Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Diagnostic Efficacy of (68)Gallium-PSMA Positron Emission Tomography Compared to Conventional Imaging for Lymph Node Staging of 130 Consecutive Patients with Intermediate to High Risk Prostate Cancer

Currently, the findings of imaging procedures used for detection or staging of prostate cancer depend on morphology of lymph nodes or bone metabolism and do not always meet diagnostic needs. Prostate-specific membrane antigen (PSMA), a transmembrane protein that has considerable overexpression on most prostate cancer cells, has gained increasing interest as a target molecule for imaging. To date, several small compounds for labelling PSMA have been developed and are currently being investigated as imaging probes for PET with the (68)Ga-labelled PSMA inhibitor Glu-NH-CO-NH-Lys(Ahx)-HBED-CC being the most widely studied agent. (68)Ga-PSMA-PET imaging in combination with multiparametric MRI (mpMRI) might provide additional molecular information on cancer localization within the prostate. In patients with primary prostate cancer of intermediate-risk to high-risk, PSMA-based imaging has been reported to improve detection of metastatic disease compared with CT or mpMRI, rendering additional cross-sectional imaging or bone scintigraphy unnecessary. Furthermore, in patients with biochemically recurrent prostate cancer, use of (68)Ga-PSMA-PET imaging has been shown to increase detection of metastatic sites, even at low serum PSA values, compared with conventional imaging or PET examination with different tracers. Thus, although current knowledge is still limited and derived mostly from retrospective series, PSMA-based imaging holds great promise to improve prostate cancer management.

Current use of PSMA-PET in prostate cancer management

Ultrasound is among the most widely used non-invasive imaging modalities in biomedicine, but plays a surprisingly small role in molecular imaging due to a lack of suitable molecular reporters on the nanoscale. Here, we introduce a new class of reporters for ultrasound based on genetically encoded gas nanostructures from microorganisms, including bacteria and archaea. Gas vesicles are gas-filled protein-shelled compartments with typical widths of 45-250 nm and lengths of 100-600 nm that exclude water and are permeable to gas. We show that gas vesicles produce stable ultrasound contrast that is readily detected in vitro and in vivo, that their genetically encoded physical properties enable multiple modes of imaging, and that contrast enhancement through aggregation permits their use as molecular biosensors.

/pdf/biogenic-gas-nanostructures-as-ultrasonic-molecular-1odhzs8uy2.pdf

Biogenic gas nanostructures as ultrasonic molecular reporters

Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

/pdf/federated-deep-learning-for-detecting-covid-19-lung-4lijcrbpng.pdf

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.

https://www.mdpi.com/2077-0383/9/5/1514/pdf

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.

Together with the novel development of combined PET/MRI, the combination of excellent morphological detail, multiparametric functional information, and molecular PET data might lead to a significant improvement in detection and staging of PCa, and thus could help to optimize oncological treatment. 4–6 Here, based on our first clinical results, we describe 68 Ga-PSMA-L PET/MRI as a novel imaging technique for patients with PCa at different disease stages, and discuss potential future applications. Briefly, PET/MRI was carried out on a fully integrated whole-body hybrid PET/MRI system (Siemens Biograph mMR; Siemens Healthcare, Erlangen, Germany). After intravenous injection of 122 ± 17 MBq 68 Ga-PSMA-L, a diagnostic PET-examination of the trunk (from base of skull to the proximal femur) with simultaneous acquisition of coronal T1-weighted and fat saturated axial T2-weighted images was carried out followed by a diagnostic multiparametric MR examination of the pelvis including a 15-min PET scan (mean time from injection to imaging: 51 min, range 45–63 min). To prevent extinction artifacts as a result of urinary excretion of the tracer, furosemide was applied and the bladder was emptied before the start of the examination. Case 1: A 72-year-old patient without evidence of malignancy in two prior biopsies, but highly suspected PCa (PSA 13.9 ng/mL, free-to-total ratio 6.9%, digital rectal examination normal) underwent

https://onlinelibrary.wiley.com/doi/pdf/10.1111/iju.12577

Positron emission tomography/magnetic resonance imaging with 68Gallium-labeled ligand of prostate-specific membrane antigen: promising novel option in prostate cancer imaging?

Introduction M. Schwaiger. Methodology:- 1. Trends in instrumentation J.T. Spinks, J. Jones. 2. Attenuation correction: Practical considerations S.L. Bacharach. 3. Radiopharmaceuticals M.R. Kilbourn. 4. Utility and limitations of (18F)2-deoxy-2-fluoro-D-glucose for the assessment of flux through metabolic pathways in heart muscle: A critical appraisal H. Taegtmeyer. 5. Quantitative evaluation of myocardial perfusion G.D. Hutchins, M. Schwaiger. Myocardial Perfusion Imaging:- 6. Assessment of myocardial perfusion with 13N-Ammonia or 82-Rubidium M. Schwaiger, et al. 7. Assessment of myocardial perfusion with 15-O Water P. Herrero, S.R. Bergmann. 8. 62Cu-PTSM: A generator-based radiopharmaceutical for myocardial perfusion imaging M.A. Green. 9. Comparison of cost-effectiveness of myocardial perfusion imaging versus other approaches: Predictions by a model R.E. Patterson, et al. Assessment of Tissue Viability:- 10. Assessment of blood flow and substrate metabolism in the myocardium of the normal human heart H.R. Schelbert. 11. Comparison of SPECT and PET for assessment of tissue viability R. Bonow. 12. 11C-Acetate in ischemic heart disease R.J. Gropler. 13. Assessment of myocardial viability using 15O-Water H. Iida, et al. 14. Myocardial 82Rubidium kinetics identify cell membrane integrity and tissue viability J. vom Dahl, M. Schwaiger. 15. Imaging hypoxic myocardium G.V. Martin, et al. Other Applications:- 16. Quantification of myocardial oxygen consumption using 11C-Acetate R. Beanlands, et al. 17. Applications in non-ischemic heart disease and heart failure R. Beanlands, H.G. Wolpers. 18. The use of PET radiopharmaceuticals to probe cardiac receptors H. Valette, et al. Appendix:- 19. Clinical PET protocols J.M. Rothley, A.R.J. Weeden. Index.

Cardiac Positron Emission Tomography

Die Positronenemissionstomographie (PET) stellt die fortschrittlichste szintigraphische Technik zur Beurteilung physiologischer und biochemischer Prozesse invivo im Herzen dar. Die z.Z. verfugbare PET-Technologie erlaubt die Quantifizierung lokaler Tracerkonzentrationen mit hoher raumlicher und zeitlicher Auflosung. Zahlreiche Tracer wurden zur Beurteilung von Perfusion, Substratstoffwechsel sowie autonomer neuronaler Innervation des Herzens entwickelt und werden klinisch eingesetzt. Das bedeutendste klinische Anwendungsgebiet ist die Beurteilung des Substratstoffwechsels mit 18Fluordeoxyglukose (18FDG), die den Gold-Standard fur die Vitalitatsdiagnostik bei der koronaren Herzerkrankung (KHK) mit regionaler Wandbewegungsstorung darstellt. Neben der funktionellen Relevanz fur die Verbesserung der regionalen Wandfunktion nach Revaskularisation besitzt die 18FDG-PET einen prognostischen Wert hinsichtlich des Nutzen-Risiko-Verhaltnisses von Revaskularisationsmasnahmen. Neuere Untersuchungen weisen darauf hin, das die 18FDG-PET in kostengunstiger SPECT- oder Koinzidenz-Technik eine vergleichbare klinische Aussagekraft bietet, die bei entsprechender Verfugbarkeit eine breitere Anwendung der Methode erwarten last. Die Messung des myokardialen Flusses in Ruhe und unter Belastung mit 13N-Ammoniak oder 82Rb stellt die genaueste szintigraphische Methode zur KHK-Diagnostik und Abschatzung ihres Schweregrades dar. Quantitative Messungen des (regionalen) myokardialen Flusses und die Bestimmung der myokardialen Flusreserve erlauben die Diagnose einer eingeschrankten Vasoreaktivitat vor dem Auftreten hamodynamisch wirksamer Veranderungen, aber auch das Monitoring therapeutischer Interventionen (Risikofaktorenmodifikation). Analoga sympathischer Neuronentransmitter wie 11C-Hydroxyepinephrin ermoglichen die Beurteilung der neuronalen (sympathischen) Reinnervation nach Herztransplantation und zeigen eine moglicherweise prognostisch relevante Verminderung der 11C-HED-Aufnahme bei Patienten mit Herzinsuffizienz. Die hohe funktionelle und prognostische Relevanz der PET wird bei besserer Verfugbarkeit und kostengunstigerer Instrumentation den zukunftigen Stellenwert dieser Untersuchungstechnik in Diagnostik, Prognosebeurteilung und Therapiemonitoring bei Herzerkrankungen bestimmen.

Positronen-Emissions-Tomographie

Molecular Imaging II. Preface.

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