Purpose:This study aimed to construct a risk prediction model based on radiomics, circulating tumor cells (CTCs), and dual-center clinical data to predict the invasiveness of lung adenocarcinoma, specifically for discriminating between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). The clinical value of this model in the precise diagnosis of early-stage lung adenocarcinoma was investigated to provide a reference for formulating reasonable treatment plans. Patients and Methods:Clinical data, imaging data, CTCs, and pathological information from 202 patients with lung adenocarcinoma were retrospectively collected and analyzed from two medical centers between May 2022 and July 2023. The 146 cases from medical center 1 were randomly divided into a development set and an internal test set at a 3:2 ratio. The 56 cases from medical center 2 served as an external validation set. Machine learning was employed to analyze preoperative CTC counts and CT radiomic features. A feature selection method based on LASSO regression (with λ determined by the minimum criterion) was used to screen out 12 radiomic features. These features were subsequently incorporated into logistic regression to construct three prediction models: (1) a radiomics model based on radiomic features; (2) a CTCs-clinical data model based on the total development set; and (3) a composite clinical data-radiomics-CTCs model integrating the former two. The optimal model was selected to construct a nomogram. Its goodness-of-fit was assessed using a calibration curve (Hosmer-Lemeshow goodness-of-fit test), and its predictive performance was validated in the external validation set. Results:A total of 107 radiomic features were extracted and categorized into 7 groups: 18 (16.8%) first-order features, 24 (22.4%) gray-level co-occurrence matrix (GLCM) features, 14 (13.1%) gray-level dependence matrix (GLDM) features, 16 (15.0%) each for gray-level run length matrix (GLRLM) and gray-level size zone matrix (GLSZM) features, 5 (4.7%) neighboring gray-tone difference matrix (NGTDM) features, and the remaining (13.1%) were shape-based features. In the total development set, significant differences were observed in clinical-imaging semantic features including CEA, CK19, CTC count, and lesion diameter, which were used to construct the clinical model. The area under the curve (AUC) for the radiomics model was 0.896 95% CI:0.832-0.960. The CTCs-clinical model demonstrated superior performance AUC:0.960, 95% CI:0.926-0.994. The composite clinical-radiomics-CTCs model showed the highest predictive accuracy AUC:0.980, 95% CI:0.960-1.000. According to decision curve analysis and the Akaike information criterion, the composite clinical-radiomics-CTCs model outperformed any single clinical or radiomic feature in terms of clinical predictive capability. Conclusion:For assessing the invasiveness of early-stage lung adenocarcinoma, the radiomics approach can effectively discriminate between MIA and IAC. However, compared to single-modality methods, the composite clinical-radiomics-CTCs model offers a novel auxiliary diagnostic method for evaluating the risk of invasiveness in early-stage lung cancer.

Abstract Purpose The prognostic value of the Naples prognostic score in lung cancer remains controversial. Therefore, we performed a meta-analysis of relevant published studies to determine the prognostic value of the Naples prognostic score in patients with lung cancer. Methods We conducted a systematic search of relevant studies in PubMed, Ovid, the Cochrane Library, and Web of Science databases. Data and characteristics of each study were extracted and hazard ratios (HRs) at 95% confidence intervals (95% CI) were calculated to estimate effects. A meta-regression analysis was used to assess the prognostic value of the Naples Prognostic Score in patients with lung cancer. Results A total of 1691 patients from six studies were included in this meta-analysis, with a combined HR of 3.357 (95% CI: 1.964–5.738, P < 0.001); the results suggest that a high Naples Prognostic Score predicts a shorter overall survival (OS) for patients. Conclusion This meta-analysis suggests that a high Naples Prognostic Score may be a predictor of poor prognosis in lung cancer patients. Further large cohort studies are needed to confirm these findings.

Preoperative accurate prediction of lymph node metastasis remains challenging in patients with clinical T1b-c peripheral lung adenocarcinoma. Although lobe-specific lymph node dissection (L-SLND) has been proposed as an alternative to systematic lymph node dissection (SLND), the predictive factors and patterns of lymph node metastasis in this specific patient population have not been fully elucidated. This study retrospectively analyzed 419 patients with cT1b–c peripheral lung adenocarcinoma who underwent lobectomy and SLND at Hebei General Hospital between January 2022 and June 2024. Independent risk factors for lymph node metastasis (LNM) were identified using univariate and multivariate logistic regression analyses. Patients were randomly divided into training and validation cohorts at a 7:3 ratio. A nomogram predictive model was developed and internally validated. Furthermore, the lobe-specific patterns of lymph node metastasis in cT1b–c peripheral lung adenocarcinoma were analyzed. First, a univariate logistic regression analysis was conducted to identify relevant risk factors. Variables that showed statistical significance were then incorporated into a multivariate logistic regression model for further screening. As a result, the multivariate analysis identified smoking history (P=0.026), serum carcinoembryonic antigen (CEA) level (P<0.001), nodule diameter (P=0.027), and consolidation-to-tumor ratio (CTR, P<0.001) as independent risk factors for LNM. The nomogram incorporating these factors demonstrated excellent discriminative ability in both the training and validation cohorts, with area under the curve (AUC) values of 0.904(95

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, with chemotherapy resistance and tumor heterogeneity posing significant challenges. The Chromobox (CBX) protein family, crucial epigenetic regulators in tumor progression, has not been systematically characterized in LUAD. This study aimed to develop a CBX-based molecular classification system for LUAD and explore the mechanistic role of the CDX2-CBX3 regulatory axis in tumor progression. Through multiomics analysis of TCGA-LUAD data, four distinct CBX subtypes were identified, each associated with variations in survival, clinical stage, DNA repair pathway activation, and immune cell infiltration. Mechanistic investigations (ChIP-qPCR, luciferase assays, and gain/loss-of-function experiments) confirmed that CDX2 directly upregulates CBX3 transcription via conserved promoter binding. CDX2 overexpression enhanced migration, invasion, and xenograft growth, whereas CBX3 knockdown suppressed these phenotypic changes. In conclusion, this study defines clinically relevant CBX molecular subtypes in LUAD and reveals the CDX2-CBX3 transcriptional cascade as a novel driver of tumor progression, offering potential targets for precision therapy.

OBJECTIVE: The aim of this study was to develop a machine learning model that can predict spread through air space (STAS) of lung adenocarcinoma preoperatively. STAS is associated with poor prognosis in invasive lung adenocarcinoma. Therefore non-invasive and accurate pre-surgical prediction of STAS in patients with lung adenocarcinoma is essential for individualised patient management. METHODS：We included 138 patients with invasive lung adenocarcinoma who underwent lobectomy, collected their preoperative imaging data and clinical features, built a model for predicting STAS using machine learning and deep learning methods, and validated the efficacy of the model. Finally a nomogram was created based on logistic regression (LR). RESULTS：Imaging histology features showed good model efficacy in both the training set (LR AUC=0.764) and the test set (LR AUC=0.776), and we combined the imaging histology and clinical features to jointly build a nomogram graph (AUC=0.878), extracted the deep learning features, and built a machine learning model based on the ResNET50 algorithm, where the LR AUC=0.918 CONCLUSIONS：This presented radiomics model can be served as a non-invasive for predicting STAS in Infiltrating lung adenocarcinoma.

ObjectiveTo explore the clinical application value of combining circulating tumor cell (CTC) detection with the artificial intelligence imaging software “uAI platform” in predicting the pathological nature of pulmonary nodules (PN). Develop a joint diagnostic system based on the uAI platform and quantitative detection of CTCs, enable simultaneous classification of pulmonary nodules as benign or malignant and assess the degree of infiltration.MethodsA total of 76 patients with pulmonary nodules undergoing surgical treatment were enrolled. Preoperatively, three-dimensional nodule risk stratification (low、medium、high risk) was performed using the uAI platform, and CTC high-throughput detection was conducted. Key indicators were selected through multi-group comparisons (Benign、Malignant、Invasive subgroups) and logistic regression analysis. A multi-dimensional nomogram model was constructed, and its clinical utility was evaluated using ROC curves and clinical decision curves.ResultsComparison between benign and malignant pulmonary nodule groups revealed significant differences in the risk stratification of the uAI platform (proportion of high-risk: 75.61% vs 34.29%) and in the median value of CTC quantitative detection (P<0.001). Multivariate logistic regression analysis demonstrated that high-risk classification by uAI and CTC quantitative detection were independent predictors of malignancy in pulmonary nodules (P<0.05). The nomogram model constructed based on these factors exhibited excellent discrimination, and its combined diagnostic performance was significantly better than that of single indicators (AUC=0.805 vs uAI 0.730/CTC 0.743).ConclusionThe combined uAI-CTC model breaks through the limitations of single-dimension diagnosis, enabling risk stratification of malignant pulmonary nodules and quantitative assessment of infiltration, providing evidence-based support for clinical treatment strategies.

Background: Posterior mediastinal leiomyosarcoma is an extremely rare malignant mesenchymal tumor with no special clinical symptoms, which is easily confused with some common tumors in the posterior mediastinum, affecting the accuracy of the first diagnosis by clinicians and delaying the treatment of patients. Case summary: We report a 59-year-old woman with a space-occupying lesion in the posterior mediastinum. The patient was mistakenly diagnosed with lumbar muscle or vertebral body lesions due to chest and back pain and underwent conservative treatment, but her symptoms did not improve significantly and she gradually developed pain in both lower limbs. Chest computed tomography (CT) scan indicated the left lower lung paraspinal space and underwent standard single-aperture video-assisted thoracoscopic surgery (VATS), which was pathologically confirmed as posterior mediastinal leiomyosarcoma. Conclusion: Complete surgical resection of posterior mediastinal leiomyosarcoma can achieve good clinical results.

PurposeThe purpose of this study is to investigate whether gene mutations can lead to the growth of malignant pulmonary nodules. MethodsRetrospective analysis was conducted on patients with pulmonary nodules at Hebei Provincial People's Hospital, collecting basic clinical information such as gender, age, BMI, and hematological indicators. According to the inclusion and exclusion criteria, 85 patients with malignant pulmonary nodules were selected for screening, and gene mutation testing was performed on all patient tissues to explore the relationship between gene mutations and the growth of malignant pulmonary nodules. ResultsThere is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules (P < 0.05), while there is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules in the subgroup of invasive malignant pulmonary nodules (P < 0.05). ConclusionMutations in the TP53 gene can lead to the growth of malignant pulmonary nodules and are correlated with the degree of invasion of malignant pulmonary nodules.

With advancements in imaging testing and surgical procedures, an increasing number of nodules with smaller diameters and deeper locations have been deemed suitable for surgical intervention. The preoperative localization of these nodules has become essential. In this retrospective single-center study, we aimed to compare the effectiveness and patient comfort associated with the use of a four-hook needle versus a hook-wire needle for preoperative localization. Additionally, we sought to evaluate the impact of different patient postures on localization effectiveness. We retrospectively analyzed the data of 692 patients following preoperative CT-guided localization. The patients were categorized into different groups based on the type of localization needles used and their respective postures during localization. There was no statistical difference in total complications between the four-hook needle group and the hook-wire needle group (P > 0.05). The chest pain score in the four-hook needle group was lower than the hook-wire needle group (P = 0.001). The incidence of decoupling in the four-hook needle group was significantly lower than the hook-wire needle group (P < 0.05). The four-hook needle group had better performance in terms of localization operation time, operation time, intraoperative bleeding and first-day drainage (P < 0.05). Compared with the supine and lateral groups, the prone posture group had better performance in total complications and localization operation time, and worse performance in decoupling and chest pain (P < 0.05). The four-hook needle has better effectiveness on localization and comfort in patient than the hook-wire needle, which is worthy of clinical promotion and application. The patient’s different postures during localization procedure may affect the localization results.

Non-small cell lung cancer (NSCLC) constitutes the majority of lung cancer cases, accounting for over 80%. RNAs in EVs play a pivotal role in various biological and pathological processes mediated by extracellular vesicle (EV). Long non-coding RNAs (lncRNAs) are widely associated with cancer-related functions, including cell proliferation, migration, invasion, and drug resistance. Tumor-associated macrophages are recognized as pivotal contributors to tumorigenesis. Given these insights, this study aims to uncover the impact of lncRNA NORAD in EVs derived from M2 macrophages in NSCLC cell lines and xenograft mouse models of NSCLC. EVs were meticulously isolated and verified based on their morphology and specific biomarkers. The interaction between lncRNA NORAD and SMIM22 was investigated using immunoprecipitation. The influence of SMIM22/GALE or lncRNA NORAD in EVs on glycolysis was assessed in NSCLC cell lines. Additionally, we evaluated the effects of M2 macrophage-derived lncRNA NORAD in EVs on cell proliferation and apoptosis through colony formation and flow cytometry assays. Furthermore, the impact of M2 macrophage-derived lncRNA NORAD in EVs on tumor growth was confirmed using xenograft tumor animal models. The results underscored the potential role of M2 macrophage-derived lncRNA NORAD in EVs in NSCLC. SMIM22/GALE promoted glycolysis and the proliferation of NSCLC cells. Furthermore, lncRNA NORAD in EVs targeted SMIM22 and miR-520g-3p in NSCLC cells. Notably, lncRNA NORAD in EVs promoted the proliferation of NSCLC cells and facilitated NSCLC tumor growth through the miR-520g-3p axis. In conclusion, M2 macrophage-derived lncRNA NORAD in EVs promotes NSCLC progression through the miR-520g-3p/SMIM22/GALE axis.

Background Exploring the clinical application value of combining circulating tumor cell (CTC) with artificial intelligence in predicting the pathological nature of pulmonary nodules. Constructing a prediction model based on factors related to lung cancer to provide reliable prediction criteria for clinical doctors to predict the pathological nature of pulmonary nodules, in order to guide clinical doctors in judging the benign and malignant nature and infiltration degree of pulmonary nodules (PN). Methods This study included a total of 76 patients with PN who underwent surgical treatment. Based on preoperative imaging of the patients, an artificial intelligence imaging system called "United Imaging Intelligence" was used to classify the pulmonary nodules into three levels of "low risk", "medium risk", and "high risk", and the preoperative CTC level of the patients was recorded. Multiple logistic regression analysis was used to analyze the risk factors affecting the nature of the PN and to construct relevant column charts. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic value of artificial intelligence and CTC levels for the nature of PN lesions. Results The artificial intelligence model for grouping benign and malignant PN and the difference in CTC levels have statistical significance (P < 0.05). The results of multifactor logistic regression analysis showed that artificial intelligence high-risk grouping, CTC level, and age are independent risk factors affecting the nature of PN (P < 0.05). We also constructed a column chart to guide clinical doctors in treatment. The area under the curve (AUC) for the artificial intelligence risk grouping and CTC level diagnosis of malignant PN were 78.9% and 74.3%, respectively. Conclusion Artificial intelligence model combined with CTC detection helps improve the accuracy of lung nodule characterization diagnosis and assists in guiding clinical decisions.

Intravascular large B-cell lymphoma (IVLBCL) is an aggressive extranodal large B-cell lymphoma, cocurrence in the same organ with other malignancies is very rare, especially in the lung. Here, we report a rare case of lung adenocarcinoma with IVLBCL. The patient was admitted to the hospital due to diarrhea associated with fever and cough. A computed tomography (CT) scan of the chest showed an irregular patchy high-density shadow in the upper lobe of the right lung with ground-glass opacity at the margin. After admission, the patient was given anti-infection treatment, but still had intermittent low fever (up to 37.5 oC). The pathological diagnosis of percutaneous lung biopsy (PLB) was lepidic-predominant adenocarcinoma with local infiltration, which was proved to be invasive nonmucinous adenocarcinoma of the lung with IVLBCL after surgery. This paper analyzed the clinicopathological characteristics and reviewed the relevant literature to improve the knowledge of clinicians and pathologists and avoid missed diagnosis or misdiagnosis.

Albumin-bilirubin (ALBI) grade was first described in 2015 as an indicator of liver dysfunction in patients with hepatocellular carcinoma. ALBI grade has been reported to have prognostic value in several malignancies including non-small cell lung cancer (NSCLC). The present study aimed to explore the prognostic impact of ALBI grade in patients with small cell lung cancer (SCLC). It retrospectively analyzed 135 patients with SCLC treated at Hebei General Hospital between April 2015 and August 2021. Patients were divided into two groups according to the cutoff point of ALBI grade determined by the receiver operating characteristic (ROC) curve: Group 1 with pre-treatment ALBI grade <=-2.55 for an improved hepatic reserve and group 2 with ALBI grade >-2.55. Kaplan-Meier and Cox regression analysis were performed to assess the potential prognostic factors associated with progression free survival (PFS) and overall survival (OS). Propensity score matching (PSM) was applied to eliminate the influence of confounding factors. PFS and OS (P<0.001) were significantly improved in group 1 compared with in group 2. Multivariate analysis revealed that sex (P=0.024), surgery (P=0.050), lactate dehydrogenase (LDH; P=0.038), chemotherapy (P=0.038) and ALBI grade (P=0.028) are independent risk factors for PFS and that surgery (P=0.013), LDH (P=0.039), chemotherapy (P=0.009) and ALBI grade (P=0.013) are independent risk factors for OS. After PSM, ALBI grade is an independent prognostic factor of PFS (P=0.039) and OS (P=0.007). It was concluded that ALBI grade was an independent prognostic factor in SCLC.

目的 建立人肺腺癌A549细胞株裸鼠移植瘤模型,用增敏剂量的β-榄香烯联合放疗对移植瘤进行干预,探讨放疗增敏机制是否与抑制葡萄糖转运蛋白1(GLUT-1)表达有关.方法 采用细胞悬液接种法建立裸鼠移植瘤模型,将肿瘤体积达到75 mm3左右的20只裸鼠随机分为空白对照组(NS组)、β-榄香烯单药组(ELE组)、β-榄香烯联合放疗组(ELE+RAD组)和单纯放疗组(RAD组).监测干预后各组裸鼠肿瘤体积的变化,获得各组裸鼠的肿瘤生长曲线.通过计算增敏系数,检测45 mg·kg-1 β-榄香烯是否发挥增敏作用,采用Real-Time PCR、Western blotting及免疫组化染色检测各组移植瘤中GLUT-1的表达水平.结果 成功建立A549细胞株裸鼠移植瘤模型,依据各组移植瘤体积变化绘制生长曲线.测得增敏系数(EF)为2.44,提示β-榄香烯的剂量已达到增敏效果.与NS组相比,实验组移植瘤GLUT-1 mRNA及蛋白表达水平均显著降低(P＜0.01);ELE组与RAD组GLUT-1 mRNA及蛋白表达水平无显著差异(P＞0.05);与ELE组、RAD组相比,ELE+RAD组GLUT-1 mRNA及蛋白表达水平均显著降低(P＜0.01).免疫组化结果显示,与NS组比较,实验组GLUT-1的表达均被显著抑制(P＜0.05);其中ELE组和RAD组GLUT-1的表达抑制作用相对较弱,RAD组的抑制作用略强于ELE组,但两组差异无统计学意义(P＞0.05);而ELE+RAD组GLUT-1的表达抑制作用较强(P＜0.05).结论 增敏剂量的β-榄香烯与放疗联合应用可显著抑制裸鼠移植瘤中GLUT-1 mRNA及蛋白表达,明显增强放疗对肺癌裸鼠移植瘤的生长抑制效果,提示β-榄香烯可能通过抑制GLUT-1表达发挥放疗增敏作用.

13岁女性患儿，反复咳嗽、咳痰8年余，多次抗感染治疗，症状反复。术前进行个体化三维重建，制订手术方案为左肺上叶固有段切除术。术后病理证实为肺囊肿，恢复顺利，术后随访1个月无明显不适。三维重建技术在儿童肺部疾病的应用为手术方案及个体化治疗提供了帮助，保证了患儿术后的生活质量。

Objective Despite the vital role of blood perfusion in tumor progression, in patients with persistent pulmonary nodule with ground-glass opacity (GGO) is still unclear. This study aims to investigate the relationship between tumor blood vessel and the growth of persistent malignant pulmonary nodules with ground-glass opacity (GGO). Methods We collected 116 cases with persistent malignant pulmonary nodules, including 62 patients as stable versus 54 patients in the growth group, from 2017 to 2021. Three statistical methods of logistic regression model, Kaplan–Meier analysis regression analysis were used to explore the potential risk factors for growth of malignant pulmonary nodules with GGO. Results Multivariate variables logistic regression analysis and Kaplan–Meier analysis identified that tumor blood vessel diameter ( p = 0.013) was an significant risk factor in the growth of nodules and Cut-off value of tumor blood vessel diameter was 0.9 mm with its specificity 82.3% and sensitivity 66.7%.While in subgroup analysis, for the GGO CTR < 0.5[C(the maximum diameter of consolidation in tumor)/T(the maximum diameter of the whole tumor including GGO) ratio], tumor blood vessel diameter ( p = 0.027) was important during the growing processes of nodules. Conclusions The tumor blood vessel diameter of GGO lesion was closely associated with the growth of malignant pulmonary nodules. The results of this study would provide evidence for effective follow-up strategies for pulmonary nodule screening.

BackgroundA comprehensive understanding of the anatomical variations in the pulmonary bronchi and arteries is particularly essential to the implementation of safe and precise left superior division segment (LSDS) segmentectomy. However, no report shows the relationship between the descending bronchus and the artery crossing intersegmental planes. Thus, the purpose of the present study was to analyze the branching pattern of the pulmonary artery and bronchus in LSDS using three-dimensional computed tomography bronchography and angiography (3D-CTBA) and to explore the associated pulmonary anatomical features of the artery crossing intersegmental planes. Materials and methodsThe 3D-CTBA images of 540 cases were retrospectively analyzed. We reviewed the anatomical variations of the LSDS bronchus and artery and assorted them according to different classifications. ResultsAmong all 540 cases of 3D-CTBA, there were 16 cases (44.4%) with lateral subsegmental artery crossing intersegmental planes (AX(3)a), 20 cases (55.6%) Without AX(3)a in the descending B(3)a or B-3 type, and 53 cases (10.5%) with AX(3)a, 451 cases (89.5%) Without AX(3)a in the Without the descending B(3)a or B-3 type. This illustrated that the AX(3)a was more common in the descending B(3)a or B-3 type (P < 0.005). Similarly, there were 69 cases (36.1%) with horizontal subsegmental artery crossing intersegmental planes (AX(1 + 2)c), 122 cases (63.9%) Without AX(1 + 2)c in the descending B(1 + 2)c type, and 33 cases (9.5%) with AX(1 + 2)c, 316 cases (90.5%) Without AX(1 + 2)c in the Without the descending B(1 + 2)c type. Combinations of the branching patterns of the AX(1 + 2)c and the descending B(1 + 2)c type were significantly dependent (p < 0.005). The combinations of the branching patterns of the AX(1 + 2)c and the descending B(1 + 2)c type were frequently observed. ConclusionsThis is the first report to explore the relationship between the descending bronchus and the artery crossing intersegmental planes. In patients with the descending B(3)a or B-3 type, the incidence of the AX(3)a was increased. Similarly, the incidence of the AX(1 + 2)c was increased in patients with the descending B(1 + 2)c type. These findings should be carefully identified when performing an accurate LSDS segmentectomy.

Objective Lung cancer is a malignancy with high a mortality rate that threatens human health. This study is aimed to explore the correlation among the triglyceride/high-density lipoprotein ratio (TG/HDL-C), non-high-density lipoprotein/high-density lipoprotein ratio (non-HDL-C/HDL-C) and survival of patients with non-small cell lung cancer (NSCLC) undergoing video-associated thoracic surgery (VATS). Methods This retrospective study analyzed 284 patients with NSCLC who underwent VATS at Hebei General Hospital, Shijiazhuang, China. The time-dependent receiver operating characteristic curve was used to determine the optimal cutoff value and evaluate the area under the curve. Kaplan–Meier and Cox regression analyses were performed to determine the prognostic effect. Results The median overall survival (OS) was 46 months. Patients with low TG/HDL-C and low non-HDL-C/HDL-C had a longer OS. The low non-HDL-C/HDL-C group showed a longer mean survival time (59.00 vs. 52.35 months). Multivariate analysis revealed that TG/HDL-C and non-HDL-C/HDL-C were significantly correlated with OS. Conclusions TG/HDL-C and non-HDL-C/HDL-C are associated with the prognosis of patients with NSCLC who received VATS. Preoperative serum TG/HDL-C and non-HDL-C/HDL-C may be effective independent prognostic factors for predicting the outcomes of patients with NSCLC.