[Diagnosis and Treatment of Pulmonary Ground-glass Nodules].
Ontology highlight
ABSTRACT:
SUBMITTER: Liu B
PROVIDER: S-EPMC6712268 | biostudies-literature | 2019 Jul
REPOSITORIES: biostudies-literature
Publications
[Diagnosis and Treatment of Pulmonary Ground-glass Nodules].
Zhongguo fei ai za zhi = Chinese journal of lung cancer 20190701 7
Recent widespread use of high resolution computed tomography (HRCT) for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass nodule (GGN). However, it had been proved that GGN was well associated with lung cancer in previous studies. Therefore, the classification, imaging characteristics, pathological type, follow-up, suggested managements and other clinical concerns of GGN were reviewed in this paper. . ...[more]
Similar Datasets
Project description:In this study, we evaluated the diagnostic value and molecular characteristics of plasma extracellular vesicles (EVs)-derived miRNAs for patients with solitary pulmonary nodules (SPNs), particularly ground-glass nodules (GGNs). This study was registered at www.clinicaltrials.gov under registration number NCT03230019. Small RNA sequencing was performed to assess plasma EVs miRNAs in 59 patients, including 12 patients with benign nodules (2017, training set). MiRNA profiles of 40 an additional individuals were sequenced (2018, validation set). Overall, 16 pure GGNs, 21 mixed GGNs, and 42 solid nodules were included, with paired post-operative plasma samples available for 20 patients. The target miRNA/reference miRNA ratio was used to construct a support vector machine (SVM) model. The SVM model with the best specificity showed 100% specificity in both the training and validation sets independently. The model with the best sensitivity showed 100% and 96.9% sensitivity in the training and validation sets, respectively. Principal component analysis revealed that pure GGN distributions were distinct from those of solid nodules, and mixed GGNs had a diffuse distribution. Among differentially expressed miRNAs, miR-500a-3p, miR-501-3p, and miR-502-3p were upregulated in tumor tissues and enhanced overall survival. The SVM classifier accurately distinguished malignant GGNs and benign nodules. The distinct profile characteristics of miRNAs provided insights into the feasibility of EVs miRNAs as prognostic factors in lung cancer.
| S-EPMC6758624 | biostudies-literature
Project description:BACKGROUND:Lobectomy plus lymph node dissection is the standard treatment of early-stage lung cancer, but the low lymph node metastasis rate with ground-glass opacity (GGO) makes surgeons not perform lymphadenectomy. This study aimed to re-evaluate the lymph node metastasis rate of GGO to help make a clinical judgment. METHODS:We performed this retrospective study to enroll patients who received lung cancer surgery from 2011 to 2016. Patient characteristics collected included tumor size, solid part size and lymph node metastasis rate. These patients were categorized into pure GGO and part solid GGO groups to undergo analysis. RESULTS:Lymph node metastasis rates were 0%, 3.8% and 6.9% in order of the pure GGO group, the GGO predominant group and the solid predominant group. In the lobectomy patients, the solid predominant group still showed to have the highest lymph node metastasis rate and recurrence rate (8.3% and 10.1%). CONCLUSION:It is unnecessary to perform lymphadenectomy for patients with pure GGO in view of the 0% lymph node metastasis rate. The higher lymph node metastasis rate in the patients with the solid predominant group, 6.9%, suggested that surgeons should choose a rational lymphadenectomy method according to their GGO property and clinical judgment.
| S-EPMC7141214 | biostudies-literature
Project description:Background:The method of locating pulmonary nodules before operation plays a crucial role in the surgery of pulmonary ground-glass nodules (GGNs). However, the methodologies surrounding intraoperative localization remains limited, with the majority procedures requiring specific additional equipment. We report a new approach in locating pulmonary GGNs by image-localized body surface marking intraoperative (IBMI) localization. Methods:A retrospective review of the medical records of 76 patients with pulmonary GGNs was performed. All patients underwent IBMI localization between January 2018 and March 2019. Twenty-six patients underwent CT-guided hook wire localization before IBMI localization during surgery. IBMI localization was undertaken directly without pre-treatment in the remaining patients. The efficacy and complications of this approach were analyzed and compared with other pre- or intraoperative localization methods in the current literature. Results:The intraoperative localizations were performed successfully in 72 of all 76 patients pulmonary GGNs within a mean duration of 5.3±1.8 (range, 2.0 to 9.6) minutes. The GGNs in four cases were found to have a significant deviation (>1.5 cm) from the positioning points. All GGNs were successfully resected. Except for five cases of active chest wall bleeding (6.5%), no other intra- or postoperative complications occurred. Conclusions:The IBMI localization approach is a safe and short-duration procedure with high success rates and fewer complications. We used it for the first time for intraoperative localization of peripheral GGNs with excellent results.
| S-EPMC7417760 | biostudies-literature
Project description:BackgroundRecent advances in imaging modalities and recommended low-dose computed tomography screening programs have made it easier to diagnose early lung cancer. However, the diagnosis of small ground-glass nodules (GGNs) has been problematic due to inappropriate specimen procurement and failure of conventional percutaneous core needle biopsy. Thus, we aimed to evaluate the usefulness of electromagnetic navigation bronchoscopy (ENB)-guided video-assisted lung resection for not only the diagnosis but also treatment of GGNs.MethodsFrom 2017 to 2019, 110 patients with suspicious lung cancer lesions that were not diagnosed by conventional procedure underwent ENB-guided lung resection. Among 35 cases of GGNs, 33 cases of localization were included in this study (two cup biopsy cases were excluded). We used SuperDimension™ for the ENB procedure. After general anesthesia, indigo carmine (0.3-0.5 mL) was injected, and GGNs were resected through video-assisted thoracoscopic surgery.ResultsOf the 33 GGNs, 16 were pure (2 adenocarcinomas in situ, 5 minimally invasive adenocarcinomas (MIAs), 3 adenocarcinomas, and 6 benign lesions) and 17 were mixed (1 MIA, 11 adenocarcinomas, and 5 benign lesions). The mean size of all lesions was 11.2±7.78 mm, mean distance to the pleura was 11.2±14.2 mm, and mean ENB procedure time was 18.8±8.88 minutes. Dye localization and surgical resection of GGN were successful in all cases. There was no procedure-related complication.ConclusionsENB is a feasible and highly accurate localization method for minimally invasive lung resection of small GGNs.
| S-EPMC7330407 | biostudies-literature
Project description:Background: The management of ground glass nodules (GGNs) remains a distinctive challenge. This study is aimed at comparing the predictive growth trends of radiomic features against current clinical features for the evaluation of GGNs. Methods: A total of 110 GGNs in 85 patients were included in this retrospective study, in which follow up occurred over a span ≥2 years. A total of 396 radiomic features were manually segmented by radiologists and quantitatively analyzed using an Analysis Kit software. After feature selection, three models were developed to predict the growth of GGNs. The performance of all three models was evaluated by a receiver operating characteristic (ROC) curve. The best performing model was also assessed by calibration and clinical utility. Results: After using a stepwise multivariate logistic regression analysis and dimensionality reduction, the diameter and five specific radiomic features were included in the clinical model and the radiomic model. The rad-score [odds ratio (OR) = 5.130; P < 0.01] and diameter (OR = 1.087; P < 0.05) were both considered as predictive indicators for the growth of GGNs. Meanwhile, the area under the ROC curve of the combined model reached 0.801. The high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test and the decision curve analysis was utilized for the nomogram. Conclusions: A combined model using the current clinical features alongside the radiomic features can serve as a powerful tool to assist clinicians in guiding the management of GGNs.
| S-EPMC7606974 | biostudies-literature
Project description: Not available
| S-EPMC8435391 | biostudies-literature
Project description:Background:It is important to distinguish the classification of lung adenocarcinoma. A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT. Materials and Methods:A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs. In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P <0.05) using univariate analysis. Multivariate analysis showed that PM, lobulated border, pleural retraction, age, and fractal dimension (FD) were significantly different between preinvasive lesions and IPAs. After logistic regression analysis, PM and FD were used to develop a prediction nomogram. The validation cohort was comprised of patients scanned after Jan 2016. Results:The model showed good discrimination between preinvasive lesions and IPAs with an area under curve (AUC) of 0.76 [95% CI: 0.71 to 0.80] in ROC curve for the primary cohort. The nomogram also demonstrated good discrimination in the validation cohort with an AUC of 0.79 [95% CI: 0.71 to 0.88]. Conclusions:For GGNs, PM, lobulated border, pleural retraction, age, and FD were features discriminating preinvasive lesions from IPAs. The radiomics model based upon PM and FD may predict the invasiveness of pulmonary adenocarcinomas appearing as GGNs.
| S-EPMC6020660 | biostudies-literature
Project description:OBJECTIVES:To assess the performance of the "Computer-Aided Nodule Assessment and Risk Yield" (CANARY) software in the differentiation and risk assessment of histological subtypes of lung adenocarcinomas manifesting as pure ground glass nodules on computed tomography (CT). METHODS:64 surgically resected and histologically proven adenocarcinomas manifesting as pure ground-glass nodules on CT were assessed using CANARY software, which classifies voxel-densities into three risk components (low, intermediate, and high risk). Differences in risk components between histological adenocarcinoma subtypes were analysed. To determine the optimal threshold reflecting the presence of an invasive focus, sensitivity, specificity, negative predictive value, and positive predictive value were calculated. RESULTS:28/64 (44%) were adenocarcinomas in situ (AIS); 26/64 (41%) were minimally invasive adenocarcinomas (MIA); and 10/64 (16%) were invasive ACs (IAC). The software showed significant differences in risk components between histological subtypes (P<0.001-0.003). A relative volume of 45% or less of low-risk components was associated with histological invasiveness (specificity 100%, positive predictive value 100%). CONCLUSIONS:CANARY-based risk assessment of ACs manifesting as pure ground glass nodules on CT allows the differentiation of their histological subtypes. A threshold of 45% of low-risk components reflects invasiveness in these groups. KEY POINTS:• CANARY-based risk assessment allows the differentiation of their histological subtypes. • 45% or less of low-risk component reflects histological invasiveness. • CANARY has potential role in suspected adenocarcinomas manifesting as pure ground-glass nodules.
| S-EPMC5717124 | biostudies-literature
Project description:Background and objectiveThe incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area.MethodsWe used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures.Key content and findingsWe examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs.ConclusionsA variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.
| S-EPMC11320543 | biostudies-literature
Project description:IntroductionCryobiopsy (CB) using a 1.1-mm cryoprobe under fluoroscopic guidance is feasible and safe for diagnosis of ground glass opacity (GGO) lesions. However, the efficacy of CB combined with cone-beam CT (CBCT) for GGO-predominant pulmonary nodules remains elusive.MethodsWe retrospectively studied patients who underwent CB combined with conventional biopsy under CBCT guidance for GGO-predominant pulmonary nodules with a consolidation-to-tumour ratio <50.0%.ResultsA total of 32 patients with GGO-predominant pulmonary nodules were enrolled: 17 pure GGOs and 15 mixed GGOs. The mean lesion diameter was 15.81 ± 5.52 mm and the overall diagnostic yield was 71.9%. Seven lesions were diagnosed by CB alone, which increased the diagnostic outcomes by 21.9%. Diagnostic yields for CB, forceps biopsy (FB), brushing, and guide sheath flushing were 65.6%, 46.9%, 15.6%, and 14.3%, respectively. Univariate analysis revealed that positive computed tomography (CT) bronchus sign (p = 0.035), positive CBCT sign (p < 0.01), and CB-first biopsy sequence (p = 0.036) were significant predictive factors for higher diagnostic yield. Specimens obtained by CB had larger mean sample size (p < 0.01), lower blood cell area (p < 0.01), and fewer crush artefacts (p < 0.01) than specimens from FB. No severe bleeding or other complications occurred.ConclusionCB using a 1.1-mm cryoprobe under CBCT guidance increased diagnostic yield for GGO-predominant pulmonary nodules based on conventional biopsy. Further, it provided larger and nearly intact samples compared with forceps.
| S-EPMC10823549 | biostudies-literature