RESUMEN
Purpose: To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS). Materials and methods: In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant. Results: Three features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics. Conclusion: Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.
RESUMEN
ObjectiveTo describe the development and initial validation of a novel patient-reported outcome measure of Long COVID symptom burden, the Symptom-Burden Questionnaire for Long COVID (SBQ-LC). Method and FindingsThis multi-phase, prospective mixed-methods study took place between April and August 2021 in the United Kingdom (UK). A conceptual framework and initial item pool were developed from published systematic reviews. Further concept elicitation and content validation was undertaken with adults with lived experience (n = 13) and clinicians (n = 10), and face validity was confirmed by the Therapies for Long COVID Study Patient and Public Involvement group (n = 25). The draft SBQ-LC was field tested by adults with self-reported Long COVID recruited via social media and international Long COVID support groups (n = 274). Thematic analysis of interview and survey transcripts established content validity and informed construction of the draft questionnaire. Rasch analysis of field test data guided item and scale refinement and provided evidence of the final SBQ-LCs measurement properties. The Rasch-derived SBQ-LC is composed of 17 independent scales with promising psychometric properties. Respondents rate symptom burden during the past 7-days using a dichotomous response or 4-point rating scale. Each scale provides coverage of a different symptom domain and returns a summed raw score that may be converted to a linear (0 - 100) score. Higher scores represent higher symptom burden. ConclusionsThe SBQ-LC is a comprehensive patient-reported assessment of Long COVID symptom burden developed using modern psychometric methods. It measures symptoms of Long COVID important to individuals with lived experience and may be used to evaluate the impact of interventions and inform best practice in clinical management.
RESUMEN
IntroductionIndividuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. Methods and analysisA cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink (CPRD) and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability, and patient reported outcome measures. Data will be collected monthly for one year. Statistical clustering methods will be used to identify distinct Long COVID symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear sub-study which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy. We will review existing evidence on interventions for post-viral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulated evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation. Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. Ethics and disseminationEthical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). The study is registered on the ISRCTN Registry (1567490). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. Article SummaryO_ST_ABSStrengths and limitations of the studyC_ST_ABSO_LIThe study will generate a nationally representative cohort of individuals with Long COVID recruited from primary care. C_LIO_LIWe will recruit controls matched on a wide range of demographic and clinical factors to assess differences in symptoms between people with Long COVID and similar individuals without a history of COVID-19. C_LIO_LIWe will use a newly developed electronic patient reported outcome measure (Symptom Burden Questionnaire) for Long COVID to comprehensively assess a wide range of symptoms highlighted by existing literature, patients, and clinicians. C_LIO_LIImmunological, proteomic, genetic, and wearable data captured in the study will allow deep phenotyping of Long COVID syndromes to help better target therapies. C_LIO_LIA limitation is that a significant proportion of non-hospitalised individuals affected by COVID-19 in the first wave of the pandemic will lack confirmatory testing and will be excluded from recruitment to the study. C_LI