Ground glass opacities of the lung before, during and post COVID-19 pandemic.
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ABSTRACT:
SUBMITTER: Migliore M
PROVIDER: S-EPMC8339874 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
Publications
Ground glass opacities of the lung before, during and post COVID-19 pandemic.
Annals of translational medicine 20210701 13
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Project description:BackgroundRadiological manifestations of coronavirus disease 2019 (COVID-19) featured ground-glass opacities (GGOs), especially in the early stage, which might create confusion in differential diagnosis with early lung cancer. We aimed to specify the radiological characteristics of COVID-19 and early lung cancer and to unveil the discrepancy between them.MethodsOne hundred and fifty-seven COVID-19 patients and 374 early lung cancer patients from four hospitals in China were retrospectively enrolled. Epidemiological, clinical, radiological, and pathological characteristics were compared between the two groups using propensity score-matched (PSM) analysis.ResultsCOVID-19 patients had more distinct symptoms, tended to be younger (P<0.0001), male (P<0.0001), and had a higher body mass index (P=0.014). After 1:1 PSM, 121 matched pairs were identified. Regarding radiological characteristics, patients with a single lesion accounted for 17% in COVID-19 and 89% in lung cancer (P<0.0001). Most lesions were peripherally found in both groups. Lesions in COVID-19 involved more lobes (median 3.5 vs. 1; P<0.0001) and segments (median 6 vs. 1; P<0.0001) and tended to have multiple types (67%) with patchy form (54%). Early lung cancer was more likely to have a single type (92%) with oval form (66%). Also, COVID-19 and early lung cancer either had some distinctive features on computed tomography (CT) images.ConclusionsBoth COVID-19 and early lung cancers showed GGOs, with similar but independent features. The imaging characteristics should be fully understood and combined with epidemiological history, pathogen detection, laboratory tests, short-term CT reexamination, and pathological results to aid differential diagnosis.
| S-EPMC7481645 | 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: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
Project description:BackgroundUnderstanding the genomic landscape of early-stage lung adenocarcinoma (LUAD) may provide new insights into the molecular evolution in the early stages of LUAD.MethodsThrough sequencing of 79 spatially distinct regions from 37 patients with ground glass opacities (GGOs), we provided a comprehensive mutational landscape of GGOs, highlighting the importance of ancestry differences.ResultsOur study had several interesting features. First, epidermal growth factor receptor (EGFR), BRAF (v-RAF murine sarcoma viral oncogene homologue B1), and ERBB2 (Erb-B2 Receptor Tyrosine Kinase 2, also known as HER2) were more frequently mutated in our study, which supports the notion that EGFR is considered to be a major driver and tends to drive the occurrence of LUAD. Second, Signature 1, Signature 3, and Signature 6 were identified in patients with GGOs. Our results further suggested that Signature 1 was more prominent among early mutations. Third, compared with LUADs, GGOs exhibited significantly lower levers of arm-level copy number variation (CNV)-which alter the diploid status of DNA, and lower focal CNVs.ConclusionsIn our study, 79 samples of patients were included to analyze the GGO gene profile, revealing the genetic heterogeneity of GGO in East Asian population, and providing guidance for prognosis analysis of GGO patients by comparison with LUAD. Our study revealed that GGOs had fewer genomic alterations and simpler genomic profiles than LUADs. The most commonly altered processes were related to the receptor tyrosine kinase (RTK)/Ras/phosphatidylinositol-3-kinase (PI3K) signaling pathways in GGOs, and EGFR alterations were the dominant genetic changes across all targetable somatic changes.
| S-EPMC8107556 | biostudies-literature
Project description:BackgroundBilateral multiple ground glass opacities (GGOs) are observed in quite a part of patients with early-stage lung adenocarcinoma. For this so-called synchronous multiple primary lung cancer (sMPLC), targeting immune checkpoint is a favorable option in addition to surgical resection. The purpose of this study is to reveal the safety and efficacy of performing immune checkpoint inhibitors (ICIs) on patients with sMPLC and to explore the biomarkers of the efficacy.MethodsA total of 21 patients with sMPLC were enrolled and all included cases were pathologically confirmed adenocarcinoma after conducting surgical treatment for unilateral GGOs. ICIs of Sintilimab were then used to target programmed death 1 (200mg i.v., Q3W) for up to 10 cycles. Seven patients of them received the other surgery for contralateral GGOs, and multiomics assessments, including neoantigens, somatic mutations, and methylated loci, were further performed to investigate potential biomarkers.ResultsGrade 1 or 2 treatment-related adverse events (AEs) occurred in most of the patients (12/21, 57.1%), and one subject withdrawn for grade 3 AEs. For the seven patients underwent twice surgeries, twelve and thirteen GGOs were achieved before and after the use of ICIs separately, and a favorable efficacy was observed among six lesions after immunotherapy (> 50% pathologic tumor regression). Tumor infiltration T-cell and B-cell were further shown to be associated with the biological activity of ICIs. According to mechanism-based multiomics analyses, MUC19- and PCDHB5- mutations were indicated to correlate with a favorable prognosis of sMPLC underwent immunotherapy, and our results suggested that immunogenetic mutation and associated promoter methylation could provide a quantitative explanation for the pathologic response of GGOs.ConclusionOur study provides evidence that the use of ICIs contributed favorable efficacy and safety to patients with sMPLC. Immune infiltration and immunogenic biomarkers are revealed to be implications of performing ICIs on sMPLC. These preliminary findings exhibit the prospects in performing neoadjuvant or adjuvant immunotherapies on patients with sMPLC.Clinical trial registrationhttps://www.chictr.org.cn/showproj.aspx?proj=36878, identifier ChiCTR1900022159.
| S-EPMC9642914 | biostudies-literature
Project description: Not available
| S-EPMC7354109 | biostudies-literature
Project description: Not available
| S-EPMC5870446 | biostudies-literature
Project description:BackgroundPulmonary ground glass opacities (GGOs) are early-stage adenocarcinoma spectrum lesions that are not easily palpable. Challenges in localizing GGOs during intraoperative pathology can lead to imprecise diagnoses and additional time under anesthesia. To improve localization of GGOs during frozen section diagnosis, we evaluated a novel technique, 3-dimensional near-infrared specimen mapping (3D-NSM).MethodsFifty-five patients with a cT1 GGO were enrolled and received a fluorescent tracer preoperatively. After resection, specimens were inspected to identify lesions. Palpable and nonpalpable nodules underwent 3D-NSM and the area of highest fluorescence was marked with a suture. Time for 3D-NSM, time for frozen section diagnosis, and number of tissue sections examined were recorded. To compare 3D-NSM with standard-of-care techniques, a control cohort of 20 subjects with identical inclusion criteria were enrolled. Specimens did not undergo 3D-NSM and were sent directly to pathology.Results3D-NSM localized 54 of 55 lesions with 1 false negative. All 41 palpable lesions were identified by 3D-NSM. Thirteen (92.8%) of 14 nonpalpable lesions were located by 3D-NSM. Time to diagnosis for the 3D-NSM cohort was 23.5 minutes, compared with 26.0 minutes in the control cohort (P = .04). 3D-NSM did not affect time to diagnosis of palpable lesions (23.2 minutes vs 21.4 minutes; P = .10). 3D-NSM significantly reduced time to diagnosis for nonpalpable lesions (23.3 minutes vs 34.4 minutes; P < .0001). 3D-NSM also reduced the number of tissue sections analyzed in nonpalpable lesions (4.50 vs 11.00; P < .0001).Conclusions3D-NSM accurately localizes GGOs and expedites intraoperative diagnosis by reducing the number of tissue sections analyzed for nonpalpable GGOs.
| S-EPMC9188686 | biostudies-literature