Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging

BACKGROUND

The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.

AIM OF REVIEW

The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Unspoken Truths: Mental Health Among Academic Surgeons

OBJECTIVE

To characterize the current state of mental health within the surgical workforce in the United States.

BACKGROUND

Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown.

METHODS

Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed.

RESULTS

Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4%-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. In all, 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P <0.001), anxiety (31.6% vs. 16.2%, P =0.001), PTSD (12.8% vs. 5.6%, P =0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P =0.022). Overall, 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past 2 weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P =0.033) and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P <0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P =0.002) were associated with increased odds of suicidal ideation over the past 12 months.

CONCLUSIONS

Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the United States.

Neuroimmune activation and increased brain aging in chronic pain patients after the COVID-19 pandemic onset

The COVID-19 pandemic has exerted a global impact on both physical and mental health, and clinical populations have been disproportionally affected. To date, however, the mechanisms underlying the deleterious effects of the pandemic on pre-existing clinical conditions remain unclear. Here we investigated whether the onset of the pandemic was associated with an increase in brain/blood levels of inflammatory markers and MRI-estimated brain age in patients with chronic low back pain (cLBP), irrespective of their infection history. A retrospective cohort study was conducted on 56 adult participants with cLBP (28 'Pre-Pandemic', 28 'Pandemic') using integrated Positron Emission Tomography/ Magnetic Resonance Imaging (PET/MRI) and the radioligand [11C]PBR28, which binds to the neuroinflammatory marker 18 kDa Translocator Protein (TSPO). Image data were collected between November 2017 and January 2020 ('Pre-Pandemic' cLBP) or between August 2020 and May 2022 ('Pandemic' cLBP). Compared to the Pre-Pandemic group, the Pandemic patients demonstrated widespread and statistically significant elevations in brain TSPO levels (P =.05, cluster corrected). PET signal elevations in the Pandemic group were also observed when 1) excluding 3 Pandemic subjects with a known history of COVID infection, or 2) using secondary outcome measures (volume of distribution -VT- and VT ratio - DVR) in a smaller subset of participants. Pandemic subjects also exhibited elevated serum levels of inflammatory markers (IL-16; P <.05) and estimated BA (P <.0001), which were positively correlated with [11C]PBR28 SUVR (r's ≥ 0.35; P's < 0.05). The pain interference scores, which were elevated in the Pandemic group (P <.05), were negatively correlated with [11C]PBR28 SUVR in the amygdala (r = -0.46; P<.05). This work suggests that the pandemic outbreak may have been accompanied by neuroinflammation and increased brain age in cLBP patients, as measured by multimodal imaging and serum testing. This study underscores the broad impact of the pandemic on human health, which extends beyond the morbidity solely mediated by the virus itself.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

PURPOSE

To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies.

METHODS

We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated.

RESULT

In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community.

CONCLUSION

In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers.

CRITICAL RELEVANCE STATEMENT

A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning.

KEY POINTS

• A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Early Detection of Lung Cancer Using Small RNAs

INTRODUCTION

Lung cancer remains the deadliest cancer in the world, and lung cancer survival is heavily dependent on tumor stage at the time of detection. Low-dose computed tomography screening can reduce mortality; however, annual screening is limited by low adherence in the United States of America and still not broadly implemented in Europe. As a result, less than 10% of lung cancers are detected through existing programs. Thus, there is a great need for additional screening tests, such as a blood test, that could be deployed in the primary care setting.

METHODS

We prospectively recruited 1384 individuals meeting the National Lung Screening Trial demographic eligibility criteria for lung cancer and collected stabilized whole blood to enable the pipetting-free collection of material, thus minimizing preanalytical noise. Ultra-deep small RNA sequencing (20 million reads per sample) was performed with the addition of a method to remove highly abundant erythroid RNAs, and thus open bandwidth for the detection of less abundant species originating from the plasma or the immune cellular compartment. We used 100 random data splits to train and evaluate an ensemble of logistic regression classifiers using small RNA expression of 943 individuals, discovered an 18-small RNA feature consensus signature (miLung), and validated this signature in an independent cohort (441 individuals). Blood cell sorting and tumor tissue sequencing were performed to deconvolve small RNAs into their source of origin.

RESULTS

We generated diagnostic models and report a median receiver-operating characteristic area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.86) in the discovery cohort and generalized performance of 0.83 in the validation cohort. Diagnostic performance increased in a stage-dependent manner ranging from 0.73 (95% CI: 0.71-0.76) for stage I to 0.90 (95% CI: 0.89-0.90) for stage IV in the discovery cohort and from 0.76 to 0.86 in the validation cohort. We identified a tumor-shed, plasma-bound ribosomal RNA fragment of the L1 stalk as a dominant predictor of lung cancer. The fragment is decreased after surgery with curative intent. In additional experiments, results of dried blood spot collection and sequencing revealed that small RNA analysis could potentially be conducted through home sampling.

CONCLUSIONS

These data suggest the potential of a small RNA-based blood test as a viable alternative to low-dose computed tomography screening for early detection of smoking-associated lung cancer.

Trends and statistics of artificial intelligence and radiomics research in Radiology, Nuclear Medicine, and Medical Imaging: bibliometric analysis

OBJECTIVE

To conduct a comprehensive bibliometric analysis of artificial intelligence (AI) and its subfields as well as radiomics in Radiology, Nuclear Medicine, and Medical Imaging (RNMMI).

METHODS

Web of Science was queried for relevant publications in RNMMI and medicine along with their associated data from 2000 to 2021. Bibliometric techniques utilised were co-occurrence, co-authorship, citation burst, and thematic evolution analyses. Growth rate and doubling time were also estimated using log-linear regression analyses.

RESULTS

According to the number of publications, RNMMI (11,209; 19.8%) was the most prominent category in medicine (56,734). USA (44.6%) and China (23.1%) were the two most productive and collaborative countries. USA and Germany experienced the strongest citation bursts. Thematic evolution has recently exhibited a significant shift toward deep learning. In all analyses, the annual number of publications and citations demonstrated exponential growth, with deep learning-based publications exhibiting the most prominent growth pattern. Estimated continuous growth rate, annual growth rate, and doubling time of the AI and machine learning publications in RNMMI were 26.1% (95% confidence interval [CI], 12.0-40.2%), 29.8% (95% CI, 12.7-49.5%), and 2.7 years (95% CI, 1.7-5.8), respectively. In the sensitivity analysis using data from the last 5 and 10 years, these estimates ranged from 47.6 to 51.1%, 61.0 to 66.7%, and 1.4 to 1.5 years.

CONCLUSION

This study provides an overview of AI and radiomics research conducted mainly in RNMMI. These results may assist researchers, practitioners, policymakers, and organisations in gaining a better understanding of both the evolution of these fields and the importance of supporting (e.g., financial) these research activities.

KEY POINTS

• In terms of the number of publications on AI and ML, Radiology, Nuclear Medicine, and Medical Imaging was the most prominent category compared to the other categories related to medicine (e.g., Health Policy & Services, Surgery). • All evaluated analyses (i.e., AI, its subfields, and radiomics), based on the annual number of publications and citations, demonstrated exponential growth, with decreasing doubling time, which indicates increasing interest from researchers, journals, and, in turn, the medical imaging community. • The most prominent growth pattern was observed in deep learning-based publications. However, the further thematic analysis demonstrated that deep learning has been underdeveloped but highly relevant to the medical imaging community.

CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII

Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.

Assessing Bias and Limitations of Clinical Validation Studies of Molecular Diagnostic Tests for Indeterminate Thyroid Nodules: Systematic Review and Meta-Analysis

Background: Molecular tests for thyroid nodules with indeterminate fine needle aspiration results are increasingly used in clinical practice; however, true diagnostic summaries of these tests are unknown. A systematic review and meta-analysis were completed to (1) evaluate the accuracy of commercially available molecular tests for malignancy in indeterminate thyroid nodules and (2) quantify biases and limitations in studies that validate those tests. Summary: PubMed, EMBASE, and Web of Science were systematically searched through July 2021. English language articles that reported original clinical validation attempts of molecular tests for indeterminate thyroid nodules were included if they reported counts of true-negative, true-positive, false-negative, and false-positive results. We performed screening and full-text review, followed by assessment of eight common biases and limitations, extraction of diagnostic and histopathological information, and meta-analysis of clinical validity using a bivariate linear mixed-effects model. Forty-nine studies were included. Meta-analysis of Afirma Gene expression classifiers (GEC; n = 38 studies) revealed a sensitivity of 0.92 (confidence interval: 0.90-0.94), specificity of 0.26 (0.20-0.32), negative likelihood ratio (LR-) of 0.32 (0.23-0.44), positive LR+ of 1.24 (1.15-1.35), and area under the curve (AUC) of 0.83 (0.74-0.89). Afirma Genomic Sequencing Classifier (GSC; n = 10) had a sensitivity of 0.94 (0.89-0.96), specificity of 0.38 (0.27-0.50), LR- of 0.18 (0.10-0.30), LR+ of 1.52 (1.28-1.87), and AUC of 0.91 (0.62-0.92). ThyroSeq v1 and v2 (n = 10) had a sensitivity of 0.86 (0.82-0.90), specificity of 0.74 (0.59-0.85), LR- of 0.19 (0.13-0.26), LR+ of 3.52 (2.08-5.92), and AUC of 0.86 (0.81-0.90). ThyroSeq v3 (n = 6) had a sensitivity of 0.92 (0.86-0.95), specificity of 0.41 (0.18-0.69), LR- of 0.24 (0.09-0.62), LR+ of 1.67 (1.09-2.98), and AUC of 0.90 (0.63-0.92). Fourteen percent of studies conducted a blinded histopathologic review of excised thyroid nodules, and 8% made the decision to go to surgery blind to molecular test results. Conclusions: Meta-analyses reveal a high diagnostic accuracy of molecular tests for thyroid nodule assessment of malignancy risk; however, these studies are subject to several limitations. Limitations and their potential clinical impacts must be addressed and, when feasible, adjusted for using valid statistical methodologies.

Study protocol for a phase II, double-blind, randomised controlled trial of cannabidiol (CBD) compared with placebo for reduction of brain neuroinflammation in adults with chronic low back pain

INTRODUCTION

Chronic pain is a debilitating medical problem that is difficult to treat. Neuroinflammatory pathways have emerged as a potential therapeutic target, as preclinical studies have demonstrated that glial cells and neuroglial interactions play a role in the establishment and maintenance of pain. Recently, we used positron emission tomography (PET) to demonstrate increased levels of 18 kDa translocator protein (TSPO) binding, a marker of glial activation, in patients with chronic low back pain (cLBP). Cannabidiol (CBD) is a glial inhibitor in animal models, but studies have not assessed whether CBD reduces neuroinflammation in humans. The principal aim of this trial is to evaluate whether CBD, compared with placebo, affects neuroinflammation, as measured by TSPO levels.

METHODS AND ANALYSIS

This is a double-blind, randomised, placebo-controlled, phase II clinical trial. Eighty adults (aged 18-75) with cLBP for >6 months will be randomised to either an FDA-approved CBD medication (Epidiolex) or matching placebo for 4 weeks using a dose-escalation design. All participants will undergo integrated PET/MRI at baseline and after 4 weeks of treatment to evaluate neuroinflammation using [¹¹C]PBR28, a second-generation radioligand for TSPO. Our primary hypothesis is that participants randomised to CBD will demonstrate larger reductions in thalamic [¹¹C]PBR28 signal compared with those receiving placebo. We will also assess the effect of CBD on (1) [¹¹C]PBR28 signal from limbic regions, which our prior work has linked to depressive symptoms and (2) striatal activation in response to a reward task. Additionally, we will evaluate self-report measures of cLBP intensity and bothersomeness, depression and quality of life at baseline and 4 weeks.

ETHICS AND DISSEMINATION

This protocol is approved by the Massachusetts General Brigham Human Research Committee (protocol number: 2021P002617) and FDA (IND number: 143861) and registered with ClinicalTrials.gov. Results will be published in peer-reviewed journals and presented at conferences.

TRIAL REGISTRATION NUMBER

NCT05066308; ClinicalTrials.gov.

A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy

Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune-related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ~30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction. We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well-characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signaling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining.

Dynamic contrast-enhanced magnetic resonance imaging of the lung reveals important pathobiology in idiopathic pulmonary fibrosis

Introduction

Evidence suggests that abnormalities occur in the lung microvasculature in idiopathic pulmonary fibrosis (IPF). We hypothesised that dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) could detect alterations in permeability, perfusion and extracellular extravascular volume in IPF, thus providing in vivo regional functional information not otherwise available.

Methods

Healthy controls and IPF subjects underwent DCE-MRI of the thorax using a dynamic volumetric radial sampling sequence and administration of gadoterate meglumine at a dose of 0.1 mmol·kg⁻¹ at 2 mL·s⁻¹. Model-free analysis of signal intensity versus time curves in regions of interest from a lower, middle and upper axial plane, a posterior coronal plane and the whole lung yielded parameters reflective of perfusion and permeability (peak enhancement and rate of contrast arrival (k_washin)) and the extracellular extravascular space (rate of contrast clearance (k_washout)). These imaging parameters were compared between IPF and healthy control subjects, and between fast/slow IPF progressors.

Results

IPF subjects (n=16, 56% male, age (range) 67.5 (60–79) years) had significantly reduced peak enhancement and slower k_washin in all measured lung regions compared to the healthy volunteers (n=17, 65% male, age (range) 58 (51–63) years) on unadjusted analyses consistent with microvascular alterations. k_washout, as a measure of the extravascular extracellular space, was significantly slower in the lower lung and posterior coronal regions in the IPF subjects consistent with an increased extravascular extracellular space. All estimates were attenuated after adjusting for age. Similar trends were observed, but only the associations with k_washin in certain lung regions remained statistically significant. Among IPF subjects, k_washout rates nearly perfectly discriminated between those with rapidly progressive disease versus those with stable/slowly progressive disease.

Conclusions

DCE-MRI detects changes in the microvasculature and extravascular extracellular space in IPF, thus providing in vivo regional functional information.

Dynamic contrast-enhanced MRI demonstrates important in vivo lung regional microvascular and extravascular extracellular differences between IPF patients and healthy controls. These results signify IPF pathobiology and may have prognostic significance.https://bit.ly/3l14SWM

Association between Inflammatory Conditions and Alzheimer's Disease Age of Onset in Down Syndrome

Adults with Down syndrome (DS) have an exceptionally high prevalence of Alzheimer disease (AD), with an earlier age of onset compared with the neurotypical population. In addition to beta amyloid, immunological processes involved in neuroinflammation and in peripheral inflammatory/autoimmune conditions are thought to play important roles in the pathophysiology of AD. Individuals with DS also have a high prevalence of autoimmune/inflammatory conditions which may contribute to an increased risk of early AD onset, but this has not been studied. Given the wide range in the age of AD onset in those with DS, we sought to evaluate the relationship between the presence of inflammatory conditions and the age of AD onset. We performed a retrospective study on 339 adults with DS, 125 who were cognitively stable (CS) and 214 with a diagnosis of AD. Data were available for six autoimmune conditions (alopecia, celiac disease, hypothyroidism, psoriasis, diabetes and vitamin B12 deficiency) and for one inflammatory condition, gout. Gout was associated with a significant delay in the age of AD onset by more than 2.5 years. Our data suggests that inflammatory conditions may play a role in the age of AD onset in DS. Further studies are warranted.

Practical application and validation of the 2018 ATS/ERS/JRS/ALAT and Fleischner Society guidelines for the diagnosis of idiopathic pulmonary fibrosis

Background

Accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is essential to inform prognosis and treatment. In 2018, the ATS/ERS/JRS/ALAT and Fleischner Society released new diagnostic guidelines for usual interstitial pneumonitis (UIP)/IPF, adding Probable UIP as a CT category based on prior studies demonstrating this category had relatively high positive predictive value (PPV) for histopathologic UIP/Probable UIP. This study applies the 2018 ATS/ERS/JRS/ALAT and Fleischner Society guidelines to determine test characteristics of CT categories in academic clinical practice.

Methods

CT and histopathology were evaluated by three thoracic radiologists and two thoracic pathologists. Comparison of consensus categorization by the 2018 ATS and Fleischner Society guidelines by CT and histopathology was performed.

Results

Of patients with CT UIP, 87% (PPV, 95% CI: 60–98%) had histopathologic UIP with 97% (CI: 90–100%) specificity. Of patients with CT Probable UIP, 38% (PPV, CI: 14–68%) had histopathologic UIP and 46% (PPV, CI: 19–75%) had either histopathologic UIP or Probable UIP, with 88% (CI: 77–95%) specificity. Patients with CT Indeterminate and Alternative Diagnosis had histopathologic UIP in 27% (PPV, CI: 6–61%) and 21% (PPV, CI: 11–33%) of cases with specificities of 90% (CI: 80–96%) and 25% (CI: 16–37%). Interobserver variability (kappa) between radiologists ranged 0.32–0.81.

Conclusions

CT UIP and Probable UIP have high specificity for histopathologic UIP, and CT UIP has high PPV for histopathologic UIP. PPV of CT Probable UIP was 46% for combined histopathologic UIP/Probable UIP. Our results indicate that additional studies are needed to further assess and refine the guideline criteria to improve classification performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01670-7.

Variation of Shear Wave Elastography With Preload in the Thyroid: Quantitative Validation

OBJECTIVES

Thyroid shear wave elastography (SWE) has been shown to have advantages compared to biopsy or other imaging modalities in the evaluation of thyroid nodules. However, studies show variability in its assessment. The objective of this study was to evaluate whether stiffness measurements of the normal thyroid, as estimated by SWE, varied due to preload force or the pressure applied between the transducer and the patient.

METHODS

In this study, a measurement system was attached to the ultrasound transducer to measure the applied load. Shear wave elastographic measurements were obtained from the left lobe of the thyroid at applied transducer forces between 2 and 10 N. A linear mixed-effects model was constructed to quantify the association between the preload force and stiffness while accounting for correlations between repeated measurements within each participant. The preload force effect on elasticity was modeled by both linear and quadratic terms to account for a possible nonlinear association between these variables.

RESULTS

Nineteen healthy volunteers without known thyroid disease participated in the study. The participants had a mean age ± SD of 36 ± 8 years; 74% were female; 74% had a normal body mass index; and 95% were white non-Hispanic/Latino. The estimated elastographic value at a 2-N preload force was 16.7 kPa (95% confidence interval, 14.1-19.3 kPa), whereas the value at 10 N was 29.9 kPa (95% confidence interval, 24.9-34.9 kPa).

CONCLUSIONS

The preload force was significantly and nonlinearly associated with SWE estimates of thyroid stiffness. Quantitative standardization of preload forces in the assessment of thyroid nodules using elastography is an integral factor for improving the accuracy of thyroid nodule evaluation.

Small-diameter TIPS combined with splenic artery embolization in the management of refractory ascites in cirrhotic patients

PURPOSE

Maximally decreasing portal pressures with transjugular intrahepatic portosystemic shunt (TIPS) is associated with improved ascites control but also increased encephalopathy incidence. Since splenic venous flow contributes to portal hypertension, we assessed if combining small-diameter TIPS with splenic artery embolization could improve ascites while minimizing encephalopathy.

METHODS

Fifty-five patients underwent TIPS creation for refractory ascites. Subjects underwent creation of 8 mm TIPS followed by proximal splenic artery embolization (group A, n=8), or of 8 mm (group B, n=6) or 10 mm TIPS (group C, n=41) without splenic embolization. Data were retrospectively reviewed.

RESULTS

In group A, median portosystemic gradient decreased from 19 mmHg to 9 mmHg after TIPS, and 8 mmHg after subsequent splenic artery embolization. In groups B and C, gradient decreased from 15 mmHg to 8 mmHg and 16 mmHg to 6 mmHg. All patients except for one in group A and two in C had greater than 50% reduction in the number of paracenteses in 3 months. Any postprocedural encephalopathy incidence was 62%, 50%, 83% in groups A, B, and C, respectively. Overall, 20% of subjects with 10 mm TIPS required TIPS reduction/closure compared to 7% of subjects with 8 mm TIPS.

CONCLUSION

We found that 8 mm diameter TIPS provided similar ascites control compared to 10 mm TIPS regardless of splenic embolization. While more patients with 10 mm TIPS required reduction/closure for severe encephalopathy, the study was underpowered for definitive assessment. Splenic embolization might have the potential to further decrease portosystemic gradient and ascites as an alternative to dilation of TIPS to 10 mm minimizing the risk of encephalopathy, but larger studies are warranted.

Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients

Importance

The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular signatures, such as blood-borne microRNA (miRNA) signatures, may have high rates of sensitivity and specificity but require additional studies with large cohorts and standardized measurements to confirm the generalizability of miRNA signatures.

Objective

To investigate the use of blood-borne miRNAs as potential circulating markers for detecting lung cancer in an extended cohort of symptomatic patients and control participants.

Design, Setting, and Participants

This multicenter, cohort study included patients from case-control and cohort studies (TREND and COSYCONET) with 3102 patients being enrolled by convenience sampling between March 3, 2009, and March 19, 2018. For the cohort study TREND, population sampling was performed. Clinical diagnoses were obtained for 3046 patients (606 patients with non-small cell and small cell lung cancer, 593 patients with nontumor lung diseases, 883 patients with diseases not affecting the lung, and 964 unaffected control participants). No samples were removed because of experimental issues. The collected data were analyzed between April 2018 and November 2019.

Main Outcomes and Measures

Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer.

Results

A total of 3102 patients with a mean (SD) age of 61.1 (16.2) years were enrolled. Data on the sex of the participants were available for 2856 participants; 1727 (60.5%) were men. Genome-wide miRNA profiles of blood samples from 3046 individuals were evaluated by machine-learning methods. Three classification scenarios were investigated by splitting the samples equally into training and validation sets. First, a 15-miRNA signature from the training set was used to distinguish patients diagnosed with lung cancer from all other individuals in the validation set with an accuracy of 91.4% (95% CI, 91.0%-91.9%), a sensitivity of 82.8% (95% CI, 81.5%-84.1%), and a specificity of 93.5% (95% CI, 93.2%-93.8%). Second, a 14-miRNA signature from the training set was used to distinguish patients with lung cancer from patients with nontumor lung diseases in the validation set with an accuracy of 92.5% (95% CI, 92.1%-92.9%), sensitivity of 96.4% (95% CI, 95.9%-96.9%), and specificity of 88.6% (95% CI, 88.1%-89.2%). Third, a 14-miRNA signature from the training set was used to distinguish patients with early-stage lung cancer from all individuals without lung cancer in the validation set with an accuracy of 95.9% (95% CI, 95.7%-96.2%), sensitivity of 76.3% (95% CI, 74.5%-78.0%), and specificity of 97.5% (95% CI, 97.2%-97.7%).

Conclusions and Relevance

The findings of the study suggest that the identified patterns of miRNAs may be used as a component of a minimally invasive lung cancer test, complementing imaging, sputum cytology, and biopsy tests.

Advanced MRI of Liver Fibrosis and Treatment Response in a Rat Model of Nonalcoholic Steatohepatitis

Background Liver biopsy is the reference standard to diagnose nonalcoholic steatohepatitis (NASH) but is invasive with potential complications. Purpose To evaluate molecular MRI with type 1 collagen-specific probe EP-3533 and allysine-targeted fibrogenesis probe Gd-Hyd, MR elastography, and native T1 to characterize fibrosis and to assess treatment response in a rat model of NASH. Materials and Methods MRI was performed prospectively (June-November 2018) in six groups of male Wistar rats (a) age- and (b) weight-matched animals received standard chow (n = 12 per group); (c) received choline-deficient, l-amino acid-defined, high-fat diet (CDAHFD) for 6 weeks or (d) 9 weeks (n = 8 per group); (e) were fed 6 weeks of CDAHFD and switched to standard chow for 3 weeks (n = 12); (f) were fed CDAHFD for 9 weeks with daily treatment of elafibranor beginning at week 6 (n = 14). Differences in imaging measurements and tissue analyses among groups were tested with one-way analysis of variance. The ability of each imaging measurement to stage fibrosis was quantified by using area under the receiver operating characteristic curve (AUC) with quantitative digital pathology (collagen proportionate area [CPA]) as reference standard. Optimal cutoff values for distinguishing advanced fibrosis were used to assess treatment response. Results AUC for distinguishing fibrotic (CPA >4.8%) from nonfibrotic (CPA ≤4.8%) livers was 0.95 (95% confidence interval [CI]: 0.91, 1.00) for EP-3533, followed by native T1, Gd-Hyd, and MR elastography with AUCs of 0.90 (95% CI: 0.83, 0.98), 0.84 (95% CI: 0.74, 0.95), and 0.65 (95% CI: 0.51, 0.79), respectively. AUCs for discriminating advanced fibrosis (CPA >10.3%) were 0.86 (95% CI: 0.76, 0.97), 0.96 (95% CI: 0.90, 1.01), 0.84 (95% CI: 0.70, 0.98), and 0.74 (95% CI: 0.63, 0.86) for EP-3533, Gd-Hyd, MR elastography, and native T1, respectively. Gd-Hyd MRI had the highest accuracy (24 of 26, 92%; 95% CI: 75%, 99%) in identifying responders and nonresponders in the treated groups compared with MR elastography (23 of 26, 88%; 95% CI: 70%, 98%), EP-3533 (20 of 26, 77%; 95% CI: 56%, 91%), and native T1 (14 of 26, 54%; 95% CI: 33%, 73%). Conclusion Collagen-targeted molecular MRI most accurately detected early onset of fibrosis, whereas the fibrogenesis probe Gd-Hyd proved most accurate for detecting treatment response. © RSNA, 2020 Online supplemental material is available for this article.

Differentiation of regenerative nodule, dysplastic nodule, and small hepatocellular carcinoma in cirrhotic patients: a contrast-enhanced ultrasound-based multivariable model analysis

OBJECTIVE

To develop a contrast-enhanced ultrasound (CEUS)-based model for differentiating cirrhotic liver lesions and for active surveillance of hepatocellular carcinoma (HCC).

METHODS

Patients with focal liver lesions (FLLs) with biopsy/resection-proven pathology and pre-procedure CEUS were enrolled from our institution between January 2011 and November 2014. Univariable and multivariable regression models were constructed using qualitative CEUS features and/or contrast arrival time ratio (CAT_R). The optimism-adjusted Harrell's generalized concordance index (C_H) was used to quantify the discriminatory ability of each CEUS feature and model.

RESULTS

A total of 149 patients (113 men and 36 women) with 162 FLLs were enrolled with mean age 53.4 ± 12.7 years. A 0.1-unit reduction in CAT_R was associated with a 68% increase in the odds of having a higher nodule ranking (RN < DN < small HCC) (OR, 0.32; 95% CI, 0.20-0.50, p < .001). Arterial phase hypoenhancement and isoenhancement were inversely associated with a higher nodule ranking compared to hyperenhancement. Late-phase isoenhancement was associated with lower odds of a higher nodule ranking. The CEUS + CAT_R model (C_H 0.92, 0.89-0.95) provided greater discriminatory ability when compared to the CAT_R model (ΔC_H 0.09, 0.04-0.13, p < .001) and the CEUS model (ΔC_H 0.03, 0.01-0.05, p = .02).

CONCLUSIONS

Our results provide preliminary evidence that multivariable regression model constructed using both qualitative CEUS features and CAT_R provides the greatest discriminatory ability to differentiate RN, DN, and small HCC in patients with cirrhosis, and might allow for active surveillance of the progression of cirrhotic liver lesions.

KEY POINTS

• Proportional odds logistic regression models based on qualitative CEUS features and/or CAT_R can be used for differentiating cirrhotic liver lesions and for active surveillance of HCC. • The reduction of CAT_R (RN < DN < small HCC) was strongly associated with high-risk cirrhotic liver nodules. • Inclusion of CAT_R in the CEUS prediction model significantly improved its performance for cirrhotic liver lesions risk-stratification.

Comparing results from multiple imputation and dynamic marginal structural models for estimating when to start antiretroviral therapy

Optimal timing of initiating antiretroviral therapy has been a controversial topic in HIV research. Two highly publicized studies applied different analytical approaches, a dynamic marginal structural model and a multiple imputation method, to different observational databases and came up with different conclusions. Discrepancies between the two studies' results could be due to differences between patient populations, fundamental differences between statistical methods, or differences between implementation details. For example, the two studies adjusted for different covariates, compared different thresholds, and had different criteria for qualifying measurements. If both analytical approaches were applied to the same cohort holding technical details constant, would their results be similar? In this study, we applied both statistical approaches using observational data from 12,708 HIV-infected persons throughout the USA. We held technical details constant between the two methods and then repeated analyses varying technical details to understand what impact they had on findings. We also present results applying both approaches to simulated data. Results were similar, although not identical, when technical details were held constant between the two statistical methods. Confidence intervals for the dynamic marginal structural model tended to be wider than those from the imputation approach, although this may have been due in part to additional external data used in the imputation analysis. We also consider differences in the estimands, required data, and assumptions of the two statistical methods. Our study provides insights into assessing optimal dynamic treatment regimes in the context of starting antiretroviral therapy and in more general settings. Copyright © 2016 John Wiley & Sons, Ltd.

Anesthesia for liver transplantation in US academic centers: institutional structure and perioperative care

Investigators at a single institution have shown that the organization of the anesthesia team influences patient outcomes after liver transplant surgery. Little is known about how liver transplant anesthesiologists are organized to deliver care throughout the United States. Therefore, we collected quantitative survey data from adult liver transplant programs in good standing with national governing agencies so that we could describe team structure and duties. Information was collected from 2 surveys in a series of quantitative surveys conducted by the Liver Transplant Anesthesia Consortium. All data related to duties, criteria for team membership, interactions/communication with the multidisciplinary team, and service availability were collected and summarized. Thirty-four of 119 registered transplant centers were excluded (21 pediatric centers and 13 centers not certified by national governing agencies). Private practice sites (26) were later excluded because of a poor response rate. There were minimal changes in the compositions of the programs between the 2 surveys. All academic programs had distinct liver transplant anesthesia teams. Most had set criteria for membership and protocols outlining the preoperative evaluation, attended selection committees, and were always available for transplant surgery. Fewer were involved in postoperative care or were available for patients needing subsequent surgery. Most trends were associated with the center volume. In conclusion, some of the variance in team structure and responsibilities is probably related to resources available at the site of practice. However, similarities in specific duties across all teams suggest some degree of self-initiated specialization.

What can paper-based clinical information systems tell us about the design of computerized clinical information systems (CIS) in the ICU?

BACKGROUND

Screen designs in computerized clinical information systems (CIS) have been modeled on their paper predecessors. However, limited understanding about how paper forms support clinical work means that we risk repeating old mistakes and creating new opportunities for error and inefficiency as illustrated by problems associated with computerized provider order entry systems.

PURPOSE

This study was designed to elucidate principles underlying a successful ICU paper-based CIS. The research was guided by two exploratory hypotheses: (1) paper-based artefacts (charts, notes, equipment, order forms) are used differently by nurses, doctors and other healthcare professionals in different (formal and informal) conversation contexts and (2) different artefacts support different decision processes that are distributed across role-based conversations.

METHOD

All conversations undertaken at the bedsides of five patients were recorded with any supporting artefacts for five days per patient. Data was coded according to conversational role-holders, clinical decision process, conversational context and artefacts. 2133 data points were analyzed using Poisson logistic regression analyses.

RESULTS

Results show significant interactions between artefacts used during different professional conversations in different contexts (chi(2)((df=16))=55.8, p<0.0001). The interaction between artefacts used during different professional conversations for different clinical decision processes was not statistically significant although all two-way interactions were statistically significant.

CONCLUSIONS

Paper-based CIS have evolved to support complex interdisciplinary decision processes. The translation of two design principles - support interdisciplinary perspectives and integrate decision processes - from paper to computerized CIS may minimize the risks associated with computerization.

Development of methodology for conducting clinical trials in frontotemporal lobar degeneration

To design clinical trials for the frontotemporal lobar degenerations (FTLD), knowledge about measurement of disease progression is needed to estimate power and enable the choice of optimal outcome measures. The aim here was to conduct a multicentre, 1 year replica of a clinical trial in patients with one of four FTLD syndromes, behavioural variant frontotemporal dementia (bvFTD), progressive nonfluent aphasia (PNFA), progressive logopenic aphasia (PLA) and semantic dementia (SMD). Patients with one of the four FTLD syndromes were recruited from five academic medical centres over a 2 year period. Standard operationalized diagnostic criteria were used. In addition to clinical inclusion and exclusion criteria, patients were required to exhibit focal frontal, temporal or insular brain atrophy or dysfunction by neuroimaging. Patients underwent neuropsychological, functional, behavioural, neurological and MR imaging assessment at baseline and approximately 12 months later. Potential outcome measures were examined for their rates of floor and ceiling values at baseline and end of study, their mean changes and variances. The neuropsychological tests were combined into two cognitive composites -- one for language functions and the other for executive functions. There were 107 patients who underwent baseline assessment and 78 who completed a follow-up assessment within 10-16 months. Two global measures, the FTLD-modified Clinical Dementia Rating (FTLD-modified CDR) and the Clinical Global Impression of Change (CGIC) demonstrated decline in the majority of patients. Several cognitive measures showed negligible floor or ceiling scores either at baseline or follow-up. Scores declined at follow-up in the majority of patients. The cognitive, executive and combined composites were shown to be sensitive to change across all FTLD syndromes. Patients improved at follow-up on the behavioural scales -- the Frontal Behavioural Inventory (22%) and the Neuropsychiatric Inventory (28%) -- suggesting that these instruments may not be ideal for clinical trial use. It was feasible to recruit FTLD patients in a simulated multi-centre trial. There are several candidate outcome measures -- including the FTLD-CDR and the cognitive composites -- that could be used in clinical trials across the spectrum of FTLD.