Stage Lung Cancer Research Articles

Prognostic impact based on tumor diameter of pathological N1 lymph node metastases for non-small cell lung cancer.

The N parameter in the tumor-node-metastasis (TNM) classification of lung cancer is categorized according to the location of nodal metastasis, while that for some other cancers are classified according to the size and number of metastatic foci. In lung cancer, the impact of size of nodal metastasis on prognosis is unclear. This analysis aims to examine whether it is possible to subdivide the pathological N (pN) factor based on the tumor diameter of lymph node metastases. We studied 35 cases of adenocarcinoma and 26 cases of squamous cell carcinoma of the lung. The maximum diameter of lymph node metastasis was measured, and the relationship between the maximum diameter of lymph node metastases and prognosis was investigated. Squamous cell carcinoma cases had a significantly larger maximum tumor diameter of lymph node metastasis than adenocarcinoma cases (P=0.03). Based on receiver operating characteristic (ROC) curve results for the adenocarcinoma cases, we set 4 mm as a diameter cutoff value. The 5-year overall survival (OS) rate was 85.6% for patients with diameters ≤4 mm and 57.7% for those with diameters >4 mm; this difference was statistically significant (P=0.04). On univariate analysis using a Cox proportional hazards model, significant differences were observed in metastatic lymph node diameter (≤4 vs. >4 mm) (P=0.04), sex (P=0.04), and pathological T (pT) status (pT1 and 2 vs. pT3 and 4) (P=0.03). However, similar results were not obtained for patients with squamous cell carcinoma. The tumor diameter of pathological N1 lymph node metastases impacts the prognosis of patients with lung adenocarcinoma. If pathological N1 can be further subdivided according to tumor diameter of lymph node metastases, a more accurate prognosis may become possible. However, pathological type must be considered for staging in lung cancer. While the pN parameter for most cancers considers tumor size of lymph node metastasis, this does not apply to lung cancer.

Dissection of intratumor microbiome–host interactions at single-cell level in lung cancer

The intratumor microbiome, one of the hallmarks of cancer, plays a crucial role in cancer progression through interaction with the host. However, the underlying mechanisms remain poorly understood. Herein, we investigated the heterogeneity of host–microbiome interactions at the single-cell level by integrating six single-cell transcriptomic lung cancer datasets using single-cell analysis of host–microbiome interactions (SAHMI). Our findings indicate that primary tumor tissues display a high proportion of fungi-associated cells, whereas metastatic brain tissues predominantly feature bacteria-associated cells. A distinct distribution of fungal and bacterial taxa across various cell types was observed. Notably, the presence of specific bacteria significantly influences the transcriptome of resident host cells, including T cells and macrophages, by modulating pathways related to ribosomal RNA (rRNA) processing, cellular responses to stress and stimuli, and the metabolism of RNA and proteins. Finally, specific cell-associated bacteria are significantly correlated with clinical features, such as lung cancer stages and smoking frequency. These single-cell insights into microbiome–host interactions facilitate our understanding of lung cancer development and progression, offering potential micro-ecological and diagnostic insights.

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach

BackgroundIn oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines. ObjectivesThis study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment. MethodsLung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports. ResultsWe evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification. ConclusionThis study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.

The predictive accuracy of systematic versus selective endobronchial ultrasound-guided transbronchial needle aspiration for assessing mediastinal staging in non-small cell lung cancer

The predictive accuracy of systematic versus selective endobronchial ultrasound-guided transbronchial needle aspiration for assessing mediastinal staging in non-small cell lung cancer

1214P Imaging AI prognosis of early stage lung cancer using CT radiomics

1214P Imaging AI prognosis of early stage lung cancer using CT radiomics

1191P Efficient lung cancer stage prediction and outcome informatics with Bayesian deep learning and MCMC method

1191P Efficient lung cancer stage prediction and outcome informatics with Bayesian deep learning and MCMC method

Additional benefit of endoscopic ultrasound with bronchoscope-guided fine needle aspiration to endobronchial ultrasound-guided transbronchial needle aspiration in the evaluation of lung cancer: a systematic review and meta-analysis.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and endoscopic ultrasound with bronchoscope-guided fine needle aspiration (EUS-B-FNA) are minimally invasive procedures for the diagnosis and staging of lung cancer. This study aimed to investigate the additional diagnostic value of EUS-B-FNA following EBUS-TBNA. We performed a systematic literature review of PubMed, Embase, and the Cochrane Central Register databases and extracted the studies reporting the implementation of the combined EBUS-TBNA/EUS-B-FNA. A proportional meta-analysis was conducted to determine the pooled diagnostic yield of this procedure. We identified nine studies involving 2,375 patients. The overall pooled diagnostic yield of EBUS-TBNA alone and combined EBUS-TBNA/EUS-B-FNA was 0.87 [95% confidence interval (CI): 0.79-0.95, I2=96.55%] and 0.92 (95% CI: 0.85-0.99, I2=97.89%), respectively. Adding EUS-B-FNA to EBUS-TBNA increased the diagnostic yield by approximately 0.05. There was statistical heterogeneity among the studies (I2=54.49%). Among the 832 patients in seven studies, additional diagnostic benefits of EUS-B-FNA were observed in 37 lesions. The most common diagnosed lesion was in station 4L (n=10), followed by station 5 (n=8) and station 7 (n=8). In pooled estimates, the addition of EUS-B-FNA to EBUS-TBNA increased the diagnostic yield for the diagnosis and staging of lung cancer. Nodal station 4L, station 5, and station 8 were lesions frequently diagnosed by the addition of EUS-B-FNA. Because of statistical between-study heterogeneity, our findings should be interpreted with caution.

Comparative Effectiveness of the Revised American Joint Committee on Cancer Staging System.

The American Joint Committee on Cancer (AJCC) staging manual is periodically updated. This study aims to examine the staging performances in delineating stage-specific survival curves and to evaluate the reclassification of cases based on the 7th and 8th AJCC editions in Taiwan. Data were sourced from the Taiwan Cancer Registry for cases diagnosed in 2017 (7th edition) and 2018 (8th edition), each with a 2-year follow-up period. Performance was assessed using the area under the receiver operating characteristic curve and the Lorenz curve-derived Gini index, along with 2-year survival rates for evaluating patient prognoses. The 8th edition showed superior staging in four specific cancer types. Oropharyngeal cancer exhibited more variable 2-year survival rates across stages, and liver cancer showed a clearer decline in survival rates with advancing stages. The 8th edition also improved prognostic staging for non-small cell lung cancer and reclassified 26.4% of stage 4 prostate cancer patients under the 7th edition into stages 3 or 4A, showing 2-year survival rates above 90%. Our study highlights the 8th edition's refined capacity for stage-specific survival distinctions and reclassification of cases to enhance prognostication in certain cancers within Taiwan. We recommend a comprehensive evaluation when adopting a new edition or version.

The links between symptom burden, illness perception, psychological resilience, social support, coping modes, and cancer-related worry in Chinese early-stage lung cancer patients after surgery: a cross-sectional study

ObjectivesThis study aims to investigate the links between the clinical, demographic, and psychosocial factors and cancer-related worry in patients with early-stage lung cancer after surgery.MethodsThe study utilized a descriptive cross-sectional design. Questionnaires, including assessments of cancer-related worry, symptom burden, illness perception, psychological resilience, coping modes, social support and participant characteristics, were distributed to 302 individuals in early-stage lung cancer patients after surgery. The data collection period spanned from January and October 2023. Analytical procedures encompassed descriptive statistics, independent Wilcoxon Rank Sum test, Kruskal-Wallis- H- test, Spearman correlation analysis, and hierarchical multiple regression.ResultsAfter surgery, 89.07% had cancer-related worries, with a median (interquartile range, IQR) CRW score of 380.00 (130.00, 720.00). The most frequently cited concern was the cancer itself (80.46%), while sexual issues were the least worrisome (44.37%). Regression analyses controlling for demographic variables showed that higher levels of cancer-related worry (CRW) were associated with increased symptom burden, illness perceptions, and acceptance-rejection coping modes, whereas they had lower levels of psychological resilience, social support and confrontation coping modes, and were more willing to obtain information about the disease from the Internet or applications. Among these factors, the greatest explanatory power in the regression was observed for symptom burden, illness perceptions, social support, and sources of illness information (from the Internet or applications), which collectively explained 52.00% of the variance.ConclusionsHealthcare providers should be aware that worry is a common issue for early stage lung cancer survivors with a favorable prognosis. During post-operative recovery, physicians should identify patient concerns and address unmet needs to improve patients’ emotional state and quality of life through psychological support and disease education.

Efficacy of Respiration-controlled Radiotherapy Among Patients With Locally Advanced-stage Lung Cancer: A Population-based Study.

Definitive radiotherapy is the main treatment modality for patients with locally advanced stage lung cancer and a good performance status who are ineligible for surgery. Respiration-controlled radiotherapy (RCRT) has been recommended, but its effectiveness has been debated in the literature. Therefore, we aimed to study the efficacy of RCRT in definitive radiotherapy for locally advanced-stage lung cancer. We identified lung cancer patients diagnosed between 2011 and 2021 using data from the Taiwan Cancer Registry and related databases. In our primary analysis, we applied propensity score weighting (PSW) to balance observable potential confounders. We then compared the hazard ratio (HR) of death between the RCRT group and the non-RCRT group. Additionally, we conducted a comprehensive assessment of other outcomes and performed various supplementary analyses. The primary analysis included 3,020 patients. Overall survival did not significantly differ between the RCRT group and the non-RCRT group, with a PSW-adjusted HR of 0.79 (95% confidence interval=0.49-1.28, p=0.342). These findings were consistent with other outcomes and supplementary analyses. In locally advanced-stage lung cancer patients who received definitive radiotherapy, survival did not significantly differ between those treated with RCRT and those treated without RCRT. To our knowledge, this is the first population-based study on this topic.

Thoracic sarcopenia was a poor prognostic predictor in patients receiving immunotherapy retain-->for advanced non-small-cell retain-->lung cancer

Sarcopenia, as measured at the level of the third lumbar (L3) has been shown to predict the survival of cancer patients. However, many patients with advanced non-small cell lung cancer (NSCLC) do not undergo routine abdominal imaging. The objective of this study was to investigate the association of thoracic sarcopenia with survival outcomes among patients who underwent immunotherapy for NSCLC. In this retrospective study, patients who initiated immunotherapy for advanced NSCLC from 2019 to 2022 were enrolled. and detailed patient data were collected. Cross sectional skeletal muscle area was calculated at the fifth thoracic vertebra (T5) on pretreatment chest computed tomography (CT) scan. Gender-specific lowest quartile values was used to define sarcopenia. The risk factors were analyzed using Cox analyses. The log-rank test and the random survival forest (RSF) were used to compare progression free survival (PFS). The model's performance was assessed using calibration curve and the receiver operating characteristic curve (ROC). A total of 242 patients was included (discovery cohort n=194, validation cohort n=48). In the discovery cohort, patients with sarcopenia exhibited significantly poorer PFS (p<0.001) than patients without sarcopenia. Univariate cox regression revealed that sarcopenia, lung cancer stage, body mass index, smoking status, and neutrophil-to-lymphocyte ratio were predictors of poor PFS. A RSF model was constructed based on the aforementioned parameters, to evaluate the model's efficacy, the ROC curve was utilized. with an area under the curve for predicting 6-month PFS of 0.68 and for 12-month PFS of 0.69. The prediction models for survival outcomes built by the discovery cohort showed similar performance in the validation cohort. Sarcopenia at T5 is independent prognostic factors in patients who received immunotherapy for advanced NSCLC.

Utility of adding esophageal to endobronchial endosonography when staging lung cancer: A randomized trial

IntroductionBoth combined endobronchial ultrasonography (EBUS) and transesophageal bronchoscopic ultrasonography (EUS-B) and EBUS alone have been recommended for preoperative mediastinal staging of non-small cell lung cancer (NSCLC). However, no randomized study comparing these two methods has been published. The purpose of the present study was to compare the sensitivity of EBUS and that of combined EBUS and EUS-B (EBUS/EUS-B) in terms of detecting N2/N3 disease during staging of NSCLC.MethodsPatients with known or suspected, potentially operable NSCLC were recruited and randomized to undergo EBUS or EBUS/EUS-B under conscious sedation. The primary endpoint was a comparison of the sensitivity of EBUS alone and EBUS/EUS-B.ResultsA total of 240 patients were enrolled and randomized, among whom 219 (105, EBUS group; 114, EBUS/EUS-B group) were included in analysis. The sensitivities of EBUS and EBUS/EUS-B in terms of detecting N2/N3 disease were 75.0 and 79.3% respectively (p=0.698). In the EBUS/EUS-B group, only EUS-B yielded diagnostic results in two patients; the sensitivity thus increased from 72.4 to 79.3% on addition of EUS-B to EBUS.ConclusionsThe difference in the sensitivities of EBUS alone and EBUS/EUS-B in terms of diagnosing N2/N3 disease was not statistically significant. Although the increase in sensitivity with the addition of EUS-B is modest, it is maximized when EUS-B is used to sample lymph nodes not accessible by EBUS alone.

A Hybrid Lung Cancer Model for Diagnosis and Stage Classification from Computed Tomography Images

Pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54 %, while the classification precision among the three classes was 98.63 %. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using Deep Learning.

Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling.

Predictive Value of Lymphocyte-to-Neutrophil Ratio and Platelet-to-Neutrophil Ratio on PD-L1 Expression in Lung Cancer.

This study aimed to examine the predictive effect of the lymphocyte-to-neutrophil ratio (LNR) and the platelet-to-neutrophil ratio (PNR) on the expression of programmed death receptor ligand 1 (PD-L1) in patients diagnosed with lung cancer. The clinical records of 86 patients diagnosed with lung cancer between January 2020 and February 2022 at Fu Yang People's Hospital were retrospectively analyzed. The records included information on age, gender, smoking history, hematological indices at the time of admission, staging of the lung malignancy, histopathological subtype, comorbidities, and the expression levels of PD-L1. Patients were stratified into two distinct cohorts based on their PD-L1 expression levels: Those with an expression level greater than or equal to 1% were classified into the PD-L1 positive expression group, while the remainder were categorized as the PD-L1 negative expression group. Univariate analysis and multivariate logistic regression analysis were used to identify the influencing factors of PD-L1, and the diagnostic efficacy was calculated using the receiver operating characteristic (ROC) curve. Upon analysis, the PD-L1 positive expression group manifested notably lower values as compared to their counterparts in the PD-L1 negative expression group (LNR: 0.262 ± 0.105 vs. 0.390 ± 0.201; PNR: 41.03 [29.64, 50.11] vs. 49.50 [37.38, 73.83]), and these differences were statistically significant. There was a notable disparity in PD-L1 expression based on gender, with males exhibiting a statistically significant higher positivity rate compared to females. Furthermore, patients in Stages I-III of the disease demonstrated a markedly elevated PD-L1 positivity rate compared to those in Stage IV (p < 0.05). Incorporating univariates with statistical differences into multivariate logistic regression analysis suggests that stage and LNR are independent risk factors for PD-L1 negative expression. ROC curve analyses revealed that the area under the ROC curve (AUC) for LNR as an indicator for PD-L1 positive expression stood at 0.706, while the AUC for PNR was calculated at 0.687. PD-L1 expression is correlated with gender and lung cancer staging, and LNR and PNR have a predictive value for PD-L1 expression.

Pattern of Clinical Presentation, Staging and Surgical Treatment of Lung cancer in 250 Consecutive Patients.

Introduction: GLOBOCAN 2022 showed lung cancer to be leading cancer in Nepal accounting for about 11% (2431) of all new cancer cases and about 15% (2207) of all cancer deaths. We reviewed the pattern, clinical presentation, stage and multimodality treatment of lung cancer results in Nepal. Methods: Two hundred fifty consecutive patients with clinical stages of I-IIIA, who were considered for lung resectional surgery were studied. Bronchoscopy and Computed Tomography were used for diagnosis and staging. Results: Surgery alone was done in 36% cases, rest of the patients (64%) underwent multimodality approach. Lobectomy, bilobectomy, pneumonectomy, sub lobar resection and sleeve resection were done in 85.6%, 4%, 6.4%, 2% and 2% respectively. Final histology revealed squamous cell carcinoma, adeno carcinoma, and others in 72.4%, 18.4%, 9.2% respectively. Final pathological report showed stage I, II, and III in 14.8%, 30% and 55.2% cases. Thirty-day mortality was observed in 1.6% of cases. Air leak was most common post operative complication (8.4%). Conclusion: Squamous cell carcinoma still remains the commonest pathological variant of lung cancer in Nepal. Lobectomy remains the most commonly performed surgical procedure

CTSG may inhibit disease progression in HIV-related lung cancer patients by affecting immunosuppression

ObjectivesLung cancer is an independent risk factor for pulmonary complications following HIV infection. This study aimed to examine the expression and clinical significance of Cathepsin G (CTSG) protein in both non-HIV and HIV-related lung cancers.MethodsThe data related to lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) in the TCGA dataset and the data related to healthy individuals in the GTEx dataset, the GEPIA2 database was used to excavate the distinction in the expression of CTSG protein in non-small cell lung cancer (NSCLC) tissues versus normal non-cancerous tissues. The Ualcan database was used to compare the differences in CTSG expression at different stages of LUAD and LUSC. Immunohistochemistry (IHC) was used to detect the expression of CTSG proteins in the pathological tissues of patients with HIV-related lung cancer and patients with lung cancer without co-infection, the Kaplan-Meier method was used for survival analysis.ResultsWe observed that CTSG expression in NSCLC is lower compared to adjacent non-tumor tissues and correlates with NSCLC clinical stage. CTSG protein expression in HIV-related lung cancer tissues was lower than in adjacent tissues and lower than in lung cancer tissues without HIV infection, with a statistically significant difference (P < 0.05). It correlated with CD4 + T cell count and CD4+/CD8 + T cell ratio, as well as with the pathological type, distant metastasis, and clinical stage of HIV-related lung cancer, all with statistical significance (P < 0.05).ConclusionsCTSG could potentially mitigate disease advancement in HIV-related lung cancer patients by inhibiting immune depletion, serving as a prospective immunotherapeutic target for both non-HIV and HIV-associated lung cancers.

Multiplex plasma protein assays as a diagnostic tool for lung cancer.

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.

EBUS-TBNA in mediastinal staging of non-small cell lung cancer: comparison with pathological staging.

Although EBUS-TBNA combined with EUS-FNA or EUS-B-FNA stands as the primary approach for mediastinal staging in lung cancer, guidelines recommend mediastinoscopy confirmation if a lymph node identified on chest CT or showing increased PET scan uptake yields negativity on these techniques. This study aimed to assess the staging precision of EBUS/EUS. We conducted a retrospective study comparing the clinical staging of non-small cell lung cancer patients undergoing EBUS/EUS with their post-surgery pathological staging. We analyzed the influence of histology, location, tumor size, and the time lapse between EBUS and surgery. Patients with N0/N1 staging on EBUS/EUS, undergoing surgery, and with at least one station approached in both procedures were selected. Post-surgery, patients were categorized into N0/N1 and N2 groups. Among the included patients (n = 47), pathological upstaging to N2 occurred in 6 (12.8%). Of these, 4 (66.7%) had a single N2 station, and 2 (33.3%) had multiple N2 stations. The adenopathy most frequently associated with upstaging was station 7. None of the analyzed variables demonstrated a statistically significant difference in the occurrence of upstaging. PET scan indicated increased uptake in only one of these adenopathies, and only one was visualized on chest CT. Upstaging proved independent of the studied variables, and only 2 patients with negative EBUS/EUS would warrant referral for mediastinoscopy. Exploring other noninvasive methods with even greater sensitivity for detecting micrometastatic lymph node disease is crucial.