Management of Ground-Glass Nodules: When and How to Operate?

The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis

BACKGROUND

Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.

METHODS

To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls.

RESULTS

A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls.

CONCLUSIONS

To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

Network pharmacology to explore the mechanism of traditional Chinese medicine in the treatment of ground glass nodules

Background

Ground glass nodules (GGNs) in the lung are considered to be a high-risk factor of lung adenocarcinoma. Immediate surgery is not recommended for GGNs patients, and low-dose computed tomography (CT) is often used for observation and follow-up, which brings high psychological and economic burden to the patient.

Methods

Three traditional Chinese medicine (TCM) prescriptions for the treatment of GGNs were found through database including PubMed, Google Scholar, and China National Knowledge Infrastructure (CNKI), Scopus and so on. The possible targets of the active ingredients of the TCM preparations and the gene targets of GGNs were screened out from Traditional Chinese Medicine Systems Pharmacology (TCMSP), UniProt and GeneCards. Network visualization was realized via STRING, Cytoscape 3.7.2, Evenn, DAVID and Hiplot. Finally, molecular docking Vina and PyMOL software were performed to further explore the possibility of drug-target interactions using PubChem compounds, protein data bank (PDB) database, Autodocktools and Autodock.

Results

Three TCM preparations could target the same 13 potential therapeutic targets in GGNs. From network pharmacology, 14 signaling pathways, the functions of the significant targets, an effective ingredient in TCM prescriptions and its functions were obtained.

Conclusions

Chinese herbal formulas containing quercetin could be a potential treatment for GGNs, targeting C-reactive protein (CRP), tumor necrosis factor (TNF), interferon gamma (IFN-γ), intercellular adhesion molecule 1 (ICAM-1), and vascular endothelial growth factor A (VEGFA) through the hypoxia-inducible factor 1 (HIF-1) pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and leukocyte transendothelial migration.

[18F]FDG PET/CT is useful in discriminating invasive adenocarcinomas among pure ground-glass nodules: comparison with CT findings-a bicenter retrospective study

PURPOSE

Predicting the malignancy of pure ground-glass nodules (GGNs) using CT is challenging. The optimal role of [18F]FDG PET/CT in this context has not been clarified. We compared the performance of [18F]FDG PET/CT in evaluating GGNs for predicting invasive adenocarcinomas (IACs) with CT.

METHODS

From June 2012 to December 2020, we retrospectively enrolled patients with pure GGNs on CT who underwent [18F]FDG PET/CT within 90 days. Overall, 38 patients with 40 ≥ 1-cm GGNs were pathologically confirmed. CT images were analyzed for size, attenuation, uniformity, shape, margin, tumor-lung interface, and internal/surrounding characteristics. Visual [18F]FDG positivity, maximum standardized uptake value (SUVmax), and tissue fraction-corrected SUVmax (SUVmaxTF) were evaluated on PET/CT.

RESULTS

The histopathology of the 40 GGNs were: 25 IACs (62.5%), 9 minimally invasive adenocarcinomas (MIA, 22.5%), and 6 adenocarcinomas in situ (AIS, 15.0%). No significant differences were found in CT findings according to histopathology, whereas visual [18F]FDG positivity, SUVmax, and SUVmaxTF were significantly different (P=0.001, 0.033, and 0.018, respectively). The size, visual [18F]FDG positivity, SUVmax, and SUVmaxTF showed significant diagnostic performance to predict IACs (area under the curve=0.693, 0.773, 0.717, and 0.723, respectively; P=0.029, 0.001, 0.018, and 0.013, respectively). In the multivariate logistic regression analysis, visual [18F]FDG positivity discriminated IACs among GGNs among various CT and PET findings (P=0.008).

CONCLUSIONS

[18F]FDG PET/CT demonstrated superior diagnostic performance compared to CT in differentiating IAC from AIS/MIA among pure GGNs, thus it has the potential to guide the proper management of patients with pure GGNs.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions

PURPOSE

To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis.

MATERIALS AND METHODS

From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them.

RESULTS

Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness.

CONCLUSION

The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

Interpretable CT radiomics model for invasiveness prediction in patients with ground-glass nodules

AIM

To evaluate the performance of an interpretable computed tomography (CT) radiomic model in predicting the invasiveness of ground-glass nodules (GGNs).

MATERIALS AND METHODS

The study was conducted retrospectively from 1 August 2017 to 1 August 2022, at three different centres. Two hundred and thirty patients with GGNs were enrolled at centre I as a training cohort. Centres II (n=157) and III (n=156) formed two external validation cohorts. Radiomics features extracted based on CT were reduced by a coarse-fine feature screening strategy. A radiomic model was developed through the use of the LASSO (least absolute shrinkage and selection operator) and XGBoost algorithms. Then, a radiological model was established through multivariate logistic regression analysis. Finally, the interpretability of the model was explored using SHapley Additive exPlanations (SHAP).

RESULTS

The radiomic XGBoost model outperformed the radiomic logistic model and radiological model in assessing the invasiveness of GGNs. The area under the curve (AUC) values for the radiomic XGBoost model were 0.885 (95% confidence interval [CI] 0.836-0.923), 0.853 (95% CI 0.790-0.906), and 0.838 (95% CI 0.773-0.902) in the training and the two external validation cohorts, respectively. The SHAP method allowed for both a quantitative and visual representation of how decisions were made using a given model for each individual patient. This can provide a deeper understanding of the decision-making mechanisms within the model and the factors that contribute to its prediction effectiveness.

CONCLUSIONS

The present interpretable CT radiomics model has the potential to preoperatively evaluate the invasiveness of GGNs. Furthermore, it can provide personalised, image-based clinical-decision support.

Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features

RATIONALES AND OBJECTIVES

To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas.

MATERIALS AND METHODS

A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis.

RESULTS

The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth.

CONCLUSION

Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.

Computed tomography-guided hookwire localization and medical glue combined with methylene blue localization for pulmonary nodules before video-assisted thoracoscopic surgery: a single-center, retrospective study

Background

The aim of this study was to investigate the safety and efficacy of computed tomography (CT)-guided hookwire localization and new CT-guided medical glue combined with methylene blue (MGMB) localization before video-assisted thoracoscopic surgery (VATS) for solitary pulmonary nodules (SPNs) and to analyze the risk factors for complications after localization.

Methods

A total of 620 patients, comprising 727 SPNs, admitted to the Department of Thoracic Surgery of the First Hospital of the University of Science and Technology of China between December 2019 and July 2022 were retrospectively studied and case-control analyzed. According to the localization method, 620 patients were divided into the hookwire group (n=310) and MGMB group (n=310). The localization time, localization-to-surgery interval, operative time, length of hospitalization, and complication rate were compared between the 2 groups. Logistic regression was used to analyze the risk factors for the occurrence of complications in each group of localization methods.

Results

Compared to the hookwire group, the MGMB group had a shorter localization time (8.59±3.69 vs. 7.35±2.99 min; P<0.001), shorter hospital stay (5.60±2.13 vs. 6.73±2.86 days; P<0.001), and shorter operative time (103.48±54.11 vs. 98.59±49.92 min; P=0.33). The preoperative localization success rate was 99.4% (355/357) and 100% (370/370) in the hookwire group and MGMB group, respectively. No death or serious complications occurred during the localization process, but the overall complication rate was lower in the MGMB group (69/310, 22.3%) than in the hookwire group (105/310, 33.9%) (P<0.001). Logistic regression analysis showed that age, number of nodules, and localization time were risk factors for total complications, while localization technique was a protective factor for total complications [odds ratio =0.590; 95% confidence interval (CI): 0.405-0.860; P<0.05].

Conclusions

Both techniques could effectively locate SPNs before VATS; however, MGMB localization was found to be associated with a lower complication rate, shorter localization time, better safety, and higher potential clinical value and is thus worthy of clinical promotion.

Scrape cytology and radiological solid size correlation can be used in the intraoperative management of subsolid lung nodules

BACKGROUND

The term radiologic subsolid lung nodule (SLN) represents a heterogeneous group of non-neoplastic and neoplastic lesions. Intraoperative evaluation (IO) is often required to differentiate and diagnose. The current study aims to investigate the feasibility and reliability of scrape cytology (SC) and radiologic solid size correlation for the IO diagnosis of SLNs.

METHODS

Sixty-eight patients with SLN signs were eligible to take part in the study due to intraoperatively prepared SC slides. We managed to complete the blind radiologic solid size measurement and cytologic evaluation retrospectively. Cases were grouped into three categories based on their cytological features: Group-0 (Benign), Group-1 (mild atypical features), and Group-2 (severe atypical features/unequivocally carcinoma). IO diagnoses were given by combining the radiologic solid size and cytological findings.

RESULTS

Cytological features of Group-1 were observed in 100%, 93%, 32.5%, and 17% of the AIS, MIA, IA, and benign lesions, respectively. Cytological features of Group-2 were observed in 67.5%, and 7% of the IA and MIA, respectively. By combining cytology with radiologic solid size, 100%, 85%, 71%, and 83% of the AIS, IA, MIA, and benign lesions respectively were diagnosed correctly. Fifteen (15%) percent of the IA cases were underdiagnosed as MIA since their radiological solid sizes were less than 0.5 cm with cytological features of Group-1. Conversely, 29% of the MIA cases were overdiagnosed as IA since their radiological solid sizes were greater than 0.5 cm.

CONCLUSION

SLNs should be handled with caution in terms of IO management. SC and radiologic solid size correlation both provide a practical and tissue-protecting approach for the IO evaluation of SLNs, ensuring a high consistency between IO and definitive diagnosis.

[Port-only 4-Arms Robotic Segmentectomy Under Artificial Pneumothorax]

BACKGROUND

At present, robotic surgery is widely used in thoracic surgery, which has higher maneuverability, precision, and stability, especially for small space complex operations and reconstructive surgery. The advantages of robotic lung segment resection under full orifice artificial pneumothorax are obvious.

METHODS

Based on a large number of clinical practices, we established a set of surgical methods for 4-arm robotic lung segment resection under a port-only artificial pneumothorax. 98 cases of robotic lung segment resection were performed with this method from January 2019 to August 2022. The clinical experience was summarized.

RESULTS

Robotic lung segment resection under port-only artificial pneumothorax has obvious advantages in the anatomy of lung segment vessels and bronchi. It is characterized by less bleeding, shorter operation time, adequate exposure, and flexible operation.

CONCLUSIONS

This surgical model we propose optimizes the operation mode and technique of lung segment resection, makes each step procedural, reduces collateral damage, and is easy to learn and master, which is believed to cure more lung cancer patients with less trauma.

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Purpose

To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification.

Methods

A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves.

Results

Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit.

Conclusions

A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00483-1.