The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.

BACKGROUND: Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. METHODS: A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. RESULTS: Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. CONCLUSION: This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.

The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.

Background: The incidence and mortality rate of lung cancer are the highest in the world among all malignant tumors. Accurate assessment of ground-glass nodules (GGNs) is crucial in reducing lung cancer mortality. This study aimed to explore the value of computed tomography (CT) features and quantitative parameters in predicting the invasiveness and degree of infiltration of GGNs. Methods: Lesions were classified into three groups based on pathological types: the precursor glandular lesion (PGL) group, including atypical adenomatoid hyperplasia and adenocarcinoma in situ; the minimally invasive adenocarcinoma group; and the invasive adenocarcinoma group. Quantitative and qualitative data of the nodules were compared, and receiver operating characteristic (ROC) curve analysis was performed for each quantitative parameter. Binary logistic regression analysis was used to evaluate independent predictors of GGN invasiveness. Results: There were significant differences in lesion size, morphology, nodule type, bronchial abnormality, internal vascular sign and pleural retraction among the three groups (P<0.05). There were significant differences in all CT quantitative parameters (CT attenuation value in the plain phase, CT attenuation value in the arterial phase, CT attenuation value in the venous phase, arterial phase enhancement difference, venous phase enhancement difference, arterial phase enhancement index and venous phase enhancement index) among the three groups (P<0.001). The ROC curve analysis showed that the CT attenuation value in the plain phase, CT attenuation value in each enhanced phase, enhancement difference and enhancement index had good discriminatory power. Binary logistic regression analysis revealed that nodule type and internal vascular sign were independent risk factors for GGN invasiveness. Conclusions: CT features combined with enhanced scanning and quantitative analysis have important value in predicting the invasiveness of GGNs. The type of pulmonary nodule detected on CT (pure GGN or mixed GGN) and the presence of internal vascular signs are independent risk factors for GGN invasiveness.

Multimodal Deep Learning Network for Differentiating Between Benign and Malignant Pulmonary Ground Glass Nodules.

OBJECTIVE: This study aimed to establish a multimodal deep-learning network model to enhance the diagnosis of benign and malignant pulmonary ground glass nodules [GGNs]. METHODS: Retrospective data on pulmonary GGNs were collected from multiple centers across China, including North, Northeast, Northwest, South, and Southwest China. The data were divided into a training set and a validation set in an 8:2 ratio. In addition, a GGN dataset was also obtained from our hospital database and used as the test set. All patients underwent chest computed tomography [CT], and the final diagnosis of the nodules was based on postoperative pathological reports. The Residual Network [ResNet] was used to extract imaging data, the Word2Vec method for semantic information extraction, and the Self Attention method for combining imaging features and patient data to construct a multimodal classification model. Then, the diagnostic efficiency of the proposed multimodal model was compared with that of existing ResNet and VGG models and radiologists. RESULTS: The multicenter dataset comprised 1020 GGNs, including 265 benign and 755 malignant nodules, and the test dataset comprised 204 GGNs, with 67 benign and 137 malignant nodules. In the validation set, the proposed multimodal model achieved an accuracy of 90.2%, a sensitivity of 96.6%, and a specificity of 75.0%, which surpassed that of the VGG [73.1%, 76.7%, and 66.5%] and ResNet [78.0%, 83.3%, and 65.8%] models in diagnosing benign and malignant nodules. In the test set, the multimodal model accurately diagnosed 125 [91.18%] malignant nodules, outperforming radiologists [80.37% accuracy]. Moreover, the multimodal model correctly identified 54 [accuracy, 80.70%] benign nodules, compared to radiologists' accuracy of 85.47%. The consistency test comparing radiologists' diagnostic results with the multimodal model's results in relation to postoperative pathology showed strong agreement, with the multimodal model demonstrating closer alignment with gold standard pathological findings [Kappa=0.720, P<0.01]. CONCLUSION: The multimodal deep learning network model exhibited promising diagnostic effectiveness in distinguishing benign and malignant GGNs and, therefore, holds potential as a reference tool to assist radiologists in improving the diagnostic accuracy of GGNs, potentially enhancing their work efficiency in clinical settings.

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Lung adenocarcinoma manifested as ground-glass nodules in teenagers: characteristics, surgical outcomes and management strategies.

OBJECTIVES: Ground-glass nodules-featured lung cancer have been identified in some teenagers in recent years. This study aims to investigate the characteristics and surgical outcomes of these patients and explore proper management strategy. METHODS: Patients aged ≤20 with incidentally diagnosed lung cancer were retrospectively reviewed from February 2016 to March 2023. Based on lymph node evaluation status, these patients were divided into non-lymph node evaluation and lymph node evaluation groups. The clinical and pathological characteristics were analysed. RESULTS: A total of 139 teenage patients were included, with an obviously increased cases observed from 2019, corresponding to the COVID-19 pandemic. The median age of the 139 patients was 18 years (range 12-20). Eighty-five patients had pure ground-glass nodules, while others had mixed ground-glass nodules. The mean diameter of nodules was 8.87 ± 2.20 mm. Most of the patients underwent wedge resection (64%) or segmentectomy (31.7%). Fifty-two patients underwent lymph node sampling or dissection. None of these patients had lymph node metastasis. The majority of lesions were adenocarcinoma in situ (63 cases) and minimally invasive adenocarcinoma (72 cases), while four lesions were invasive adenocarcinoma. The median follow-up time was 2.46 years, and none of these patients experienced recurrence or death during follow-up. The lymph node evaluation group had longer hospital stays (P < 0.001), longer surgery time (P < 0.001), and greater blood loss (P = 0.047) than the non-lymph node evaluation group. CONCLUSIONS: The COVID-19 pandemic significantly increased the number of teenage patients incidentally diagnosed with lung cancer, presenting as ground-glass nodules on CT scans. These patients have favourable surgical outcomes. We propose a management strategy for teenage patients, and suggest that sub-lobar resection without lymph node dissection may be an acceptable surgical procedure for these patients.

Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.

This study aims to develop a CT-based hybrid deep learning network to predict pathological subtypes of early-stage lung adenocarcinoma by integrating residual network (ResNet) with Vision Transformer (ViT). A total of 1411 pathologically confirmed ground-glass nodules (GGNs) retrospectively collected from two centers were used as internal and external validation sets for model development. 3D ResNet and ViT were applied to investigate two deep learning frameworks to classify three subtypes of lung adenocarcinoma namely invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma and adenocarcinoma in situ, respectively. To further improve the model performance, four Res-TransNet based models were proposed by integrating ResNet and ViT with different ensemble learning strategies. Two classification tasks involving predicting IAC from Non-IAC (Task1) and classifying three subtypes (Task2) were designed and conducted in this study. For Task 1, the optimal Res-TransNet model yielded area under the receiver operating characteristic curve (AUC) values of 0.986 and 0.933 on internal and external validation sets, which were significantly higher than that of ResNet and ViT models (p < 0.05). For Task 2, the optimal fusion model generated the accuracy and weighted F1 score of 68.3% and 66.1% on the external validation set. The experimental results demonstrate that Res-TransNet can significantly increase the classification performance compared with the two basic models and have the potential to assist radiologists in precision diagnosis.

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.

Multidimensional biological characteristics of ground glass nodules.

The detection rate of ground glass nodules (GGNs) has increased in recent years because of their malignant potential but relatively indolent biological behavior; thus, correct GGN recognition and management has become a research focus. Many scholars have explored the underlying mechanism of the indolent progression of GGNs from several perspectives, such as pathological type, genomic mutational characteristics, and immune microenvironment. GGNs have different major mutated genes at different stages of development; EGFR mutation is the most common mutation in GGNs, and p53 mutation is the most abundant mutation in the invasive stage of GGNs. Pure GGNs have fewer genomic alterations and a simpler genomic profile and exhibit a gradually evolving genomic mutation profile as the pathology progresses. Compared to advanced lung adenocarcinoma, GGN lung adenocarcinoma has a higher immune cell percentage, is under immune surveillance, and has less immune escape. However, as the pathological progression and solid component increase, negative immune regulation and immune escape increase gradually, and a suppressive immune environment is established gradually. Currently, regular computer tomography monitoring and surgery are the main treatment strategies for persistent GGNs. Stereotactic body radiotherapy and radiofrequency ablation are two local therapeutic alternatives, and systemic therapy has been progressively studied for lung cancer with GGNs. In the present review, we discuss the characterization of the multidimensional molecular evolution of GGNs that could facilitate more precise differentiation of such highly heterogeneous lesions, laying a foundation for the development of more effective individualized treatment plans.

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.

The immune microenvironment of lung adenocarcinoma featured with ground-glass nodules.

Early-stage lung cancer is now more commonly identified in the form of ground-glass nodules (GGNs). Presently, the treatment of lung cancer with GGNs mainly depends on surgery; however, issues still exist such as overtreatment and delayed treatment due to the nonuniform standard of follow-up. Therefore, the discovery of a noninvasive treatment could expand the treatment repertoire of ground-glass nodular lung cancer and benefit the prognosis of patients. Immunotherapy has recently emerged as a new promising approach in the field of lung cancer treatment. Thus, this study presents a comprehensive review of the immune microenvironment of lung cancer with GGNs and describes the functions and characteristics of various immune cells involved, aiming to provide guidance for the clinical identification of novel immunotherapeutic targets.

Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis.

BACKGROUND: Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. OBJECTIVE: Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal. METHODS: A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves. RESULTS: In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model. CONCLUSION: We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

Sublobectomy and lymph node sampling are adequate for patients with invasive lung adenocarcinoma presenting as pure ground glass nodules.

PURPOSE: In this study, we aimed to investigate the prognosis of invasive lung adenocarcinoma that manifests as pure ground glass nodules (pGGNs) and confirm the effectiveness of sublobectomy and lymph node sampling in patients with pGGN-featured invasive adenocarcinoma (IAC). MATERIALS AND METHODS: We retrospectively enrolled 139 patients with pGGN-featured IAC, who underwent complete resection in two medical institutions between January 2011 and May 2022. Stratification analysis was conducted to ensure balanced baseline characteristics among the patients. The 5-year overall survival (OS) and disease-free survival (DFS) rates were compared between the groups using Kaplan-Meier survival curves and log-rank test. RESULTS: The 5-year OS and DFS rates for patients with IAC presenting as pGGNs after surgery were 96.5% and 100%, respectively. No lymph node metastasis or recurrence was observed in any of the enrolled patients. There was no statistically significant difference in the 5-year OS between patients who underwent lobectomy or sublobectomy, along with lymph node resection or sampling. CONCLUSION: IAC presented as pGGNs exhibited low-grade malignancy and had a relatively good prognosis. Therefore, these patients may be treated with sublobectomy and lymph node sampling.

A metabolomics study on carcinogenesis of ground-glass nodules.

Objective: This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer. Material and Methods: Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure. Results: Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples. Conclusion: Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography.

RATIONALE AND OBJECTIVES: To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes. MATERIALS AND METHODS: An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values. The ITH score's performance was compared to those of conventional radiomics (C-radiomics), and radiological assessments conducted by experienced radiologists. RESULTS: The ITH score demonstrated excellent performance in distinguishing lepidic-predominant adenocarcinoma (LPA) from other histological subtypes of clinical T1 stage lung adenocarcinoma presenting as pGGN. It outperformed both C-radiomics and radiological findings, exhibiting higher AUCs of 0.784 (95% confidence interval [CI]: 0.742-0.826) and 0.801 (95% CI: 0.739-0.863) in the training and validation cohorts, respectively. The AUCs of C-radiomics were 0.764 (95% CI: 0.718-0.810, DeLong test, p = 0.025) and 0.760 (95% CI: 0.692-0.829, p = 0.023) and those of radiological findings were 0.722 (95% CI: 0.673-0.771, p = 0.003) and 0.754 (95% CI: 0.684-0.823, p = 0.016) in the training and validation cohorts, respectively. Subgroup analysis revealed varying diagnostic efficacy across clinical T1 stages, with the highest efficacy in the T1a stage, followed by the T1b stage, and lowest in the T1c stage. CONCLUSION: The ITH score presents a superior method for evaluating histological subtypes and distinguishing LPA from other subtypes in clinical T1 stage lung adenocarcinoma presenting as pGGN.

Synchronous Computed Tomography-Guided Percutaneous Transthoracic Needle Biopsy and Microwave Ablation for Highly Suspicious Malignant Pulmonary Ground-Glass Nodules.

INTRODUCTION: There is no consensus regarding the most appropriate management of suspected malignant pulmonary ground-glass nodules (GGNs). OBJECTIVE: We aimed to explore the feasibility and safety of synchronous computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) and microwave ablation (MWA) for patients highly suspicious of having malignant GGNs. METHODS: We retrospectively reviewed medical records between July 2020 and April 2023 from our medical center. Eligible patients synchronously underwent PTNB and MWA (either MWA immediately after PTNB [PTNB-first group] or PTNB immediately after MWA [MWA-first group]) at the the physician's discretion. We analyzed the rate of definitive diagnosis and technical success, the length of hospital stay, the postoperative efficacy, and periprocedural complications. RESULTS: Of 65 patients who were enrolled, the rate of definitive diagnosis was 86.2%, which did not differ when stratified by the tumor size, the consolidation-to-tumor ratio, or the sequence of the two procedures (all p > 0.05). The diagnostic rate of malignancy was 83.1%. After the median follow-up duration of 18.5 months, the local control rate was 98.2% and the rate of completed ablation was 48.2%. The rate of perioperative minor and major complications was 44.6% and 6.2%, respectively. The most common adverse events included pain, cough, and mild hemorrhage. Mild hemorrhage took place significantly less frequently in the MWA-first group than in the PTNB-first group (16.7% vs. 45.5%, p < 0.05). CONCLUSION: Synchronous PTNB and MWA are feasible and well tolerated for patients highly suspicious of having malignant GGNs, providing an alternative option for patients who are ineligible for surgical resection.

Transareolar Video-Assisted Thoracoscopic Surgery in Females: A Novel Incision for Pulmonary Ground Glass Nodule Resection.

Purpose: Uniportal video-assisted thoracoscopic surgery (VATS) is recognized for its minimally invasive nature, widely adopted globally. However, the evident scarring it leaves often triggers psychological apprehension and resistance to surgery. Transareolar incision, known for its superior cosmetic outcome with no visible scars, poses challenges in women due to the risk of mammary gland damage. In this report, we present successful pulmonary ground glass nodule (GGN) resection using transareolar VATS in female patients, aiming to address these concerns. Materials and Methods: We retrospectively analyzed the clinical data of 35 female patients who underwent GGN resection through transareolar VATS between August 2020 and March 2022. Results: There were no serious complications or perioperative deaths in this cohort of 35 female patients undergoing GGN resection through transareolar VATS. The operations, including local resection or segmentectomy, had an average duration of 70.1 ± 26.4 minutes, with a tube duration of 4.7 ± 2.1 days and a hospitalization time of 7.2 ± 2.3 days. The surgical approach varied, with 21 cases using transareolar uniport, 8 cases assisted by a 3-mm tiny port, and 6 cases converted to two-port VATS. Scar outcomes varied, with 21 cases showing no scar, 8 cases displaying a microscar, and 6 cases presenting a dominant scar of 1.7 ± 0.5 cm. Postoperative pain scores at 1 week and 1 month were 1.9 ± 0.9 and 1.0 ± 0.9, respectively, and the wound numbness occurred in 2.86% (1/35) of cases. Regarding breast complications, 2 patients suffered delayed healing of the incision. No damage and inflammation of glands were detected by breast B-mode ultrasonography. Conclusions: The transareolar incision emerges as a novel approach for VATS in female patients, offering advantages in terms of pain management and cosmetic outcomes.

[Value of CT Quantitative Parameters in Prediction of Pathological Types
of Lung Ground Glass Nodules].

BACKGROUND: The pathological types of lung ground glass nodules (GGNs) show great significance to the clinical treatment. This study was aimed to predict pathological types of GGNs based on computed tomography (CT) quantitative parameters. METHODS: 389 GGNs confirmed by postoperative pathology were selected, including 138 cases of precursor glandular lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], 109 cases of microinvasive adenocarcinoma (MIA) and 142 cases of invasive adenocarcinoma (IAC). The morphological characteristics of nodules were evaluated subjectively by radiologist, as well as artificial intelligence (AI). RESULTS: In the subjective CT signs, the maximum diameter of nodule and the frequency of spiculation, lobulation and pleural traction increased from AAH+AIS, MIA to IAC. In the AI quantitative parameters, parameters related to size and CT value, proportion of solid component, energy and entropy increased from AAH+AIS, MIA to IAC. There was no significant difference between AI quantitative parameters and the subjective CT signs for distinguishing the pathological types of GGNs. CONCLUSIONS: AI quantitative parameters were valuable in distinguishing the pathological types of GGNs.

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules. RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%. CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

A modified 3D RPN for detecting lung nodules on CT images that exhibited greater sensitivity and CPM than did several previously reported CAD detection models was established.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs). MATERIALS AND METHODS: Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test. RESULTS: Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05). CONCLUSION: A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

Management of screening-detected ground glass nodules: a narrative review.

Wide-scale application of low-dose computed tomography (LDCT) in lung cancer screening has led to an increased detection of ground glass nodule (GGN) lesions. However, there is still no clear management plan for these lesions after detection. Clinicians are usually faced with a dilemma in choosing the best initial management approach that not only limits overtreatment but also avoids the possibility of lesions growing into invasive carcinoma. Most current and past guidelines favor surveillance with computed tomography (CT) as the initial management approach based on the notion that the majority of GGN lesions are indolent tumors. Immediate surgery is generally considered overtreatment and is usually only recommended when the lesion grows in size, persists, or increases its solid component during follow-up CT surveillance. However, due to evolution of surgery to minimal invasive procedures, such as uniportal video-assisted thoracic surgery, and the development of enhanced recovery after thoracic surgery protocols, modern surgery is now safer and associated with less postoperative mortality. Additionally, intraoperative frozen sections can be used to guide resection, making initial management via surgery more attractive than before. Based on these developments, this review recommends that immediate surgery should be considered at the same level as follow-up CT surveillance when making multidisciplinary team decisions for screening-detected GGNs, as it provides both a diagnostic and treatment role.