Non-small Cell Lung Cancer Lymph Research Articles

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics.

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

Diagnostic value of dynamic 18F-FDG PET/CT imaging in non-small cell lung cancer and FDG hypermetabolic lymph nodes.

We aimed to investigate the diagnostic value of dynamic 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography in non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes. Patients who made an active appointment for positron emission tomography-computed tomography were randomly enrolled by referring to previous imaging data and clinical information. Finally, 34 histopathologically confirmed non-small cell lung cancers (18 adenocarcinoma and 16 squamous cell carcinoma cases) were prospectively studied using dynamic and static 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography imaging (the diagnostic study has not yet been registered on a clinical trial platform). In dynamic positron emission tomography images, a volume of interest, defined by the thoracic aorta, was selected for estimating the arterial input function. Patlak and irreversible two-tissue compartment model analyses were performed based on the pixel points to obtain first-order characteristic kinetic parameters for each lesion and hypermetabolic lymph node. The first-order characteristic kinetic parameters were obtained based on the basic data of dynamic positron emission tomography images in the corresponding model and the lesion delineation of region-of-interest based on computed tomography images, such as V_Median (the median gray intensity of V), k3_Entropy, VB_Entropy, K1_Uniformity, and ki_Uniformity. The first-order characteristic kinetic parameters were also modeled by logistic regression for the differential diagnosis of non-small cell lung cancer and hypermetabolic lymph nodes. Maximum and mean standard uptake values (SUVmax and SUVmean, respectively) were obtained from static positron emission tomography images. The diagnostic efficacy of the parameters was evaluated using the receiver operating characteristic curve and the DeLong test. There was a significant difference in the V_Median values of adenocarcinoma and squamous cell carcinoma. The regression models for K1, k3, and V provided good predictions of adenocarcinoma and squamous cell carcinoma typology. Significant differences were observed in k3_Entropy, VB_Entropy, K1_Uniformity, and ki_Uniformity between benign and malignant lymph nodes. The regression model of Ki, VB, and k3 could make a good prediction for identifying benign and malignant lymph nodes. Dynamic 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography imaging showed high diagnostic value in the staging of non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes, and can be of great use in non-small cell lung cancer lymph node staging and surgical decision-making.

Lymph node assessment in early stage non-small cell lung cancer lymph node dissection or sampling?

Lymph node assessment is an essential component of the treatment of lung cancer. Identification of the correct "N" stage is important for staging which in turn determines treatment. Assessment of lymph nodes may be accomplished using imaging with CT scan and PET-CT, invasive techniques such as mediastinoscopy, endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) or endoscopic ultrasound fine needle aspiration (EUS-FNA). Ultimately, regardless of any pre-resection assessment, lymph nodes must be assessed at the time of resection. The question to be addressed in this report is the role of mediastinal lymph node dissection versus lymph node sampling. However, the issues surrounding lymph node assessment in NSCLC are complex, depending on clinical stage, imaging and histology.

Advances in Lymph Node Metastasis and Lymph Node Dissection in Early Non-small Cell Lung Cancer

肺癌是目前我国发病率和死亡率均居首位的恶性肿瘤，其中以非小细胞肺癌为主要病理类型。淋巴结转移是非小细胞肺癌最常见和最主要的转移途径，也是影响肺癌分期和预后最重要的因素。由于目前通过现有手段术前很难准确判断早期非小细胞肺癌患者的淋巴结受累情况。因此，在早期非小细胞肺癌中，尤其是在临床Ⅰ期非小细胞肺癌患者中，淋巴结清扫方式一直存在很大争议。本文就非小细胞肺癌淋巴结转移的规律及清扫方式进行综述。

Incidence and clinical relevance of non-small cell lung cancer lymph node micro-metastasis detected by staging endobronchial ultrasound-guided transbronchial needle aspiration.

Approximately twenty percent of lymph node (LN) negative non-small cell lung cancer (NSCLC) patients who undergo curative intent surgery have pan-cytokeratin immunohistochemistry (IHC)-detectable occult micro-metastases (MMs) in resected LNs. The presence of the MMs in NSCLC is associated worsened outcomes. As a substantial proportion of NSCLC LN staging is conducted using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), we sought to determine the frequency of detection of occult MMs in EBUS-TBNA specimens and to evaluate the impact of MMs on progression-free and overall survival. We performed retrospective IHC staining for pan-cytokeratin of EBUS-TBNA specimens previously deemed negative by a cytopathologist based on conventional hematoxylin and eosin staining. The results were correlated with clinical variables, including survival outcomes. Of 887 patients screened, 44 patients were identified meeting inclusion criteria with sufficient additional tissue for testing. With respect to the time of the EBUS-TBNA procedure, 52% of patients were clinical stage I, 34% clinical stage II, and clinical 14% stage IIIa NSCLC. Three patients (6.8%) were found to have cytokeratin positive MMs. All 3 MMs detected were at N2 LN stations. The presence of MMs was associated with significantly decreased progression-free (median 210 vs. 1,293 days, P=0.0093) and overall survival (median 239 vs. 1,120 days, P=0.0357). Occult LN MMs can be detected in EBUS-TBNA specimens obtained during staging examinations and are associated with poor clinical outcomes. If prospectively confirmed, these results have significant implications for EBUS-TBNA specimen analyses and possibly for the NSCLC staging paradigm.

Incidence and clinical relevance of non-small cell lung cancer lymph node micro-metastasis detected by staging endobronchial ultrasound-guided transbronchial needle aspiration

Incidence and clinical relevance of non-small cell lung cancer lymph node micro-metastasis detected by staging endobronchial ultrasound-guided transbronchial needle aspiration

Expression of epidermal growth factor receptor and kirsten rat sarcoma viral oncogene in non-small cell lung cancer lymph node metastasis and the clinical significance

Objective To investigate epidermal growth factor receptor(EGFR)and kirsten rat sarcoma viral oncogene(KRAS)expression in non-small cell lung cancer(NSCLC) and lymphatic metastases, and to explore the roles of EGFR and KRAS in NSCLC lymphatic metastases. Methods By collecting surgically resected and pathologically confirmed cases of NSCLC, get 186 cases of complete clinical data,extraction of tumor cell DNA, polymerase chain reaction(PCR)amplification and sequencing.To observe EGFR and KRASexpression in NSCLCand lymphatic metastasis,and to analyse with SPSS 14. 0 statistical software. Results Positive rate of EGFR gene expression in NSCLC was 34. 41%(64 cases).EGFR gene expression was positively correlated with the differentiation and lymphaden metastases.Positive rate of KRAS expression in gastric cancer was 36. 56%(68 cases).KRAS expression was positively correlated with the lymphaden metastases and clinical stages.The mutation rate of KRAS had significant difference in NSCLC and lymph node metastasis, but the mutation rate of EGFR had no significant difference. Conclusion EGFR and KRAS were high expression in NSCLC tissue, and it may give some guideline for the clinical diagnosis,illness evaluation, treatment and prognosis of gastric cancer. Key words: Non-small cell lung cancer; Epidermal growth factor receptor; Kirsten rat sarcoma viral oncogene; Lymphatic metastasis

The preliminary study on molecular biologic staging of non-small cell lung cancer lymph nodes

目的 应用荧光定量聚合酶链反应(PCR)及免疫组织化学方法对行根治性非小细胞肺癌手术患者的阴性淋巴结进行检测和分析,形成分子生物学分期.方法 共34例非小细胞肺癌患者根治术中193枚常规病理阴性淋巴结,进行CEAmRNA的荧光定量PCR及P53和角蛋白(AE1/AE3)免疫组织化学定性检测.根据检测结果重新确定患者的TNM分期,形成分子生物学分期,并进行平均40个月的随访.结果 肺癌阴性淋巴结CEAmRNA荧光定量PCR检测显示21.7%(42/193)阴性淋巴结阳性,共17例(17/34,50%)患者;8例患者常规病理分期上调;9例患者淋巴结阳性数目增加.肺癌阴性淋巴结P53免疫组织化学检测显示9.8%(19/193)阴性淋巴结阳性,共11例(11/34,32.4%)患者,2例患者常规病理分期上调;7例患者淋巴结阳性数目增加.肺癌阴性淋巴结AE1/AE3免疫组织化学检测显示18.6%(36/193)阴性淋巴结阳性,共15例患者(15/34,44.1%),4例患者常规病理分期上调;11例患者淋巴结阳性数目增加.三者之间差异有统计学意义(P＜0.05).结论 分子生物学技术及标记物可以更敏感的检出肺癌微转移,形成分子生物学分期.这种分期及微转移与非小细胞肺癌预后相关。