Quantitative CT of Lung in Healthy Koreans :Evaluation with PULMO-CT

Rationale. Quantitative CT (QCT) bone densitometry with asynchronous calibration not require a phantom during the scan procedure. Based on calibration data it converts X-ray density in HU to bone mineral density (BMD). Given the large number of CT studies performed on patients at risk of osteoporosis, there is a need for a hands-on method capable of assessing BMD in a short period of time without tailored software or protocols.Goal. To develop a method for QCT bone densitometry using an PHK (PHantom Kalium), to compare the volume BMD measurements with the QCT data with asynchronous calibration provided by software from a reputable developer.Methods. The studies were performed at 64-slice CT unit with body scanning parameters. The BMD was measured using two techniques: 1) QCT with asynchronous calibration using software from a reputable developer; 2) QCT using a PHK phantom (QCT-PHK). For convert the HU to BMD values, we scanned the PHK phantom and calculate correction factor. Phantom contains “vertebrae” filled with potassium hydrogen phosphate in different concentrations. In both methods, the BMD values measured for LI–II, and sometimes for ThXII, LIII.Results. The study enrolled 65 subjects (11 male and 54 female patients); median age 69.0 years. A comparison of the vertebrae BMD measured by QCT and QCT-PHK revealed a significant linear Pearson correlation r = 0.977 (p < 0.05). The Bland–Altman analysis demonstrated a lack of relationship between the difference in measurements and the average BMD and a systematic BMD; bias of +4.50 mg/ml in QCT vs. QCT-PHK. Differences in the division into groups osteoporosis / osteopenia / norm according to the ACR criteria for the two methods were not significant.Conclusion. The developed asynchronous QCT-PHK method measure BMD comparable to the widely used QCT with asynchronous calibration. This method can be used for opportunistic screening for osteoporosis.

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The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostics ◽

10.3390/diagnostics11081317 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1317

Author(s):

Maria Elena Laino ◽

Angela Ammirabile ◽

Alessandro Posa ◽

Pierandrea Cancian ◽

Sherif Shalaby ◽

...

Keyword(s):

Artificial Intelligence ◽

Literature Review ◽

Diagnostic Imaging ◽

Clinical Work ◽

Rapid Expansion ◽

Chest Imaging ◽

Imaging Data ◽

Prognostic Information ◽

Quantitative Ct ◽

Work Up

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

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Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study

Technology and Health Care ◽

10.3233/thc-218027 ◽

2021 ◽

Vol 29 ◽

pp. 297-309

Author(s):

Xiaohui Chen ◽

Wenbo Sun ◽

Dan Xu ◽

Jiaojiao Ma ◽

Feng Xiao ◽

...

Keyword(s):

Lung Diseases ◽

Early Stage ◽

Temporal Changes ◽

Time Interval ◽

Lesion Area ◽

Quantitative Ct ◽

Diagnosis And Prognosis ◽

Ct Findings ◽

Stage 4 ◽

Stage 1

BACKGROUND: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases. OBJECTIVE: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results. METHODS: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups. RESULTS: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups. CONCLUSIONS: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

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Quantitative CT‐Based Methods for Bone Microstructural Measures and Their Relationships With Vertebral Fractures in a Pilot Study on Smokers

JBMR Plus ◽

10.1002/jbm4.10484 ◽

2021 ◽

Author(s):

Xiaoliu Zhang ◽

Alejandro P Comellas ◽

Elizabeth A Regan ◽

Indranil Guha ◽

Amal Shibli‐Rahhal ◽

...

Keyword(s):

Pilot Study ◽

Vertebral Fractures ◽

Quantitative Ct

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Effects of Surface Protein Adsorption on the Distribution and Retention of Intratumorally Administered Gold Nanoparticles

Pharmaceutics ◽

10.3390/pharmaceutics13020216 ◽

2021 ◽

Vol 13 (2) ◽

pp. 216

Author(s):

Rossana Terracciano ◽

Aobo Zhang ◽

E. Brian Butler ◽

Danilo Demarchi ◽

Jason H. Hafner ◽

...

Keyword(s):

Treatment Modalities ◽

Emission Spectrometry ◽

Limiting Factor ◽

High Resolution Computed Tomography ◽

Quantitative Ct ◽

Heterogeneous Distribution ◽

Significant Difference ◽

Intratumoral Distribution

The heterogeneous distribution of delivery or treatment modalities within the tumor mass is a crucial limiting factor for a vast range of theranostic applications. Understanding the interactions between a nanomaterial and the tumor microenvironment will help to overcome challenges associated with tumor heterogeneity, as well as the clinical translation of nanotheranostic materials. This study aims to evaluate the influence of protein surface adsorption on gold nanoparticle (GNP) biodistribution using high-resolution computed tomography (CT) preclinical imaging in C57BL/6 mice harboring Lewis lung carcinoma (LLC) tumors. LLC provides a valuable model for study due to its highly heterogenous nature, which makes drug delivery to the tumor challenging. By controlling the adsorption of proteins on the GNP surface, we hypothesize that we can influence the intratumoral distribution pattern and particle retention. We performed an in vitro study to evaluate the uptake of GNPs by LLC cells and an in vivo study to assess and quantify the GNP biodistribution by injecting concentrated GNPs citrate-stabilized or passivated with bovine serum albumin (BSA) intratumorally into LLC solid tumors. Quantitative CT and inductively coupled plasma optical emission spectrometry (ICP-OES) results both confirm the presence of particles in the tumor 9 days post-injection (n = 8 mice/group). A significant difference is highlighted between citrate-GNP and BSA-GNP groups (** p < 0.005, Tukey’s multiple comparisons test), confirming that the protein corona of GNPs modifies intratumoral distribution and retention of the particles. In conclusion, our investigations show that the surface passivation of GNPs influences the mechanism of cellular uptake and intratumoral distribution in vivo, highlighting the spatial heterogeneity of the solid tumor.

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MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis

Thorax ◽

10.1136/thoraxjnl-2018-212430 ◽

2019 ◽

Vol 74 (12) ◽

pp. 1131-1139 ◽

Cited By ~ 10

Author(s):

Susan K Mathai ◽

Stephen Humphries ◽

Jonathan A Kropski ◽

Timothy S Blackwell ◽

Julia Powers ◽

...

Keyword(s):

Deep Learning ◽

Pulmonary Fibrosis ◽

Learning Algorithm ◽

Quantitative Ct ◽

First Degree Relatives ◽

Deep Learning Algorithm ◽

Increased Risk ◽

Ct Evaluation ◽

Ct Analysis ◽

Quantitative Ct Analysis

BackgroundRelatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT.MethodsFirst-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed.FindingsIn 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex.InterpretationPrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities.

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