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Aufbau: Acoustic Radiation Force Impulse (ARFI)- Bildgebung ist eine auf der konventionellen Ultraschall- Bildgebung basierende Elastographie- Methode, die die quantitative Messung der Festigkeit und Elastizität von Gewebe ermöglicht. Das Ziel der vorliegenden Studie war es, ARFI- Bildgebung für die Differenzierung von Schilddrüsenknoten zu evaluieren und mit der bereits gut erprobten qualitativen Messmethode der Real-Time Elastographie (RTE) zu vergleichen.
Material und Methoden: ARFI- Bildgebung basiert auf der Aussendung von akustischen Impulsen in Gewebe, wodurch lokale Gewebeverschiebungen hervorgerufen werden. Die dabei entstehenden Transversalwellen wiederum werden über auf Korrelation basierende Methoden mittels Ultraschallwellen detektiert und in m/s angegeben. Einschlusskriterien der Studie waren: Knoten ≥ 5 mm sowie eine zytologische/histologische Aufarbeitung. Alle Patienten erhielten eine konventionelle Ultraschall- Untersuchung, eine Real-Time Elastographie sowie eine ARFI- Bildgebung.
Ergebnisse: Es standen 158 Knoten aus 138 Patienten zur Analyse zur Verfügung. 137 Knoten erbrachten bei der zytologischen/histologischen Aufarbeitung ein benignes Ergebnis, 21 Knoten hingegen wurden als maligne eingestuft. Die mittlere Geschwindigkeit der Messungen der ARFI- Bildgebung in gesundem Schilddrüsengewebe betrug 1,76 m/s, in benignen Knoten 1,90 m/s und in malignen Knoten 2,69 m/s. Es konnte kein signifikanter Unterschied der mittleren Geschwindigkeit zwischen gesundem Schilddrüsengewebe und benignen Knoten ermitteltet werden, wohingegen ein signifikanter Unterschied zwischen malignen Knoten und gesundem Schilddrüsengewebe (p= 0,0019) einerseits und benignen Schilddrüsenknoten (p=0,0039) andererseits bestand. Für die diagnostische Genauigkeit bei der Diagnose von malignen Schilddrüsenknoten konnte kein signifikanter Unterschied zwischen RTE und ARFI- Bildgebung festgestellt werden (0,74 vs. 0,69, p=0,54). Die Kombination von RTE und ARFI- Bildgebung erhöhte die Spezifität bei der Diagnose von malignen Schilddrüsenknoten von 72% (nur RTE) auf 92% (kombiniert).
Schlussfolgerungen: ARFI- Bildgebung kann als zusätzliche Methode bei der diagnostischen Aufarbeitung von Schilddrüsenknoten genutzt werden und liefert dabei einen hohen negativen prädiktiven Wert sowie vergleichbare Ergebnisse wie die RTE.
Background: Acoustic Radiation Force Impulse (ARFI)-imaging is an ultrasound-based elastography method enabling quantitative measurement of tissue stiffness. The aim of the present study was to evaluate sensitivity and specificity of ARFI-imaging for differentiation of thyroid nodules and to compare it to the well evaluated qualitative real-time elastography (RTE).
Methods: ARFI-imaging involves the mechanical excitation of tissue using acoustic pulses to generate localized displacements resulting in shear-wave propagation which is tracked using correlation-based methods and recorded in m/s. Inclusion criteria were: nodules $5 mm, and cytological/histological assessment. All patients received conventional ultrasound, real-time elastography (RTE) and ARFI-imaging.
Results: One-hundred-fifty-eight nodules in 138 patients were available for analysis. One-hundred-thirty-seven nodules were benign on cytology/histology, and twenty-one nodules were malignant. The median velocity of ARFI-imaging in the healthy thyroid tissue, as well as in benign and malignant thyroid nodules was 1.76 m/s, 1.90 m/s, and 2.69 m/s, respectively. While no significant difference in median velocity was found between healthy thyroid tissue and benign thyroid nodules, a significant difference was found between malignant thyroid nodules on the one hand and healthy thyroid tissue (p = 0.0019) or benign thyroid nodules (p = 0.0039) on the other hand. No significant difference of diagnostic accuracy for the diagnosis of malignant thyroid nodules was found between RTE and ARFI-imaging (0.74 vs. 0.69, p = 0.54). The combination of RTE with ARFI did not improve diagnostic accuracy.
Conclusions: ARFI can be used as an additional tool in the diagnostic work up of thyroid nodules with high negative predictive value and comparable results to RTE.
Background: Thyroid Imaging Reporting and Data System (TIRADS) was developed to improve patient management and cost-effectiveness by avoiding unnecessary fine needle aspiration biopsy (FNAB) in patients with thyroid nodules. However, its clinical use is still very limited. Strain elastography (SE) enables the determination of tissue elasticity and has shown promising results for the differentiation of thyroid nodules.
Methods: The aim of the present study was to evaluate the interobserver agreement (IA) of TIRADS developed by Horvath et al. and SE. Three blinded observers independently scored stored images of TIRADS and SE in 114 thyroid nodules (114 patients). Cytology and/or histology was available for all benign (n = 99) and histology for all malignant nodules (n = 15).
Results: The IA between the 3 observers was only fair for TIRADS categories 2–5 (Coheńs kappa = 0.27,p = 0.000001) and TIRADS categories 2/3 versus 4/5 (ck = 0.25,p = 0.0020). The IA was substantial for SE scores 1–4 (ck = 0.66,p<0.000001) and very good for SE scores 1/2 versus 3/4 (ck = 0.81,p<0.000001). 92–100% of patients with TIRADS-2 had benign lesions, while 28–42% with TIRADS-5 had malignant cytology/histology. The negative-predictive-value (NPV) was 92–100% for TIRADS using TIRADS-categories 4&5 and 96–98% for SE using score ES-3&4 for the diagnosis of malignancy, respectively. However, only 11–42% of nodules were in TIRADS-categories 2&3, as compared to 58–60% with ES-1&2.
Conclusions: IA of TIRADS developed by Horvath et al. is only fair. TIRADS and SE have high NPV for excluding malignancy in the diagnostic work-up of thyroid nodules.
Background: About 2000 children and adolescents under the age of 18 are diagnosed with cancer each year in Germany. Because of current medical treatment methods, a high survival rate can be reached for many types of the disease. Nevertheless, patients face a number of long-term effects related to the treatment. As a result, physical and psychological consequences have increasingly become the focus of research in recent years. Social dimensions of health have received little attention in health services research in oncology so far. Yet, there are no robust results that allow an estimation of whether and to what extent the disease and treatment impair the participation of children and adolescents and which factors mediate this effect. Social participation is of great importance especially because interactions with peers and experiences in different areas of life are essential for the development of children and adolescents.
Methods: Data are collected in a longitudinal, prospective, observational multicenter study. For this purpose, all patients and their parents who are being treated for cancer in one of the participating clinics throughout Germany will be interviewed within the first month after diagnosis (t1), after completion of intensive treatment (t2) and half a year after the end of intensive treatment (t3) using standardized questionnaires. Analysis will be done by descriptive and multivariate methods.
Discussion: The results can be used to identify children and adolescents in high-risk situations at an early stage in order to be able to initiate interventions tailored to the needs. Such tailored interventions will finally reduce the risk of impairments in the participation of children and adolescents and increase quality of life.
Trial registration: ClinicalTrials.gov: NCT04101123.
Background: The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demanded the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Main body: We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected in a consensus-based process by working groups of medical experts from the respective specialty to ensure that the contents are aligned with the research needs of the specialty. The selected data elements were mapped to international standardized vocabularies and data exchange specifications were created using HL7 FHIR profiles on the appropriate resources. All steps were performed in close interdisciplinary collaboration between medical domain experts, medical information scientists and FHIR developers. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process. In that way, we defined dataset specifications for a total number of 23 (immunization), 59 (pediatrics), and 50 (cardiology) data elements that augment the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions: We here present extension modules for the GECCO core dataset that contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology. These extension modules were defined in an interdisciplinary, iterative, consensus-based approach that may serve as a blueprint for the development of further dataset definitions and GECCO extension modules. The here developed GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.
Background The COVID-19 pandemic has spurred large-scale, inter-institutional research efforts. To enable these efforts, researchers must agree on dataset definitions that not only cover all elements relevant to the respective medical specialty but that are also syntactically and semantically interoperable. Following such an effort, the German Corona Consensus (GECCO) dataset has been developed previously as a harmonized, interoperable collection of the most relevant data elements for COVID-19-related patient research. As GECCO has been developed as a compact core dataset across all medical fields, the focused research within particular medical domains demands the definition of extension modules that include those data elements that are most relevant to the research performed in these individual medical specialties.
Objective To (i) specify a workflow for the development of interoperable dataset definitions that involves a close collaboration between medical experts and information scientists and to (ii) apply the workflow to develop dataset definitions that include data elements most relevant to COVID-19-related patient research in immunization, pediatrics, and cardiology.
Methods We developed a workflow to create dataset definitions that are (i) content-wise as relevant as possible to a specific field of study and (ii) universally usable across computer systems, institutions, and countries, i.e., interoperable. We then gathered medical experts from three specialties (immunization, pediatrics, and cardiology) to the select data elements most relevant to COVID-19-related patient research in the respective specialty. We mapped the data elements to international standardized vocabularies and created data exchange specifications using HL7 FHIR. All steps were performed in close interdisciplinary collaboration between medical domain experts and medical information scientists. The profiles and vocabulary mappings were syntactically and semantically validated in a two-stage process.
Results We created GECCO extension modules for the immunization, pediatrics, and cardiology domains with respect to the pandemic requests. The data elements included in each of these modules were selected according to the here developed consensus-based workflow by medical experts from the respective specialty to ensure that the contents are aligned with the respective research needs. We defined dataset specifications for a total number of 48 (immunization), 150 (pediatrics), and 52 (cardiology) data elements that complement the GECCO core dataset. We created and published implementation guides and example implementations as well as dataset annotations for each extension module.
Conclusions These here presented GECCO extension modules, which contain data elements most relevant to COVID-19-related patient research in immunization, pediatrics and cardiology, were defined in an interdisciplinary, iterative, consensus-based workflow that may serve as a blueprint for the development of further dataset definitions. The GECCO extension modules provide a standardized and harmonized definition of specialty-related datasets that can help to enable inter-institutional and cross-country COVID-19 research in these specialties.