Non-invasive Imaging Biomarkers Research Articles

Amide Proton Transfer-Weighted Imaging Detects Hippocampal Proteostasis Disturbance Induced by Sleep Deprivation at 7.0 T MRI.

Sleep deprivation leads to hippocampal injury. Proteostasis disturbance is an important mechanism linking sleep deprivation and hippocampal injury. However, identifying noninvasive imaging biomarkers for hippocampal proteostasis disturbance remains challenging. Amide proton transfer-weighted (APTw) imaging is a chemical exchange saturation transfer technique based on the amide protons in proteins and peptides. We aimed to explore the ability of APTw imaging in detecting sleep deprivation-induced hippocampal proteostasis disturbance and its biological significance, as well as its biological basis. In vitro, the feasibility of APTw imaging in detecting changes of the protein state was evaluated, demonstrating that APTw imaging can detect alterations in the protein concentration, conformation, and aggregation state. In vivo, the hippocampal APTw signal declined with increased sleep deprivation time and was significantly lower in sleep-deprived rats than that in normal rats. This signal was positively correlated with the number of surviving neurons counted in Nissl staining and negatively correlated with the expression of glucose-regulated protein 78 evaluated in immunohistochemistry. Differentially expressed proteins in proteostasis network pathways were identified in the hippocampi of normal rats and sleep-deprived rats via mass spectrometry proteomics analysis, providing the biological basis for the change of the hippocampal APTw signal in sleep-deprived rats. These findings demonstrate that APTw imaging can detect hippocampal proteostasis disturbance induced by sleep deprivation and reflect the extent of neuronal injury and endoplasmic reticulum stress.

Visualizing diastolic failure: Non-invasive imaging-biomarkers in patients with heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction is an increasing challenge for modern day medicine and has been drawing more attention recently. Invasive right heart catheterization represents the mainstay for the diagnosis of diastolic dysfunction, however due to its attributable risk of an invasive procedure, other non-invasive clinical pathways are trying to approach this pathology in clinical practice. Diastolic failure is complex, and imaging is based on various parameters. In addition to transthoracic echocardiography, numerous novel imaging approaches, such as cardiac magnetic resonance imaging, computed tomography, positron emission (computed) tomography or single photon emission computed tomography techniques are being used to supplement deeper insights into causal pathology and might open targets for dedicated therapy options. This article provides insights into these sophisticated imaging techniques, their incremental value for the diagnosis of this poorly understood disease and recent promising results for an enhanced prognostication of outcome and therapy monitoring.

Association of lesion contour and lesion composition on MR with HER2 status in breast cancer brain metastases

Background and purposeWith the development of HER2-directed therapies, identifying non-invasive imaging biomarkers of HER2 status in breast cancer brain metastases has become increasingly important, particularly given the risks of tissue sampling within the brain and the possibility of a change in receptor expression from the primary tumor to the brain metastasis. The purpose of this study was to evaluate whether lesion contour and composition on MR could help identify the HER2 status of breast cancer brain metastases. Materials and methodsWe derived a cohort of 34 women with a mean age of 55 years (range: 31–81 years) with a total of 47 distinct histologically proven breast cancer brain metastases with preoperative contrast-enhanced brain MR and HER2 immunohistochemistry and/or fluorescent in-situ hybridization (FISH) of the resected/biopsied brain specimens from 2018 to 2021. Two fellowship-trained neuroradiologists evaluated the lesion contour and lesion composition of each lesion. Logistic regression analyses were performed. ResultsIn a logistic regression model, an irregular contour had an odds ratio of 170 (p = 0.007) in differentiating HER2-positive from HER2-negative lesions. In a logistic regression model, when compared to a predominantly cystic lesion composition, a solid lesion composition had an odds ratio of 17 (p = 0.016) in differentiating HER2-positive from HER2-negative lesions. ConclusionLesion contour and lesion composition on MR were significantly associated with the HER2 status of breast cancer brain metastases. Current assessment of HER2 status requires tissue sampling and immunochemical and/or FISH analyses. A non-invasive imaging biomarker that may help predict HER2 status may be of great clinical value.

Does hypointense HCC in the Hepatobiliary Phase at Gadoxetate-Enhanced MRI Predict Recurrence After Surgery? A Systematic Review and Meta-analysis

The aim of the current study was to investigate whether hypointense hepatocellular carcinoma (HCC) in the hepatobiliary phase (HBP) on gadoxetic acid-enhanced MRI at pretreatment is a potential prognostic marker for tumor recurrence within 3 years after surgery conducted for a curative purpose (resection or liver transplantation). Systematic review was performed in the PubMed, Embase, Cochrane Library, and LILACS databases. Original articles focused on evaluating HCC signal intensity (SI) in HBP, as well as recurrence at least 3 years after surgery were included in the study. Odds ratio (OR) was measured based on the inverse variance method and the random-effects model. The Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the included articles. Five studies with 718 patients, in total, were analyzed. The odds ratio of disease recurrence in patients with hypointense HCC in the HBP, within 3 years after surgery, was 3.12 times higher than that observed in patients with hyperintense HCC in the HBP (OR 3.12; 95% CI 1.27-7.68; p=0.01). Heterogeneity was classified as intermediate (I2=52%). Articles included in the review overall presented a low risk of bias. Hypointense HCC in the HBP on gadoxetic acid-enhanced MRI at pretreatment has increased the likelihood of tumor recurrence in patients subjected to resection or liver transplantation. HCC SI in the HBP is a potential non-invasive imaging biomarker associated with patient prognosis.

Coronary CT angiography derived pericoronary inflammation and bespoke cardiovascular risk prediction in the lipid clinic: beyond the calcium score

Abstract Background Dyslipidaemia promotes atherosclerosis. Genetic dyslipidaemias, Familial Hypercholesterolaemia (FH) being the most common, are associated with heightened risk of coronary artery disease (CAD) and premature major adverse cardiovascular events (MACE). However, this risk is both heterogeneous and modifiable with treatment. CT coronary imaging can identify subclinical atherosclerosis, enabling personalised risk stratification and treatment targets. Coronary artery calcium scoring (CACS) is current first-line in European guidelines for asymptomatic patients. However, calcification occurs late in CAD pathogenesis and CACS has low specificity in young patients with severe FH. CT coronary angiography (CTCA) assesses non-calcific plaque and high-risk plaque (HRP) features unappreciable with CACS. Additionally, the pericoronary fat attenuation index (FAI) measures inflammation on routine CTCA and is the strongest non-invasive imaging biomarker of risk of fatal MI. Purpose To quantify and compare the reclassification of subclinical atherosclerosis burden in Lipid Clinic patients assessed via CACS vs CTCA with FAI analysis. Methods Analysis of a prospectively maintained clinical database of asymptomatic Lipid Clinic patients with both CACS and CTCA imaging from May 2019 to December 2020. CACS was reported with the standardised Agastston criteria and compared with (i) the CTCA-derived Coronary Artery Disease – Reporting and Data System (CAD RADS) grading of anatomical stenosis, including a modifier for HRP features, and (ii) FAI analysis. Significance was defined as two-tailed p75th percentile vs age and sex matched controls) was seen in 6/27 (22%) patients with none to mild calcification on CACS and 6/28 (21%) patients with none to mild CAD on CTCA, of whom 3/7 (43%) had HRP. High FAI was seen in all groups of calcification severity and CAD RADS score (Figure 2). The proportion with high FAI was higher in CAD RADS 0 vs CAD RADS 4, and CACS severity was not associated with level of inflammation (p=0.94). High FAI was observed in 16% of patients on treatment, suggesting treatment failure. This included patients with CACS ranging from 0 to severe (>400), and CAD RADS of minimal (1) to severe (4). Conclusion CTCA re-stratifies CAD presence and severity vs CACS in a high-risk, asymptomatic patient group and identified a high proportion of patients with HRP features. FAI provides incremental value in identification of patients at risk of future MACE regardless of CACS grade, including patients without evidence of overt CAD. Identifying high FAI on treatment may imply treatment failure. Funding Acknowledgement Type of funding sources: None.

Constructing custom-made radiotranscriptomic signatures from CT angiograms: an application in COVID-19 vascular inflammation

Abstract Background Advances in computational methodologies have enabled processing of large datasets originating from imaging studies. However, most imaging biomarkers suffer from a lack of direct links with underlying biology, as they are only observationally correlated with pathophysiology. Purpose To develop and validate a novel AI-assisted image analysis platform, by applying quantitative radiotranscriptomics that quantifies cytokine-driven vascular inflammation from routine CT angiograms (CTA) performed as part of clinical care in COVID-19. Methods We used this platform to train the radiotranscriptomic signature C19-RS, derived from the perivascular space around the aorta and the internal mammary artery in routine chest CTAs, to best describe cytokine-driven vascular inflammation, defined using transcriptomic profiles from RNA sequencing data from human arterial biopsies (A). This signature was validated externally in 358 clinically indicated CT pulmonary angiograms from patients with or without COVID-19 from 3 different geographical regions. Results First, 22 patients who had a CTA before the pandemic underwent repeat CTA <6 months post COVID-19 infection (B). Compared with 22 controls (matched for age, gender, and BMI) C19-RS was increased only in the COVID-19 group (C). Next, C19-RS was calculated in a cohort of 331 patients hospitalised during the pandemic, and was higher in COVID-19 positives (adjusted OR=2.97 [95% CI: 1.43–6.27], p=0.004, D). C19-RS had prognostic value for in-hospital mortality in COVID-19, with HR=3.31 ([95% CI: 1.49–7.33], p=0.003) and 2.58 ([95% CI: 1.10–6.05], p=0.028) in two testing cohorts respectively (E, F), adjusted for clinical factors and biochemical biomarkers of inflammation and myocardial injury. The corrected HR for in-hospital mortality was 8.24 [95% CI: 2.16–31.36], p=0.002 for those who received no treatment with dexamethasone, but only 2.27 [95% CI: 0.69–7.55], p=0.18 in those who received dexamethasone subsequently to the C19-RS based image analysis, suggesting that vascular inflammation may have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0.61, p=0.0003) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways. Conclusion We present the first proof of concept study that combines transcriptomics with radiomics to provide a platform for the development of machine learning derived radiotranscriptomics analysis of routine clinical CT scans for the development of non-invasive imaging biomarkers. Application in COVID-19 produced C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation, that predicts in-hospital mortality and identifies people who will have better response to anti-inflammatory treatments, allowing targeted therapy. This AI-assisted image analysis platform may have applications across a wide range of vascular diseases, from infections to autoimmune diseases. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): EPSRC, British Heart Foundation, NIHR

Reproducibility of lung perfusion and ventilation derived using phase-resolved functional lung (PREFUL) MRI imaging

Abstract Background There is a growing demand for non-invasive imaging biomarkers of lung function, allowing more sensitive and timely decision making to achieve better patient outcomes. Recently developed Fourier decomposition MRI methods enable quantitative assessment of lung performance. Objectives To assess intra- and inter-observer agreement of lung perfusion and ventilation derived using the free-breathing, contrast agent-free, phase-resolved functional lung (PREFUL) imaging. Methods Fifty patients with acute ST-segment elevation myocardial infarction underwent CMR imaging using a 3 T MRI scanner (MAGNETOM Skyra). Coronal images were acquired during free breathing, covering the entire lung using a phase-resolved functional lung 2D FLASH sequence. The main scan parameters were: temporal resolution = 2.1 ms, number of measurements = 250. The lung perfusion and ventilation maps were generated (Figure 1). The analysis of the images was performed using prototype software (MR Lung Prototype version 2.0) by two independent observers. Analysis was repeated after four weeks to assess intra-observer agreement. Results PREFUL imaging and analysis were fast with a 35 second scan time and up to one minute post-processing time for a complete quantitative assessment. The lung perfusion and ventilation demonstrated excellent intra-observer (ICC 0.928 (0.870 to 0.959) and 0.994 (0.990 to 0.997) for mean perfusion and ventilation respectively) and inter-observer agreement (ICC 0.966 (0.940 to 0.981) and 0.992 (0.985 to 0.996) for mean perfusion and ventilation respectively). Bland-Altman plots demonstrate intra- and inter-observer reproducibility for lung perfusion and ventilation (Figure 2). Conclusion The lung perfusion and ventilation derived using phase-resolved functional lung (PREFUL) MRI imaging are highly reproducible. The Fourier decomposition method may allow fast quantitative evaluation of lung function. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Hospital of Lithuanian University of Health Sciences

ATRX status in patients with gliomas: Radiomics analysis.

The aim of this study was to develop a noninvasive radiomics analysis model based on preoperative multiparameter MRI to predict the status of the biomarker alpha thalassemia/mental retardation X-linked syndrome (ATRX) in glioma noninvasively.Material and methods:A cohort of 123 patients diagnosed with gliomas (World Health Organization grades II–IV) who underwent surgery and was treated at our center between January 2016 and July 2020, was enrolled in this retrospective study. Radiomics features were extracted from MR T1WI, T2WI, T2FLAIR, CE-T1WI, and ADC images. Patients were randomly split into training and validation sets at a ratio of 4:1. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) to train the SVM model using the training set. The prediction accuracy and area under curve and other evaluation indexes were used to explore the performance of the model established in this study for predicting the ATRX mutation state.Results:Fifteen radiomic features were selected to generate an ATRX-associated radiomic signature using the LASSO logistic regression model. The area under curve for ATRX mutation (ATRX(−)) on training set was 0.93 (95% confidence interval [CI]: 0.87–1.0), with the sensitivity, specificity and accuracy being 0.91, 0.82 and 0.88, while on the validation set were 0.84 (95% CI: 0.63–0.91), with the sensitivity, specificity and accuracy of 0.73, 0.86, and 0.79, respectively.Conclusions:These results indicate that radiomic features derived from preoperative MRI facilitat efficient prediction of ATRX status in gliomas, thus providing a novel evaluation method for noninvasive imaging biomarkers.

Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology.

Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.

Characterization of lipomatous tumors with high-resolution 1H MRS at 17.6T: Do benign lipomas, atypical lipomatous tumors and liposarcomas have a distinct metabolic signature?

BackgroundDistinguishing between some benign lipomas (BLs), atypical lipomatous tumors (ALTs), and dedifferentiated liposarcomas (DDLs) can be challenging due to overlapping magnetic resonance imaging characteristics, and poorly understood molecular mechanisms underlying the malignant transformation of liposarcomas.PurposeTo identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution 1H magnetic resonance spectroscopy (MRS).Materials and methodsIn this prospective study, human tissue specimens were obtained from participants who underwent surgical resection for radiologically-indeterminate lipomatous tumors between November 2016 and May 2019. Tissue specimens were obtained from normal subcutaneous fat (n=9), BLs (n=10), ALTs (n=7) and DDLs (n=8). Extracts from specimens were examined with high-resolution MRS at 17.6T. Computational modeling of pattern recognition-based cluster analysis was utilized to identify significant differences in metabolic signatures between the lipomatous tumor types.ResultsSignificant differences between BLs and ALTs were observed for multiple metabolites, including leucine, valine, branched chain amino acids, alanine, acetate, glutamine, and formate. DDLs were distinguished from ALTs by increased glucose and lactate, and increased phosphatidylcholine. Multivariate principal component analysis showed clear clustering identifying distinct metabolic signatures of the tissue types.ConclusionMetabolic signatures identified in 1H MR spectra of lipomatous tumors provide new insights into malignant progression and metabolic targeting. The metabolic patterns identified provide the foundation of developing noninvasive MRS or PET imaging biomarkers to distinguish between BLs, ALTs, and DDLs.

Relationship between Coronary Arterial Geometry and the Presence and Extend of Atherosclerotic Plaque Burden: A Review Discussing Methodology and Findings in the Era of Cardiac Computed Tomography Angiography.

Coronary artery disease (CAD) represents a modern pandemic associated with significant morbidity and mortality. The multi-faceted pathogenesis of this entity has long been investigated, highlighting the contribution of systemic factors such as hyperlipidemia and hypertension. Nevertheless, recent research has drawn attention to the importance of geometrical features of coronary vasculature on the complexity and vulnerability of coronary atherosclerosis. Various parameters have been investigated so far, including vessel-length, coronary artery volume index, cross-sectional area, curvature, and tortuosity, using primarily invasive coronary angiography (ICA) and recently non-invasive cardiac computed tomography angiography (CCTA). It is clear that there is correlation between geometrical parameters and both the haemodynamic alterations augmenting the atherosclerosis-prone environment and the extent of plaque burden. The purpose of this review is to discuss the currently available literature regarding this issue and propose a potential non-invasive imaging biomarker, the geometric risk score, which could be of importance to allow the early detection of individuals at increased risk of developing CAD.

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19

BackgroundDirect evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19.MethodsFor this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes.FindingsPatients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43–6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17–3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [<6·99] C19-RS; hazard ratio [HR] 3·31 [95% CI 1·49–7·33], p=0·0033; and 2·58 [1·10–6·05], p=0·028), adjusted for clinical factors, biochemical biomarkers of inflammation and myocardial injury, and technical parameters. The adjusted HR for in-hospital mortality was 8·24 (95% CI 2·16–31·36, p=0·0019) in patients who received no dexamethasone treatment, but 2·27 (0·69–7·55, p=0·18) in those who received dexamethasone after the scan, suggesting that vascular inflammation might have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0·61, p=0·00031) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways.InterpretationRadiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy.FundingEngineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation.

Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma.

Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score). We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint. The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively. In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor). • The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

Macrophages in close proximity to the vitreoretinal interface are potential biomarkers of inflammation during retinal vascular disease

BackgroundDiabetic retinopathy and retinal vein occlusion are vision threatening retinal vascular diseases. Current first-line therapy targets the vascular component, but many patients are treatment-resistant due to unchecked inflammation. Non-invasive inflammatory imaging biomarkers are a significant unmet clinical need for patients. Imaging of macrophage-like cells on the surface of the retina using clinical optical coherence tomography (OCT) is an emerging field. These cells are increased in patients with retinal vascular disease, and could be a potential inflammatory biomarker. However, since OCT is limited by an axial resolution of 5–10 microns, the exact location and identity of these retinal cells is currently unknown.MethodsWe performed OCT followed by confocal immunofluorescence in wild-type mice to identify macrophages within 5–10 microns of the vitreoretinal interface. Next, we used Cx3cr1CreER/+; Rosa26zsGreen/+ mice to fate map retinal surface macrophages. Using confocal immunofluorescence of retinal sections and flatmounts, we quantified IBA1+Tmem119+CD169neg microglia, IBA1+Tmem119negCD169neg perivascular macrophages, and IBA1+Tmem119negCD169+ vitreal hyalocytes. Finally, we modeled neuroinflammation with CCL2 treatment and characterized retinal surface macrophages using flow cytometry, OCT, and confocal immunofluorescence.ResultsWe were able to detect IBA1+ macrophages within 5–10 microns of the vitreoretinal interface in wild-type mice using OCT followed by confirmatory confocal immunofluorescence. Retinal surface macrophages were 83.5% GFP+ at Week 1 and 82.4% GFP+ at Week 4 using fate mapping mice. At steady state, these macrophages included 82% IBA1+Tmem119+CD169neg microglia, 9% IBA1+Tmem119negCD169+ vitreal hyalocytes, and 9% IBA1+Tmem119negCD169neg perivascular macrophages. After CCL2-driven neuroinflammation, many Ly6C+ cells were detectable on the retinal surface using OCT followed by confocal immunofluorescence.ConclusionsMacrophages within close proximity to the vitreoretinal interface are self-renewing cells, and predominantly microglia with minor populations of perivascular macrophages and vitreal hyalocytes at steady state. In the context of neuroinflammation, monocytes and monocyte-derived macrophages are a significant component of retinal surface macrophages. Human OCT-based imaging of retinal surface macrophages is a potential biomarker for inflammation during retinal vascular disease.

CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review.

The histopathological growth patterns (HGPs) of liver metastases (LMs) are independently associated with the long-term prognosis of the primary tumor, with different HGPs predicting different patient outcomes and clinical treatment decisions. Non-invasive imaging biomarkers for stratification of HGPs are beneficial for treatment monitoring, evaluation of efficacy, and prognosis prediction of LMs. This review describes the state of research regarding computed tomography (CT), magnetic resonance imaging (MRI), and radiomics imaging biomarkers for LM-HGPs; discusses the advantages of CT, MRI, and radiomics for classification of LM-HGPs; and provides a reference for the stratification of LM-HGPs. Finally, the difficulties and deficiencies of CT, MRI, and radiomics in LM-HGP research are summarized along with the proposed directions for future research.

Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions.

Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promisesto direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.

Preclinical evaluation of [18F]FDG-PET as a biomarker of lymphoid tissue disease and inflammation in Zika virus infection

PurposeZika (ZIKV) is a viral inflammatory disease affecting adults, children, and developing fetuses. It is endemic to tropical and sub-tropical countries, resulting in half the global population at risk of infection. Despite this, there are no approved therapies or vaccines against ZIKV disease. Non-invasive imaging biomarkers are potentially valuable tools for studying viral pathogenesis, prognosticating host response to disease, and evaluating in vivo efficacy of experimental therapeutic interventions. In this study, we evaluated [18F]fluorodeoxyglucose ([18F]FDG)-positron emission tomography (PET) as an imaging biomarker of ZIKV disease in a mouse model and correlated metabolic tracer tissue uptake with real-time biochemical, virological, and inflammatory features of tissue infection.Methods[18F]FDG-PET/CT imaging was performed in an acute, lethal ZIKV mouse infection model, at increasing stages of disease severity. [18F]FDG-PET findings were corroborated with ex vivo wholemount-tissue autoradiography and tracer biodistribution studies. Tracer uptake was also correlated with in situ tissue disease status, including viral burden and inflammatory response. Immune profiling of the spleen by flow cytometry was performed to identify the immune cell subsets driving tissue pathology and enhancing tracer uptake in ZIKV disease.ResultsFoci of increased [18F]FDG uptake were consistently detected in lymphoid tissues—particularly the spleen—of ZIKV-infected animals. Splenic uptake increased with disease severity, and corroborated findings in tissue pathology. Increased splenic uptake also correlated with increased viral replication and elevated expression of pro-inflammatory cytokines within these tissues. ZIKV-infected spleens were characterized by increased infiltration of myeloid cells, as well as increased proliferation of both myeloid and lymphoid cells. The increased cell proliferation correlated with increased tracer uptake in the spleen. Our findings support the use of [18F]FDG as an imaging biomarker to detect and track ZIKV disease in real time and highlight the dependency of affected tissue on the nature of the viral infection.Conclusion[18F]FDG uptake in the spleen is a useful surrogate for interrogating in situ tissue viral burden and inflammation status in this ZIKV murine model.

Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer: The Role of Conventional Ultrasound and Contrast-Enhanced Ultrasound.

Tumor-infiltrating lymphocytes (TILs) have emerged as an efficient biomarker predicting treatment response and prognosis of breast cancer (BC). This study aimed to evaluate the association between conventional ultrasound and contrast-enhanced ultrasound (CEUS) imaging features with TIL levels in invasive BC patients. We retrospectively included 267 women with invasive BC who had undergone conventional ultrasound and CEUS. Patients were divided into low (≤10%) and high (>10%) TIL groups. Conventional ultrasound and CEUS features were analyzed by two sonographers. The associations between the TIL levels and imaging features were evaluated. Of the 267 patients, 122 with high TILs and 145 with low TIL levels. High TIL tumors were more likely to have a circumscribed margin, oval or round shape, and enhanced posterior echoes on ultrasonography (p < 0.05). In contrast, low TIL tumors were more likely to have an irregular shape, un-circumscribed, indistinct and spiculated margin (p < 0.05). In CEUS, high TIL tumors showed a more regular shape, clearer margin, more homogeneous enhancement and higher peak intensity (PI) value (p < 0.05). Logistic analysis indicated that shape, posterior features, PI, and enhanced homogeneity were independent predictors for high TIL tumors. The model combined the four independent predictors have a moderate performance in predicting high TIL tumors with AUC 0.79, sensitivity 0.72, and specificity 0.78. Conventional ultrasound and CEUS features were associated with TIL levels in invasive BC. Consequently, the results suggested that preoperative conventional ultrasound and CEUS may be a useful noninvasive imaging biomarker for individualized treatment decisions.

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease.

Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of the anatomical variations in the Circle of Willis (CoW) andvascular morphology in healthy aging, acute ischemic stroke (AIS) and Alzheimer's Disease (AD). We used our novel automatic method to segment and extract geometric features of the cerebral vasculature from MR angiography scans of 175 healthy subjects, which were used to create a probabilistic atlas of cerebrovasculature and to study normal aging and intersubject variations in CoW anatomy. Subsequently, we quantified and analyzed vascular alterations in 45AIS and 50 AD patients, two prominent cerebrovascular and neurodegenerative disorders. In the sampled cohort, we determined that the CoW is fully formed in only 35% of healthy adults and found significantly (p&lt;.05) increased tortuosity and fractality, with increasing age and also with disease in both AIS and AD. We also found significantly lower vessel length, volume, and number of branches in AIS patients, as expected. The AD cerebral vessels exhibited significantly smaller diameter and more complex branching patterns, compared to age-matched healthy adults. These changes were significantly heightened (p&lt;.05) among healthy, early onset mild AD, and moderate/severe dementia groups. Although our study does not include longitudinal data due to paucity of such datasets, the specific geometric features and quantitative comparisons demonstrate the potential for using vascular morphology as a noninvasive imaging biomarker for neurologic disorders.

Prognostic predictors of non-small cell lung cancer treated with curative resection: the role of preoperative CT texture features, clinical features, and laboratory parameters

To explore the value of preoperative contrast-enhanced computed tomography (CT) tumour texture characteristics, and clinical and laboratory parameters on the prognosis of curative resection for non-small-cell lung cancer (NSCLC). This retrospective study included 64 patients (34 men and 30 women) with NSCLC who underwent curative resection and were then followed up for 5 years or until death. Preoperative contrast-enhanced CT images, clinical features, and laboratory parameters were collected for these patients. CT texture features of the primary tumour before surgery were extracted from the contrast-enhanced CT images using ImageJ software. Based on the cut-off values determined by X-tile software, the preoperative CT texture features, clinical features, and laboratory parameters were divided into two groups. Kaplan-Meier survival curves and log-rank tests were used to compare the 5-year overall survival (OS) of patients. Multivariate Cox regression analysis was used to determine the independent factors influencing the prognosis. The mean survival was 51.5 months. Tumour volume, entropy, platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), and albumin-to-globulin ratio (AGR) were shown to be significantly associated with 5-year OS (p<0.05). Multivariate Cox regression analysis revealed that entropy was the independent factor of prognosis (hazard ratio 4.375, 95% confidence interval [CI]: 1.646-11.620, p=0.003). Entropy is an important and potentially non-invasive imaging biomarker for predicting the prognosis of NSCLC undergoing curative resection.