Are Ground-Glass Opacities on Chest High-Resolution Computed Tomography a Manifestation of Airway Disease?
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ABSTRACT:
SUBMITTER: Moran-Mendoza O
PROVIDER: S-EPMC7958520 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
Publications
Are Ground-Glass Opacities on Chest High-Resolution Computed Tomography a Manifestation of Airway Disease?
Morán-Mendoza Onofre O Khalil Mohamed M
American journal of respiratory and critical care medicine 20210301 6
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Project description: Not available
| S-EPMC7958512 | biostudies-literature
Project description:To explore the diagnostic method in assessing the malignancy of pulmonary adenocarcinoma characterized by ground glass opacities (GGO) on computed tomography (CT).Preoperative CT data for preinvasive and invasive lung adenocarcinomas were analyzed retrospectively. GGO lesions that were detected on lung windows but absent using the mediastinal window were subject to adjustment of the window width, which was reduced with the fixed interval of 100 HU until the lesions were no longer evident, with a fixed mediastinal window level of 40 HU. The shape, smoking habits, size of the lesion on the lung window, and window width at which lesions disappeared were compared and receiver operating characteristic curves were used to determine the optimal cut-off of the lesion size and window width to differentiate between these invasive and preinvasive lesions.Of the 209 lung adenocarcinomas, 102 were preinvasive (25 atypical adenomatous hyperplasia and 77 adenocarcinoma in situ), while 107 were invasive (78 minimally invasive adenocarcinoma and 29 invasive adenocarcinoma). The shape, lesion size, and window width at which lesions were no longer evident differed significantly between the two groups (P < 0.05). The size of 8.9 mm and a window width of 1250 HU were the optimal cut-off to differentiate between preinvasive and invasive lesions.The shape, size of the lesion, and window width on high-resolution CT may be useful in assessing the invasiveness of lung adenocarcinoma that manifests as GGO. Irregular lesions that disappear at window width <1250 HU, with a diameter of > 8.9 mm are more likely to be invasive.
| S-EPMC4718115 | biostudies-other
Project description:ObjectiveTo explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions.MethodsCT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital (The Third Affiliated Hospital of Sun Yat-sen University) from January 2015 to March 2021 were analyzed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest. ANOVA and least absolute shrinkage and selection operator feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic curves were used to evaluate the classification efficiency.Results129 pGGOs were included. 2107 radiomic features were extracted from each region of interest. 18 radiomic features were eventually selected for model construction. The area under the curve of the radiomics model was 0.884 [95% confidence interval (CI), 0.818-0.949] in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate.ConclusionThe prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy.Advances in knowledgeWe focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.
| S-EPMC10996418 | biostudies-literature
Project description:ObjectivesGround-glass opacity (GGO)-a hazy, gray appearing density on computed tomography (CT) of lungs-is one of the hallmark features of SARS-CoV-2 in COVID-19 patients. This AI-driven study is focused on segmentation, morphology, and distribution patterns of GGOs.MethodWe use an AI-driven unsupervised machine learning approach called PointNet++ to detect and quantify GGOs in CT scans of COVID-19 patients and to assess the severity of the disease. We have conducted our study on the "MosMedData", which contains CT lung scans of 1110 patients with or without COVID-19 infections. We quantify the morphologies of GGOs using Minkowski tensors and compute the abnormality score of individual regions of segmented lung and GGOs.ResultsPointNet++ detects GGOs with the highest evaluation accuracy (98%), average class accuracy (95%), and intersection over union (92%) using only a fraction of 3D data. On average, the shapes of GGOs in the COVID-19 datasets deviate from sphericity by 15% and anisotropies in GGOs are dominated by dipole and hexapole components. These anisotropies may help to quantitatively delineate GGOs of COVID-19 from other lung diseases.ConclusionThe PointNet++ and the Minkowski tensor based morphological approach together with abnormality analysis will provide radiologists and clinicians with a valuable set of tools when interpreting CT lung scans of COVID-19 patients. Implementation would be particularly useful in countries severely devastated by COVID-19 such as India, where the number of cases has outstripped available resources creating delays or even breakdowns in patient care. This AI-driven approach synthesizes both the unique GGO distribution pattern and severity of the disease to allow for more efficient diagnosis, triaging and conservation of limited resources.
| S-EPMC8920286 | biostudies-literature
Project description:Objective: Pure ground-glass opacity (GGO) nodules have been detected with increasing frequency using computed tomography (CT). We performed a retrospective study to clarify whether lung cancer patient prognoses correlated with pure GGO nodules. We also analyzed the clinical characters of patients with pure GGO nodules to provide diagnostic guidance on lung cancer identification and treatment of patients in clinical practice. Methods: We enrolled 39 of 1422 patients with pure GGO nodules who accepted surgical treatment of the lung cancer nodules, and reviewed materials from 404 patients to verify our conclusions. To discover which factors were prognostically significant, we used the Kaplan-Meier method to estimate the overall survival (OS) and progression-free survival (PFS) curves. Age, gender, smoking history, histology, tumor size, and stage were the factors examined in our study. We also performed subgroup and matching group analyses to clarify the correlation between the presence of pure GGO nodules and prognoses. Results: Pure GGO nodules were associated with non-smoking females that had adenocarcinoma. The prognoses of patients in the pure GGO nodule group was better than those in the non-pure GGO nodule group (p = 0.046). Age, grade, and stage (including tumor size and lymph node metastases) were had prognostic significance. In the matching group stage assessments, although patient prognoses were not significantly different among patients of the GGO group compared with thoses of the other group in long-term, while in the short term, patients with pure GGO nodules had longer PFS. Non-smoking female patients with lung cancer were more likely to have adenocarcinoma. Conclusions: As a subgroup of GGO nodules, pure GGO nodules predict a better prognosis in all lung cancer patients. Wheras our study showed that lung patients with pure GGO nodules in similar stages were not significantly different in long-term prognoses, in the short term; patients with pure GGO nodules had longer PFS.
| S-EPMC6909955 | biostudies-literature
Project description:BackgroundThe widespread utilization of chest High-resolution Computed Tomography (HRCT) has prompted detection of pulmonary ground-glass nodules (GGNs) in otherwise asymptomatic individuals. We aimed to establish a simple clinical risk score model for assessing GGNs based on HRCT.MethodsWe retrospectively analyzed 574 GGNs in 574 patients undergoing HOOK-WIRE puncture and pulmonary nodule surgery from January 2014 to November 2018. Clinical characteristics and imaging features of the GGNs were assessed. We analyzed the differences between malignant and benign nodules using binary logistic regression analysis and constructed a simple risk score model, the VBV Score, for predicting the malignancy status of GGNs. Then, we validated this model via other 1200 GGNs in 1041 patients collected from three independent clinical centers in 2022.ResultsFor the exploratory phase of this study, out of the 574 GGNs, 481 were malignant and 93 were benign. Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. Then, we derived a VBV Score = vacuole sign + air bronchogram + intra-nodular vessel sign, to predict the malignancy of GGNs, with a sensitivity, specificity, and accuracy of 95.6%, 80.6%, and 93.2%, respectively. We also validated it on other 1200 GGNs, with a sensitivity, specificity, and accuracy of 96.0%, 82.6%, and 95.0%, respectively.ConclusionsVacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. VBV Score showed good sensitivity, specificity, and accuracy for differentiating benign and malignant pulmonary GGNs.
| S-EPMC10804577 | biostudies-literature
Project description:IntroductionThe increased use of cross-sectional imaging frequently identifies a growing number of lung nodules that require follow-up imaging studies and physician consultations. We report here the frequency of finding a ground-glass nodule (GGN) or semisolid lung lesion (SSL) in the past decade within a large academic health system.MethodsA radiology system database review was performed on all outpatient adult chest computed tomography (CT) scans between 2013 and 2022. Radiology reports were searched for the terms "ground-glass nodule," "subsolid," and "semisolid" to identify reports with findings potentially concerning for an adenocarcinoma spectrum lesion.ResultsA total of 175,715 chest CT scans were performed between 2013 and 2022, with a steadily increasing number every year from 10,817 in 2013 to 21,916 performed in the year 2022. Identification of GGN or SSL on any outpatient CT increased from 5.9% in 2013 to 9.2% in 2022, representing a total of 2019 GGN or SSL reported on CT scans in 2022. The percentage of CT scans with a GGN or SSL finding increased during the study period in men and women and across all age groups above 50 years old.ConclusionsThe total number of CT scans performed and the percentage of chest CT scans with GGN or SSL has more than doubled between 2013 and 2022; currently, 9% of all chest CT scans report a GGN or SSL. Although not all GGN or SSL radiographic findings represent true adenocarcinoma spectrum lesions, they are a growing burden to patients and health systems, and better methods to risk stratify radiographic lesions are needed.
| S-EPMC10709040 | biostudies-literature
Project description:Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts in the original scans. Similar to our preliminary study, a large portion of the annotations were excluded, possibly due to workers misunderstanding the instructions. After excluding such annotations, moderate to strong correlations with the expert can be observed, although these correlations are slightly lower than inter-expert correlations. Furthermore, the results across subjects in this study are quite variable. Although the crowd has potential in annotating airways, further development is needed for it to be robust enough for gathering annotations in practice. For reproducibility, data and code are available online: http://github.com/adriapr/crowdairway.git.
| S-EPMC8062042 | biostudies-literature
Project description:Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.
| S-EPMC9392994 | biostudies-literature
Project description: Not available
| S-EPMC10423360 | biostudies-literature