset defined by an expert nephropathologist (12,253 images) and a second consensus dataset (11,142 images) defined by three experts in the field.ResultsThe efficacy of CNN training was evaluated using another set with 180 consensus images, showing convincingly good classification results (kappa-values 0.838–0.938).Furthermore, we elucidated the image areas decisive for CNN-based decision making by class activation maps. Finally, we demonstrated that the algorithm could decipher glomerular disease patterns coinciding in a single glomerulus (e.g. necrosis along with mesangial and endocapillary hypercellularity).ConclusionsIn summary, our model, focusing on glomerular lesions detectable by conventional microscopy, is the first sui generis to deploy deep learning as a reliable and promising tool in recognition of even discrete and/or overlapping morphological changes. Our results provide a stimulus for ongoing projects that integrate further input levels next to morphology (such as immunohistochemistry, electron microscopy, and clinical information) to develop a novel tool applicable for routine diagnostic nephropathology.

Common data elements and features of brucellosis health information management system

IntroductionA key step in constructing any health information management system (HIMS) is to decide on a set of minimal yet comprehensive data items. The consensus dataset would be homogenous between healthcare settings and can pave the way for scientific collaborations. Iran is the fourth endemic country for brucellosis in the world. Despite its huge burden on society, the economy, and the environment, there is no agreed-upon minimum data set (MDS) for reporting this disease, and the data collected are rarely homogenous or directly comparable. ObjectiveTo establish the brucellosis MDS that may enable homogeneity in data collection, data reporting, and data exchange among various HIMSs. MethodsA two-step process, including an extensive literature search and a two-round Delphi survey, was performed to foster consensus about the required data items. The collected data were analyzed using SPSS V22 (SPSS Inc., Chicago, IL). ResultsThe final MDS platform of our study contained 134 items divided into five main categories of administrative information, epidemiology, diagnosis investigation, complications, and signs and symptoms. ConclusionThis study provided a practical MDS for brucellosis that can help collect unified and comprehensive data for electronic health record systems (EHRs), disease surveillance, and registries, and easily integrate them with other HIMSs. The developed MDS can promote the collaboration of policy-makers, healthcare providers, and researchers to prevent, control, and manage brucellosis.

New insights into the dispersion history and adaptive evolution of taxon Aegilops tauschii in China

Aegilops tauschii, the wild progenitor of wheat D-genome and a valuable germplasm for wheat improvement, has a wide natural distribution from eastern Turkey to China. However, the phylogenetic relationship and dispersion history of Ae. tauschii in China has not been scientifically clarified. In this study, we genotyped 208 accessions (with 104 in China) using ddRAD sequencing and 55K SNP array, and classified the population into six sublineages. Three possible spreading routes or events were identified, resulting in specific distribution patterns, with four sublineages found in Xinjiang, one in Qinghai, two in Shaanxi and one in Henan. We also established the correlation of SNP-based, karyotype-based and spike-morphology-based techniques to demonstrate the internal classification of Ae. tauschii, and developed consensus dataset with 1245 putative accessions by merging data previously published. Our analysis suggested that eight inter-lineage accessions could be assigned to the putative Lineage 3 and these accessions would help to conserve the genetic diversity of the species. By developing the consensus phylogenetic relationships of Ae. tauschii, our work validated the hypothesis on the dispersal history of Ae. tauschii in China, and contributed to the efficient and comprehensive germplasm-mining of the species.

Evaluating Suitability of SNOMED CT in Structured Searches for COVID-19 Studies.

Studies investigating the suitability of SNOMED CT in COVID-19 datasets are still scarce. The purpose of this study was to evaluate the suitability of SNOMED CT for structured searches of COVID-19 studies, using the German Corona Consensus Dataset (GECCO) as example. Suitability of the international standard SNOMED CT was measured with the scoring system ISO/TS 21564, and intercoder reliability of two independent mapping specialists was evaluated. The resulting analysis showed that the majority of data items had either a complete or partial equivalent in SNOMED CT (complete equivalent: 141 items; partial equivalent: 63 items; no equivalent: 1 item). Intercoder reliability was moderate, possibly due to non-establishment of mapping rules and high percentage (74%) of different but similar concepts among the 86 non-equal chosen concepts. The study shows that SNOMED CT can be utilized for COVID-19 cohort browsing. However, further studies investigating mapping rules and further international terminologies are necessary.

Colorectal cancer surgery in Serbia 2010-2014: An evaluation of a multicenter registry

Background/Aim. Surgical registries and databases are especially valuable in monitoring the performances in cancer treatment and detecting potential problems. For Serbian patients with colorectal cancer (CRC), data regarding the treatment received, the factors that may impact the outcome, and whether or not treatment is successful and appropriate are not currently captured. The aim of this study was to establish a collection of a consensus dataset capturing surgical treatment of CRC at multiple public hospitals across Serbia and estimate outcome in CRC patients subjected to surgical treatment in the period 2010?2014. Methods. The study encompassed all 52 public CRC surgical units in Serbia. Numerical data on all patients who underwent operative CRC resection were included. An electronic database was created and overseen by the First Surgical Clinic of the Clinical Center of Serbia, Belgrade. Data were collected independently using a specifically designed standardized questionnaire, including the number of operated patients, localization of the primary tumor, type of surgical intervention, type and urgency of surgical intervention, and postoperative mortality. Results. A total number of 22,037 colorectal surgical procedures was performed in Serbia in the period 2010?2014 (approximately 4,400 per year). It was shown that 78.5% of the total number of procedures were elective and 21.5% were emergency. The most common cause of emergency surgeries was ileus (3,618 cases, 76.4%), while the less common causes were perforation (899 cases, 18.9%) and bleeding (216 cases, 4.5%). Postoperative mortality during the study period expressed as a percentage of all interventions for CRC was 2.8% on average with a slight increasing tendency. At the end of the study period, 127 doctors were educated for performing colonoscopy. Conclusion. The main outcome of this study was the establishment of the necessary preconditions for the multicenter data collection involving large numbers of CRC patients. The study supported the premise that the development of the national database for surgical treatment of CRC is achievable and could provide valuable insight into the routine surgical management of CRC in Serbia, creating a significant resource for further research.

The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond

BackgroundThe current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.MethodsBased on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.ResultsA core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.ConclusionGECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Evaluation of a Model-based Dose Calculation Algorithm (Acuros) for Intraosseous High Dose Rate Brachytherapy (HDR) With Intramedullary Nailing for Bone Metastases

A standard practice for patients with long bone metastases after intramedullary nailing is treatment with postoperative two-dimensional external beam radiotherapy (2D EBRT), delivered to the length of the implanted hardware. Our goal is to use intraoperative, intraosseous HDR brachytherapy delivered prior to intramedullary nailing to deliver an ablative dose to a limited volume immediately surrounding the tumor. This work evaluates the dosimetric effects resulting from treatment through a metal intramedullary drill bit, which in turn, dramatically reduces the volume potentially harboring subclinical contamination from the drill path and subsequent intramedullary nail placement. A model-based dose calculation engine was commissioned using the IROC data-set. Additionally, the accuracy of dose calculations was verified at several distances from the source in water using a custom 3D-printed phantom with a series of concentric rings holding Gafchromic-EBT3 film. A separate set of films from the same batch was calibrated with a 6MV-LINAC beam. Treatment plans were generated for two different source configurations (a single dwell position and a linear array of dwell position) with a standard 4.7Fr catheter surrounded by the stainless-steel drill bit. Measurements were performed at 1cm, 2cm, 3cm, 4cm, and 5cm from the active source and compared with dose calculations from the TG-43 and Acuros algorithms. Without the drill bit surrounding the catheter, measurements were in good agreement with the dose calculated by the treatment planning system (<1.0% on average). The maximum dose deviation (1.5%) was observed at 1cm from the source, as expected due to the steep dose gradient. With the stainless-steel drill bit surrounding the catheter, the Acuros dose calculations agreed with film measurements (<1.5% on average) with the largest difference at 1cm (<2.5%). Using TG-43 for dose calculation, we observed larger deviations from measurements (average 6.3%). At larger distances, 5cm, the agreement is within <1% of the calculated dose by Acuros. A model-based dose calculation engine was commissioned using a consensus dataset. This dose engine was secondarily verified with film measurements about a linear catheter surrounded by a stainless-steel intramedullary drill bit for the purpose of facilitating accurate dose calculations for intraoperative HDR brachytherapy of long bone metastases.

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020. The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means. The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95% CI, 0.704-0.729) and for radiology residents was 0.720 (95% CI, 0.709-0.732) (P = .66), while the PPV was 0.730 (95% CI, 0.718-0.742) for the AI algorithm and 0.682 (95% CI, 0.670-0.694) for the radiology residents (P < .001), and specificity was 0.980 (95% CI, 0.980-0.981) for the AI algorithm and 0.973 (95% CI, 0.971-0.974) for the radiology residents (P < .001). These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full-fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.

A National Spinal Muscular Atrophy Registry for Real-World Evidence.

Spinal muscular atrophy (SMA) is a devastating rare disease that affects individuals regardless of ethnicity, gender, and age. The first-approved disease-modifying therapy for SMA, nusinursen, was approved by Health Canada, as well as by American and European regulatory agencies following positive clinical trial outcomes. The trials were conducted in a narrow pediatric population defined by age, severity, and genotype. Broad approval of therapy necessitates close follow-up of potential rare adverse events and effectiveness in the larger real-world population. The Canadian Neuromuscular Disease Registry (CNDR) undertook an iterative multi-stakeholder process to expand the existing SMA dataset to capture items relevant to patient outcomes in a post-marketing environment. The CNDR SMA expanded registry is a longitudinal, prospective, observational study of patients with SMA in Canada designed to evaluate the safety and effectiveness of novel therapies and provide practical information unattainable in trials. The consensus expanded dataset includes items that address therapy effectiveness and safety and is collected in a multicenter, prospective, observational study, including SMA patients regardless of therapeutic status. The expanded dataset is aligned with global datasets to facilitate collaboration. Additionally, consensus dataset development aimed to standardize appropriate outcome measures across the network and broader Canadian community. Prospective outcome studies, data use, and analyses are independent of the funding partner. Prospective outcome data collected will provide results on safety and effectiveness in a post-therapy approval era. These data are essential to inform improvements in care and access to therapy for all SMA patients.

Detection of 2019-nCoV in Saliva and Characterization of Oral Symptoms in COVID-19 Patients

Background: Coronavirus disease 19 (COVID-19) caused by a novel coronavirus (2019-nCoV) has now been classified as a pandemic by the World Health Organization (WHO). It has been reported that 2019-nCoV could be detected in various human secreta and excreta such as sputum, faeces and urine. However, the detection of 2019-nCoV in saliva has not been reported to date. To provide a more comprehensive understanding of the effects of 2019-nCoV on oral health and possible saliva transmission, the expression of the putative ACE2 (angiotensin-converting enzyme II) receptor of 2019-nCoV in salivary gland epithelial cells were analyzed in bulk RNA-seq profiles from public databases. Additionally, to evaluate the oral health status of COVID-19 patients, a questionnaire survey on oral-related symptoms of COVID-19 patients was performed.Methods: To analyze ACE2 expression in salivary glands, bulk RNA-seq profiles from four public datasets including GTEx dataset, HPA dataset, FANTOM5 dataset and Consensus dataset were collected. Altogether, 31 COVID-19 patients were recruited, whose 2019-nCoV nucleic acid detection remained positive before or on the day of sample collection. Of these 31 patients, there were 4 critically-ill cases whereby oropharyngeal swabs were tested positive. Saliva was collected from the opening of the salivary gland canal of cleaned oral cavity to avoid contamination by other secretions from the respiratory tract. At the same time, oropharyngeal swabs were also collected. Then the presence of 2019-nCoV nucleic acids in saliva was detected by RT-PCR. Additionally, a questionnaire survey on various oral symptoms such as dry mouth and amblygeustia was also carried out on COVID-19 patients.Findings: ACE2 expression was present at detectable levels in the salivary glands. Interestingly, we found 4 cases with positive detection of 2019-nCoV nucleic acid in saliva. It is worth noting that 3 cases with positive detection in saliva were critically-ill patients on ventilator support, thus implying a high potential (75%) for detection of 2019-nCoV in the saliva of critically-ill patients. At the same time, the two major oral-related symptoms, dry mouth (46.3%) and amblygeustia (47.2%), were manifested by a relatively high proportion of 108 COVID-19 patients.Interpretation: This study thus confirms the expression of ACE2 in the salivary glands, and demonstrates the possibility of 2019-nCoV infection of the salivary glands. The reason that the positive saliva detection rate was as high as 75% (3/4) in critically-ill patients in our study might be due to virus invasion caused by high viral loads or destroyed salivary glands at the late stage of the disease. The emergence of viral particles in the saliva might be an indication that the disease condition of the patient has deteriorated and progressed to the terminal stage. Hence, saliva may be a new source of diagnostic specimens for critically-ill patients, since it can be easily collected without any invasive procedures.The two major oral-related symptoms, dry mouth and amblygeustia, were manifested by a relatively high proportion of COVID-19 patients suggesting that oral symptoms can also be considered as initial symptoms of COVID-19 infection.

A consensus dataset for the management of conjunctival melanoma in the multidisciplinary setting

AbstractImportanceA key tenet of best practice in modern oncology is multidisciplinary decision making. Currently there are no specific multidisciplinary guidelines for the management of conjunctival melanoma.BackgroundConjunctival melanoma is a rare disease which can be associated with significant morbidity and in some cases, mortality. Multidisciplinary care has been shown to improve outcomes for oncology patients. The aim of the current report is to determine a consensus dataset for the multidisciplinary management of conjunctival melanoma by surveying experts involved in the management of conjunctival melanoma.Design and participantsHealth professionals (n=86) who participate in a multi‐institutional ocular oncology multidisciplinary team (MDT) meetings at the Kinghorn Cancer Centre in Sydney, NSW were invited to participate in this studyMethodsA modified Delphi method with two rounds of online survey and one oral consensus round was carried out. Data fields relating to demographic, clinical, pathology and follow up information were collated from the American Joint Committees on Cancer TNM Staging Manual (8t edition) and Cancer Australia Government recommendations.Main Outcome MeasuresFields for inclusion in a consensus dataset.ResultsIn round one 43 of 86 participants (50%) responded. Nine out of 71 questions did not achieve 80% consensus and were added to round two. 32 professionals out of 43 (74%) responded to round two. None of the nine questions in this round achieved 80% consensus, and all nine were added to round three, the oral consensus round. 16 professionals out of 32 (50%) were present in this last round. The process generated a final dataset consisting of six demographic items), 31 clinical items, 32 pathology items and four items. In total, 75 fields were considered essential for multidisciplinary management of conjunctival melanoma.Conclusions and RelevanceUsing recognised methodology, this consensus dataset represents a working document and template for optimising patient care and multidisciplinary decision making for conjunctival melanoma. It can be used as a tool for evaluating and benchmarking MDT performance.

Integration of unsupervised and supervised machine learning algorithms for credit risk assessment

For the sake of credit risk assessment, credit scoring has become a critical tool to discriminate “bad” applicants from “good” applicants for financial institutions. Accordingly, a wide range of supervised machine learning algorithms have been successfully applied to credit scoring; however, integration of unsupervised learning with supervised learning in this field has drawn little consideration. In this work, we propose a combination strategy of integrating unsupervised learning with supervised learning for credit risk assessment. The difference between our work and other previous work on unsupervised integration is that we apply unsupervised learning techniques at two different stages: the consensus stage and dataset clustering stage. Comparisons of model performance are performed based on three credit datasets in four groups: individual models, individual models + consensus model, clustering + individual models, clustering + individual models + consensus model. As a result, integration at either the consensus stage or dataset clustering stage is effective on improving the performance of credit scoring models. Moreover, the combination of the two stages achieves the best performance, thereby confirming the superiority of the proposed integration of unsupervised and supervised machine learning algorithms, which boost our confidence that this strategy can be extended to many other credit datasets from financial institutions.

A global growth-form database for 143,616 vascular plant species.

For the majority of plant species in the world, we know little about their functional ecology, and not even one of the most basic traits-the species' growth habit. To fill the gap in availability of compiled plant growth-form data, we have assembled what is, to our knowledge, the largest global database on growth-form as a plant trait. We have, with extensive error checking and data synthesis, assembled a growth-form database from 163 data sources for 143,616 vascular plant species from 445 different plant families. This is 38.6% of the currently accepted vascular plant diversity. For our database, we have chosen seven categories to cover the majority of the diversity in plant growth forms: aquatic plants, epiphytes, hemiepiphytes, climbing plants, parasitic plants, holo-mycoheterotrophs, and freestanding plants. These categories were used because we were able to reconcile the wealth of existing definitions and types of growth-form information available globally to them clearly and unequivocally, and because they are complementary with existing databases. Plants in the database were designated into a category if their adult growth form fit the criterion. We make available two databases: first, the complete data set, including species for which there is currently conflicting information, and second, a consensus data set, where all available information supports one categorization. Of the plant species for which we found information, 103,138 (72%) are freestanding, 21,110 (15%) are epiphytes, and 4,046 (3%) are parasites. Our growth-form data can be used to produce useful summary statistics by clade. For example, current data suggests that half of pteridophytes are epiphytic, that all hemiepiphytes are eudicots, and that there are no parasitic monocots, gymnosperms, or pteridophytes. Growth form is a crucial piece of fundamental plant-trait data with implications for each species' ecology, evolution, and conservation, and thus this data set will be useful for a range of basic and applied questions across these areas of research. No copyright or proprietary restrictions are associated with the use of this data set, other than citation of the present Data Paper. A static version of this dataset is provided as Supporting Information, and a living and updating version of the dataset is available in a GitHub repository.

The impact of inter-unit variations on small field dosimetry correction factors, with application to the CyberKnife system

Small field dosimetry correction factors are usually determined from calculations or measurements using one specific example of a treatment system. The sensitivity of the corrections to inter-unit variation is therefore not evaluated. We propose two methods for this evaluation that could be applied to any system. We use them to assess the variability in for the CyberKnife System caused by design changes between pre-M6 and M6 versions, and to the variability in and resulting from measured beam-data variations across 139 units. We also perform measurements to investigate the differences in reported for microchambers in a CyberKnife-specific study versus TRS-483. The results show that is smaller for the M6 version than pre-M6 versions by 0.4% for a Farmer chamber, and 0.1% for shorter chambers. The presence or absence of a lead filter within the treatment head had no significant impact on . The beam-data analysis showed inter-unit variations in of ±0.8% (2 s.d.) for Farmer chambers and ⩽ ±0.5% for shorter cavities (<10 mm) pre-M6, reducing to 0.4% and 0.2% respectively with M6. Inter-unit variations for microDiamond and microchambers were ⩽ ±1% at 5 mm field size, except for microchambers with axis perpendicular to the beam where this was > ±2%. Differences of up to 9% were confirmed between Output Factors measured using a microchamber and corrected using TRS-483 , and a consensus dataset for the same treatment unit determined using multiple detectors and Monte Carlo simulation. A set of practical recommendations for small field dosimetry with the CyberKnife System is derived from these results.

Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project

The Alzheimer's Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed “consensus calling,” to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.

Rotational Raman-based temperature measurements in a high-velocity, turbulent jet

Spontaneous rotational Raman scattering spectroscopy is used to acquire measurements of the mean and root mean square (rms) temperature fluctuations in turbulent, high-velocity heated jets. Raman spectra in air were obtained across a matrix of radial and axial locations downstream from a 50 mm diameter nozzle operating from subsonic to supersonic conditions over a wide range of temperatures and Mach numbers, in accordance with the Tanna matrix frequently used in jet noise studies. These data were acquired in the hostile, high noise (115 dB) environment of a large scale open air test facility at NASA Glenn Research Center (GRC). Temperature estimates were determined by performing non-linear least squares fitting of the single shot spectra to the theoretical rotational Stokes spectra of N2 and O2. The laser employed in this study was a high energy, long-pulsed, frequency doubled Nd:YAG laser. One thousand single-shot spectra were acquired at each spatial coordinate. Mean temperature and rms temperature variations were calculated at each measurement location. Excellent agreement between the averaged and single-shot temperatures was observed with an accuracy better than 2.5% for temperature, and rms variations in temperature between ±2.2% at 296 K and ±4.5% at 850 K. The mean and normalized rms temperatures measured here were then compared to NASA’s Consensus data set of PIV velocity and turbulence measurements in similar jet flows. The results of this and planned follow-on studies will support NASA GRC’s development of physics-based jet noise prediction, turbulence modeling and aeroacoustic source modeling codes.

Development of a consensus core dataset in juvenile dermatomyositis for clinical use to inform research

ObjectivesThis study aimed to develop consensus on an internationally agreed dataset for juvenile dermatomyositis (JDM), designed for clinical use, to enhance collaborative research and allow integration of data between centres.MethodsA...

Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours.

BackgroundHigh reproducibility of LV mass and volume measurement from cine cardiovascular magnetic resonance (CMR) has been shown within single centers. However, the extent to which contours may vary from center to center, due to different training protocols, is unknown. We aimed to quantify sources of variation between many centers, and provide a multi-center consensus ground truth dataset for benchmarking automated processing tools and facilitating training for new readers in CMR analysis.MethodsSeven independent expert readers, representing seven experienced CMR core laboratories, analyzed fifteen cine CMR data sets in accordance with their standard operating protocols and SCMR guidelines. Consensus contours were generated for each image according to a statistical optimization scheme that maximized contour placement agreement between readers.ResultsReader-consensus agreement was better than inter-reader agreement (end-diastolic volume 14.7 ml vs 15.2–28.4 ml; end-systolic volume 13.2 ml vs 14.0–21.5 ml; LV mass 17.5 g vs 20.2–34.5 g; ejection fraction 4.2 % vs 4.6–7.5 %). Compared with consensus contours, readers were very consistent (small variability across cases within each reader), but bias varied between readers due to differences in contouring protocols at each center. Although larger contour differences were found at the apex and base, the main effect on volume was due to small but consistent differences in the position of the contours in all regions of the LV.ConclusionsA multi-center consensus dataset was established for the purposes of benchmarking and training. Achieving consensus on contour drawing protocol between centers before analysis, or bias correction after analysis, is required when collating multi-center results.

Tissue composition and density impact on the clinical parameters for 125I prostate implants dosimetry

The MCNPX code was used to calculate the TG-43U1 recommended parameters in water and prostate tissue in order to quantify the dosimetric impact in 30 patients treated with 125I prostate implants when replacing the TG-43U1 formalism parameters calculated in water by a prostate-like medium in the planning system (PS) and to evaluate the uncertainties associated with Monte Carlo (MC) calculations. The prostate density was obtained from the CT of 100 patients with prostate cancer. The deviations between our results for water and the TG-43U1 consensus dataset values were −2.6% for prostate V100, −13.0% for V150, and −5.8% for D90; −2.0% for rectum V100, and −5.1% for D0.1; −5.0% for urethra D10, and −5.1% for D30. The same differences between our water and prostate results were all under 0.3%. Uncertainties estimations were up to 2.9% for the gL(r) function, 13.4% for the F(r,θ) function and 7.0% for Λ, mainly due to seed geometry uncertainties. Uncertainties in extracting the TG-43U1 parameters in the MC simulations as well as in the literature comparison are of the same order of magnitude as the differences between dose distributions computed for water and prostate-like medium. The selection of the parameters for the PS should be done carefully, as it may considerably affect the dose distributions. The seeds internal geometry uncertainties are a major limiting factor in the MC parameters deduction.