Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: There is increasing interest in the role of lipids in processes that modulate lung fibrosis with evidence of lipid deposition in idiopathic pulmonary fibrosis (IPF) histological specimens. The aim of this study was to identify measurable markers of pulmonary lipid that may have utility as IPF biomarkers. STUDY DESIGN AND METHODS: IPF and control lung biopsy specimens were analysed using a unbiased lipidomic approach. Pulmonary fat attenuation volume (PFAV) was assessed on chest CT images (CTPFAV ) with 3D semi-automated lung density software. Aerated lung was semi-automatically segmented and CTPFAV calculated using a Hounsfield-unit (-40 to -200HU) threshold range expressed as a percentage of total lung volume. CTPFAV was compared to pulmonary function, serum lipids and qualitative CT fibrosis scores. RESULTS: There was a significant increase in total lipid content on histological analysis of IPF lung tissue (23.16 nmol/mg) compared to controls (18.66 mol/mg, p = 0.0317). The median CTPFAV in IPF was higher than controls (1.34% vs. 0.72%, p < 0.001) and CTPFAV correlated significantly with DLCO% predicted (R2 = 0.356, p < 0.0001) and FVC% predicted (R2 = 0.407, p < 0.0001) in patients with IPF. CTPFAV correlated with CT features of fibrosis; higher CTPFAV was associated with >10% reticulation (1.6% vs. 0.94%, p = 0.0017) and >10% honeycombing (1.87% vs. 1.12%, p = 0.0003). CTPFAV showed no correlation with serum lipids. CONCLUSION: CTPFAV is an easily quantifiable non-invasive measure of pulmonary lipids. In this pilot study, CTPFAV correlates with pulmonary function and radiological features of IPF and could function as a potential biomarker for IPF disease severity assessment.

iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning.

PURPOSE: This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy. MATERIALS AND METHODS: This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN. RESULTS: 812 patients were initially included (397 female, average age 73), 63 for external validation. The best performing clinical score and ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967, 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was found overall and our XGB model was more accurate than SPAN (p < 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤2B and 3 points for ≥3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than our XGB model (p > 0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively. CONCLUSION: iSPAN incorporates machine-derived features to achieve better predictions compared to existing clinical scores. It is not inferior to our XGB model and is externally generalisable.

Combined autologous blood patch-immediate patient rollover does not reduce the pneumothorax or chest drain rate following CT-guided lung biopsy compared to immediate patient rollover alone.

PUPROSE: The purpose of this study was to evaluate a combined autologous blood-patch (ABP)-immediate patient rollover (IPR) technique compared with the IPR technique alone on the incidence of pneumothorax and chest drainage following CT-guided lung biopsy. METHODS: In this interventional cohort study of both prospectively and retrospectively acquired data, 652 patients underwent CT-guided lung biopsy. Patient demographics, lesion characteristics and technical biopsy variables including the combined ABP-IPR versus IPR alone were evaluated as predictors of pneumothorax and chest drain rates using regression analysis. RESULTS: The combined ABP-IPR technique was performed in 259 (39.7 %) patients whilst 393 (60.3 %) underwent IPR alone. There was no significant difference in pneumothorax rate or chest drains required between the combined ABP-IPR vs IPR groups (p =.08, p =.60 respectively). Predictors of pneumothorax adjusted for the combined ABP-IPR and IPR alone groups included age (p =.02), lesion size (p =.01), location (p =.005), patient position (p =.008), emphysema along the needle track (p =.005) and lesion distance from the pleura (p =.02). Adjusted predictors of chest drain insertion included lesion location (p =.09), patient position (p =.002), bullae crossed (p =.02) and lesion distance from the pleura (p =.02). CONCLUSION: The combined ABP-IPR technique does not reduce the pneumothorax or chest drain rate compared to the IPR technique alone. Utilising IPR without an ABP following CT-guided lung biopsy results in similar pneumothorax and chest drain rates while minimising the potential risk of systemic air embolism.

Quantitative Airway Assessment of Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH) on CT as a Novel Biomarker.

Objectives: Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia (DIPNECH) occurs due to abnormal proliferation of pulmonary neuroendocrine cells. We hypothesized that performing a quantitative analysis of airway features on chest CT may reveal differences to matched controls, which could ultimately help provide an imaging biomarker. Methods: A retrospective quantitative analysis of chest CTs in patients with DIPNECH and age matched controls was carried out using semi-automated post-processing software. Paired segmental airway and artery diameters were measured for each bronchopulmonary segment, and the airway:artery (AA) ratio, airway wall thickness:artery ratio (AWTA ratio) and wall area percentage (WAP) calculated. Nodule number, size, shape and location was recorded. Correlation between CT measurements and pulmonary function testing was performed. Results: 16 DIPNECH and 16 control subjects were analysed (all female, mean age 61.7 +/− 11.8 years), a combined total of 425 bronchopulmonary segments. The mean AwtA ratio, AA ratio and WAP for the DIPNECH group was 0.57, 1.18 and 68.8%, respectively, compared with 0.38, 1.03 and 58.3% in controls (p < 0.001, <0.001, 0.03, respectively). DIPNECH patients had more nodules than controls (22.4 +/− 32.6 vs. 3.6 +/− 3.6, p = 0.03). AA ratio correlated with FVC (R2 = 0.47, p = 0.02). A multivariable model incorporating nodule number, AA ratio and AWTA-ratio demonstrated good performance for discriminating DIPNECH and controls (AUC 0.971; 95% CI: 0.925−1.0). Conclusions: Quantitative CT airway analysis in patients with DIPNECH demonstrates increased airway wall thickness and airway:artery ratio compared to controls. Advances in knowledge: Quantitative CT measurement of airway wall thickening offers a potential imaging biomarker for treatment response.

PET/CT Variants and Pitfalls in Head and Neck Cancers Including Thyroid Cancer.

PET/CT imaging is a dual-modality diagnostic technology that merges metabolic and structural imaging. There are several currently available radiotracers, but 18F-FDG is the most commonly utilized due to its widespread availability. 18F-FDG PET/CT is a cornerstone of head and neck squamous cell carcinoma imaging. 68Ga-DOTA-TOC is another widely used radiotracer. It allows for whole-body imaging of cellular somatostatin receptors, commonly expressed by neuroendocrine tumors and is the standard of reference for the characterization and staging of neuroendocrine tumors. The normal biodistribution of these PET radiotracers as well as the technical aspects of image acquisition and inadequate patient preparation affect the quality of PET/CT imaging. In addition, normal variants, artifacts and incidental findings may impede accurate image interpretation and can potentially lead to misdiagnosis. In order to correctly interpret PET/CT imaging, it is necessary to have a comprehensive knowledge of the normal anatomy of the head and neck and to be cognizant of potential imaging pitfalls. The interpreter must be familiar with benign conditions which may accumulate radiotracer potentially mimicking neoplastic processes and also be aware of malignancies which can demonstrate low radiotracer uptake. Appropriate use of structural imaging with either CT, MR or ultrasound can serve a complimentary role in several head and neck pathologies including local tumor staging, detection of bone marrow involvement or perineural spread, and classification of thyroid nodules. It is important to be aware of the role of these complementary modalities to maximize diagnostic accuracy and patient outcomes. The purpose of this article is to outline the basic principles of PET/CT imaging, with a focus on 18F-FDG PET/CT and 68Ga-DOTA PET/CT. Basic physiology, variant imaging appearances and potential pitfalls of image interpretation are presented within the context of common use cases of PET technology in patients with head and neck cancers and other pathologies, benign and malignant.

Molecular and Anatomical Imaging of Dementia With Lewy Bodies and Frontotemporal Lobar Degeneration.

Dementia with Lewy bodies (DLB) and frontotemporal lobar degeneration (FTLD) are common causes of dementia. Early diagnosis of both conditions is challenging due to clinical and radiological overlap with other forms of dementia, particularly Alzheimer's disease (AD). Structural and functional imaging combined can aid differential diagnosis and help to discriminate DLB or FTLD from other forms of dementia. Imaging of DLB involves the use of 123I-FP-CIT SPECT and 123I-metaiodobenzylguanidine (123I-MIBG), both of which have an established role distinguishing DLB from AD. AD is also characterised by more pronounced atrophy of the medial temporal lobe structures when compared to DLB and these can be assessed at MR using the Medial Temporal Atrophy Scale. 18F-FDG-PET is used as a supportive biomarker for the diagnoses of DLB and can distinguish DLB from AD with high accuracy. Polysomnography and electroencephalography also have established roles in the diagnoses of DLB. FTLD is a heterogenous group of neurodegenerative disorders characterised pathologically by abnormally aggregated proteins. Clinical subtypes include behavioral variant FTD (bvFTD), primary progressive aphasia (PPA), which can be subdivided into semantic variant PPA (svPPA) or nonfluent agrammatic PPA (nfaPPA) and FTD associated with motor neuron disease (FTD-MND). Structural imaging is often the first step in making an image supported diagnoses of FTLD. Regional patterns of atrophy can be assessed on MR and graded according to the global cortical atrophy scale. FTLD is typically associated with atrophy of the frontal and temporal lobes. The patterns of atrophy are associated with the specific clinical subtypes, underlying neuropathology and genetic mutations although there is significant overlap. 18F-FDG-PET is useful for distinguishing FTLD from other forms of dementia and focal areas of hypometabolism can often precede atrophy identified on structural MR imaging. There are currently no biomarkers with which to unambiguously diagnose DLB or FTLD and both conditions demonstrate a wide range of heterogeneity. A combined approach of structural and functional imaging improves diagnostic accuracy in both conditions.

Higher Coronary Plaque Burden in Psoriatic Arthritis Is Independent of Metabolic Syndrome and Associated With Underlying Disease Severity.

OBJECTIVE: To examine the effect of metabolic syndrome and psoriatic disease-related variables on coronary plaque burden in psoriatic arthritis (PsA) patients. METHODS: Fifty PsA patients without symptoms of coronary artery disease (CAD) (25 with metabolic syndrome and 25 without metabolic syndrome) and 50 age- and sex-matched controls underwent 64-slice coronary computed tomography angiography. Plaque localization, segment involvement score (SIS), segment stenosis score (SSS), and total plaque volume (TPV) were calculated. Plaques were classified as calcified, mixed, or noncalcified. Kruskal-Wallis test, rank correlations, and linear regression analyses were used to study the relationship between PsA, metabolic syndrome, and plaque burden. RESULTS: Plaques were found in 76% of PsA patients versus 44% of controls (P = 0.001), and a higher proportion of patients with PsA had affected coronary vessels (P = 0.007). SIS, SSS, and TPV were greater in PsA patients than controls (P = 0.003, P = 0.001, and P ≤ 0.001, respectively). More PsA patients had mixed plaques, and mixed plaque volume was higher than in controls (P < 0.001). PsA patients with metabolic syndrome and those without metabolic syndrome had similar plaque burdens and types. SIS, SSS, and TPV did not show significant relationships with features of metabolic syndrome, but did significantly correlate with disease activity measures. TPV was associated with a diagnosis of PsA (B = 0.865, P = 0.008), but not with metabolic syndrome. Age, highest C-reactive protein level, highest swollen joint count, disease duration, and plasma glucose level were independent predictors of higher plaque burden in PsA. CONCLUSION: PsA is associated with accelerated coronary plaque formation, particularly mixed plaques, independent of metabolic disease. Psoriatic disease activity and severity may predict coronary plaque burden better than traditional risk factors.