Quantitative computed tomography applied to interstitial lung diseases

Obert, Martin; Kampschulte, Marian; Limburg, R; Barańczuk, Stefan; Krombach, Gabriele A.

doi:10.1016/j.ejrad.2018.01.018

Cited by 15 publications

(11 citation statements)

References 20 publications

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“…Obert et al applied CT histogram analysis to differentiate normal lungs from those affected by emphysema and fibrotic changes [ 15 ]. The facts that the sample size was small and different pathologies were analyzed hampered a side-by-side comparison with the data of the study at hand.…”

Section: Discussionmentioning

confidence: 99%

“…Density histograms were plotted with an interval of 1 HU and normalized to an area under the histogram curve of 1. As imaging biomarkers, the following standard histogram parameters were calculated for each lung [ 15 , 16 , 17 , 18 , 19 , 40 ]: mean lung attenuation, median lung attenuation, histogram standard deviation, skewness and kurtosis. For comprehensive parameter definitions, please refer to Supplement S2 .…”

Section: Methodsmentioning

confidence: 99%

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Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

et al. 2021

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CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600–0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600–0 HU] (r = 0.56, 95% CI = 0.46–0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

et al. 2021

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“…Recently, it has been observed that advanced computer algorithms-based CT analyses can better forecast survival from various fibrosing lung diseases in comparison to visual CT scores [13–15]. However, survival of CFA patients is strongly associated with quantification of pulmonary vessel-related structures (PVS) [15].…”

Section: Introductionmentioning

confidence: 99%

Association Between Pulmonary Vessel-Related Structures and Cryptogenic Fibrosing Alveolitis Using Derived Computed Tomography Among Chinese Patients

2019

Med Sci Monit

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Background The aim of this study was to assess the association between pulmonary vessel-related structures and cryptogenic fibrosing alveolitis (CFA) in a drug trial in a Chinese population using derived computed tomography (dCT) to evaluate functional reduction and survival. Material/Methods Discovery and validation cohorts were chosen separately by fulfilment of drug trial entry criteria, and we enrolled 269 and 292 consecutive patients, respectively. CFA patients who had undergone imaging based on volumetric non-contrast CT at our hospital were subjected to pulmonary vessel-related structure (PVS) measures and dCT to forecast mortality and reduction in reduced forced vital capacity of CFA. Results The best forecaster of survival and reduction in terms of reduced forced vital capacity were found to be the dCT-generated outcomes in terms of PVS scores. Patients having less extensive disease highlighted the dCT outcomes through outperformance of CFA measures. When we used the cohort enhancement device, we found reduction in the requisite sample size of a CFA drug trial by 31% with the use of more than 5.0% dCT PVS score. Conclusions We found an association between CFA and PVS using dCT and it is far better than the results achieved so far by use of criterion standard measures. Additionally, reduction in the restrictive trial costs was also achieved by using cohort enhancement in a CFA drug trial setting, as PVS scores forced us to decrease the size of required CFA drug trial population by 30%. Interestingly, patients who had to take antifibrotic medication for longer periods had longer survival and less decreases forced vital capacity, as identified by PVS scores.

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“…Lung segmentation reserves a significant place before important objectives like detection of pulmonary nodules, pulmonary vessels, cancer types and various diseases [1][2][3][4]. In medical area, computed tomography (CT) scans are frequently preferred to perform a diagnostic about aforementioned subjects.…”

Section: Introductionmentioning

confidence: 99%

Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

Koyuncu

2018

ijisae

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Lung imaging and computer aided diagnosis (CAD) play a critical role in detection of lung diseases. The most significant part of a lung based CAD is to fulfil the parenchyma segmentation, since disease information is kept in the parenchyma texture. For this purpose, parenchyma segmentation should be accurately performed to find the necessary diagnosis to be used in the treatment. Besides, lung parenchyma segmentation remains as a challenging task in computed tomography (CT) owing to the handicaps oriented with the imaging and nature of parenchyma. In this paper, a cascade framework involving histogram analysis, morphological operations, mean shift segmentation (MSS) and region growing (RG) is proposed to perform an accurate segmentation in thorax CT images. In training data, 20 axial CT images are utilized to define the optimum parameter values, and 150 images are considered as test data to objectively evaluate the performance of system. Five statistical metrics are handled to carry out the performance assessment, and a literature comparison is realized with the state-of-the-art techniques. As a result, parenchyma tissues are segmented with success rates as 98.07% (sensitivity), 99.72% (specificity), 99.3% (accuracy), 98.59% (Dice similarity coefficient) and 97.23% (Jaccard) on test dataset.

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Quantitative computed tomography applied to interstitial lung diseases

Cited by 15 publications

References 20 publications

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

Association Between Pulmonary Vessel-Related Structures and Cryptogenic Fibrosing Alveolitis Using Derived Computed Tomography Among Chinese Patients

Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

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