Predicting occult lymph node metastasis by nomogram in patients with lung adenocarcinoma ≤2 cm

Preoperative prediction of occult lymph node metastasis in patients with non-small cell lung cancer: a simple and widely applicable model

OBJECTIVE

Lymph node metastasis (LNM) is one of the most common pathways of metastasis in non-small cell lung cancer (NSCLC). Preoperative assessment of occult lymph node metastasis (OLNM) in NSCLC patients is beneficial for selecting appropriate treatment plans and improving patient prognosis.

METHOD

A total of 370 NSCLC patients were included in the study. Univariate and multivariate logistic regression analysis were used to screen potential risk factors for OLNM in preoperative NSCLC patients. And establish a nomogram for OLNM in NSCLC patients before surgery. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the established nomogram.

RESULT

Both univariate and multivariate logistic regression analyses suggested that multiple tumors, ERBB2 missense mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin are potential risk factors for OLNM in NSCLC patients. The constructed nomogram was evaluated, and the consistency index (C-index) and area under the ROC curve of the model were both 0.846. The calibration curve showed that the predicted values of the model had a high degree of fit with the actual observed values, and DCA suggested that the above indicators had good utility.

CONCLUSION

The personalized scoring prediction model constructed based on multiple tumors, ERBB2 miss mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin can screen NSCLC patients who may have OLNM.

Development and external validation of a nomogram for predicting lymph node metastasis in 1-3 cm lung adenocarcinoma

Aim: This study aimed to investigate the risk factors for lymph node metastasis in 1-3 cm adenocarcinoma and develop a new nomogram to predict the probability of lymph node metastasis.Materials & methods: This study collected clinical data from 1656 patients for risk factor analysis and an additional 500 patients for external validation. The logistic regression analyses were employed for risk factor analysis. The least absolute shrinkage and selection operator regression was used to select variables, and important variables were used to construct the nomogram and an online calculator.Results: The nomogram for predicting lymph node metastasis comprises six variables: tumor size (mediastinal window), consolidation tumor ratio, tumor location, lymphadenopathy, preoperative serum carcinoembryonic antigen level and pathological grade. According to the predicted results, the risk of lymph node metastasis was divided into low-risk group and high-risk group. We confirmed the exceptional clinical efficacy of the model through multiple evaluation methods.Conclusion: The importance of intraoperative frozen section is increasing. We discussed the risk factors for lymph node metastasis and developed a nomogram to predict the probability of lymph node metastasis in 1-3 cm adenocarcinomas, which can guide lymph node resection strategies during surgery.

A clinical nomogram for predicting occult lymph node metastasis in patients with non-small-cell lung cancer ≤2 cm

OBJECTIVES

Sublobar resection has been shown to be feasible for non-small-cell lung cancers (NSCLC) <2 cm in size based on several prospective studies. However, the prognosis of clinical N0 patients who experience an N-stage upgrade after surgery [known as occult lymph node metastasis (OLM)] may be worse. The ability of predict OLM in patients eligible for sublobar resection remains a controversial issue.

METHODS

Patients with NSCLC ≤2 cm in diameter and containing a solid component who underwent surgical treatment at the Affiliated Hospital of Qingdao University were retrospectively enrolled, and 1:1 case matching was performed. The risk factors were identified through logistic regression analyses and theoretical criteria, followed by the development of a nomogram that was evaluated using 200 iterations of 10-fold cross-validation.

RESULTS

After case matching, 130 pairs of patients were selected for modelling. According to the multivariable logistic regression analysis, the carcinoembryonic antigen level, consolidation tumour ratio, mean computed tomography number and tumour margin were included in the nomogram. The cross-validated average area under the receiver operating characteristic curve was found to be 0.86. Furthermore, calibration curve and decision curve analyses demonstrated the excellent predictive accuracy and clinical utility of the nomogram respectively.

CONCLUSIONS

By utilizing accessible characteristics, we developed a nomogram that predicts the probability of OLM in patients with NSCLC ≤2 cm with a solid component. Risk stratification with this nomogram could aid in surgical method decision-making.

CLINICAL REGISTRATION NUMBER

Not applicable.

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics

BACKGROUND

The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next-generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed.

METHODS

Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort.

RESULTS

Twenty-four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN-positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild-type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780).

CONCLUSION

This study suggests that the integration of genomic profiling and clinical features identifies early-invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time

PURPOSE

Latent lymph node metastasis is a clinical concern in the surgical treatment of non-small cell lung cancer (NSCLC). The present study identified a simple tool, including the volume-doubling time (VDT), for evaluating the risk of nodal metastasis.

METHODS

We reviewed, retrospectively, 560 patients who underwent radical resection for cN0M0 NSCLC. The whole tumor VDT and solid component VDT (SVDT) for differentiating the histological type and adenocarcinoma subtype were analyzed and a nomogram was constructed using variables selected through a stepwise selection method. The model was assessed through a calibration curve and decision curve analysis (DCA).

RESULTS

Lymph node metastases were detected in 89 patients (15.9%). The SVDT tended to be longer in patients with adenocarcinoma (294.5 days, p < 0.0001) than in those with other histological types of NSCLC, but was shorter when the solid/micropapillary component was predominant (127.0 days, p < 0.0001). The selected variables (tumor location, solid component diameter, consolidation tumor ratio, SVDT, and carcinoembryonic antigen) demonstrated significant differences and were used for the nomogram. The calibration curve indicated consistency, and the DCA showed validity across most threshold ranges from 0 to 68%.

CONCLUSIONS

The established nomogram is a useful tool for the preoperative prediction of lymph node metastasis, and the SVDT was the most influential factor in the nomogram.

A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach

Through meticulous examination of lymph nodes, the stage and severity of cancer can be determined. This information is invaluable for doctors to select the most appropriate treatment plan and predict patient prognosis; however, any oversight in the examination of lymph nodes may lead to cancer metastasis and poor prognosis. In this review, we summarize a significant number of articles supported by statistical data and clinical experience, proposing a standardized evaluation protocol for lymph nodes. This protocol begins with preoperative imaging to assess the presence of lymph node metastasis. Radiomics has replaced the single-modality approach, and deep learning models have been constructed to assist in image analysis with superior performance to that of the human eye. The focus of this review lies in intraoperative lymphadenectomy. Multiple international authorities have recommended specific numbers for lymphadenectomy in various cancers, providing surgeons with clear guidelines. These numbers are calculated by applying various statistical methods and real-world data. In the third chapter, we mention the growing concern about immune impairment caused by lymph node dissection, as the lack of CD8 memory T cells may have a negative impact on postoperative immunotherapy. Both excessive and less lymph node dissection have led to conflicting findings on postoperative immunotherapy. In conclusion, we propose a protocol that can be referenced by surgeons. With the systematic management of lymph nodes, we can control tumor progression with the greatest possible likelihood, optimize the preoperative examination process, reduce intraoperative risks, and improve postoperative quality of life.

Lymph node metastasis and its risk factors in T1 lung adenocarcinoma

BACKGROUND

In this study, the focus was primarily on examining the occurrence of lymph node metastasis in T1 lung adenocarcinoma, while also analyzing the relationship between clinical variables such as imaging characteristics, pathological classifications, and lymph node metastasis.

METHODS

We retrospectively analyzed data from patients with T1 lung adenocarcinoma who underwent lobectomy and lymph node dissection between January 2016 and December 2019. Utilizing univariate and multivariate analyses, we assessed the associations between lymph node metastasis and various clinical factors, including imaging characteristics, lesion location and depth, and pathological subtypes.

RESULTS

Of the 433 patients with T1 lung adenocarcinoma, 139 had lymph node metastasis. Moreover, the incidence of node 1 (N1) lymph node, sequential, and node 2 (N2) skip metastases were 12.2%, 12.7%, and 7.2%, respectively. Univariate analysis revealed that tumor diameter, depth ratio, sex, invasive imaging features, and pathological subtype were significantly associated with lymph node metastasis. Multivariate analysis revealed that the tumor depth ratio, tumor diameter, pleural indentation or traction sign, nonvascular penetration sign, solid component, nonadherence, and micropapillary pathological subtype were risk factors for lymph node metastasis. In the multivariate analysis, the micropapillary pathological subtype was an independent risk factor for N2 skip metastasis.

CONCLUSIONS

In patients with clinical stage T1 lung adenocarcinoma, the risk of lymph node metastasis is higher for tumors located deep within the lung tissue with solid components, invasive preoperative imaging features, and larger diameters. For N2 skip lymph node metastasis, the micropapillary pathological subtype represents a significant high-risk factor.

Single-cell transcriptomics reveals the drivers and therapeutic targets of lymph node metastasis in lung adenocarcinoma

Lymph node metastasis (LNM) is usually the most common metastatic pathway in lung adenocarcinoma (LUAD) and is associated with a poorer prognosis and higher possibility of recurrence. Therefore, discovering the drivers and therapeutic targets of LNM is important for early and non-invasive detection of patients with a high risk of LNM and guiding individualized therapy. Various cell constitutions of the primary tumor and lymph node microenvironment was characterized based on scRNA-seq data. The copy number variation (CNV) analysis was performed to probe clonal structures and origins of metastatic lymph nodes, and found 6q loss and 20q gain may drive LNM in LUAD. Then a LNM-related cell subset, named Scissor+ cells, was identified using the Scissor algorithm. And cell-cell communication network among Scissor+ cells and microenvironment was further analyzed. Besides, a pro-LNM signature was subsequently constructed based on 27 genes using pseudotime trajectory analysis and gene set variation analysis. The pro-LNM signature showed a significant correlation with N stage and a good predictive ability of LUAD survival. At last, we identified that erastin and gefitinib could potentially inhibit LNM by targeting Scissor+ cells based on the drug sensitivity data of the cancer cell lines, which provided new insights for LUAD therapy.

Lymph Node Metastases in Surgically Resected Solitary Ground-Glass Opacities: A Two-Center Retrospective Cohort Study and Pooled Literature Analysis

BACKGROUND

An increasing body of evidence supports the noninferiority of sublobar resection compared with lobectomy in terms of survival for patients with early-stage lung cancer with ground-glass opacities (GGOs). However, few studies have focused on the incidence of lymph node (LN) metastases in these patients. We aimed to analyze N1 and N2 lymph node involvement in patients with non-small cell lung cancer (NSCLC) with GGO components stratified with different consolidation tumor ratio (CTR).

PATIENTS AND METHODS

We performed two-center studies by retrospectively reviewing a total of 864 patients with NSCLC with semisolid or pure GGO manifestation (diameter ≤ 3 cm). Clinicopathologic features and outcomes were analyzed. We also reviewed 35 studies to characterize the patient with NSCLC population with the GGO manifestation.

RESULTS

In both cohorts, there was no LN involvement for pure GGO NSCLC, while solid predominant GGO exhibited a relatively high LN involvement rate. On the basis of a pooled literature analysis, the incidence of pathologic mediastinal LN was 0% and 3.8% for pure and semisolid GGOs, respectively. GGO NSCLCs with CTR ≤ 0.5 also had rare LN involvement (0.1%).

CONCLUSIONS

From two cohorts and pooled literature analysis, LN involvement was not observed in patients with pure GGO, and very few patients with semisolid GGO NSCLC with CTR ≤ 0.5 had LN involvement, revealing that it may be unnecessary to perform lymphadenectomy for pure GGOs, while mediastinal lymph node sampling (MLNS) is enough for semisolid GGOs with CTR ≤ 0.5. For the patients with GGO CTR > 0.5, mediastinal lymphadenectomy (MLD) or MLNS should be considered.

PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer

A portion of gastric cancer patients with negative lymph node metastasis at an early stage eventually die from tumor recurrence or advanced metastasis. Occult lymph node metastasis (OLNM] is a potential risk factor for the recurrence and metastasis in these patients, and it is highly important for clinical prognosis. Positron emission tomography (PET)/computed tomography (CT) is used to assess lymph node metastasis in gastric cancer due to its advantages in anatomical and functional imaging and non-invasive nature. Among the major metabolic parameters of PET, the maximum standardized uptake value (SUVmax) is commonly used for examining lymph node status. However, SUVmax is susceptible to interference by a variety of factors. In recent years, the exploration of new PET metabolic parameters, new PET imaging agents and radiomics, has become an active research topic. This paper aims to explore the feasibility and predict the effectiveness of using PET/CT to detect OLNM. The current landscape and future trends of primary metabolic parameters and new imaging agents of PET are reviewed. For gastric cancer patients, the possibility to detect OLNM non-invasively will help guide surgeons to choose the appropriate lymph node dissection area, thereby reducing unnecessary dissections and providing more reasonable, personalized and comprehensive treatments.

Development and validation of nomograms based on clinical characteristics and CT reports for the preoperative prediction of precise lymph node dissection in lung cancer

OBJECTIVE

To develop and validate nomograms for preoperative prediction of precision lymph node (LN) dissection in lung cancer.

PATIENTS AND METHODS

The prediction models of each group LNs (LNx) were developed in a primary cohort that consisted of 1380 patients with clinicopathologically confirmed lung cancer. Clinical characteristics and CT reports were extracted. Patients with LNx dissection were divided into training cohort and testing cohort. Nomograms were built through univariate and multivariate regression analysis in the training cohort and internally verified in the testing cohort. The accuracy of the models was verified by constructing survival analysis in patients without LNx dissection.

RESULTS

Due to the lack of sufficient patients for LN1, 8, 13, a total of 10 nomograms were constructed in this study, including LN-2 ∼ 7, 9 ∼ 12. According to the nomogram of each group LN, the most common independent risk factors predicting LN status were CT-reported lymphadenectasis, tumor diameter and location, and the others include age, gender, and whether there were multiple nodules, etc. All models showed good discrimination, with the average C-index of 0.738 in the training cohort and 0.707 in the testing cohort. Survival analysis in patients without LNx dissection all showed the high accuracy of each nomogram to predict LN metastasis status and TNM staging.

CONCLUSION

We constructed nomograms to predict the metastasis status of each group of lymph nodes based on clinical characteristics and CT reports. Surgeons can accurately determine the extent of lymph node dissection in patients with lung cancer based on our nomogram models before surgery.

Correlation between the Expression of VEGF and Ki67 and Lymph Node Metastasis in Non-small-Cell Lung Cancer: A Systematic Review and Meta-Analysis

Background

Lymph node metastasis is the most common and important way of metastasis in NSCLC and is also the most important factor affecting lung cancer stage and prognosis. It is very important to analyze the relationship between the expression of vascular endothelial growth factor (VEGF) and Ki67 and lymph node metastasis (LNM) in non-small-cell lung cancer (NSCLC).

Methods

We searched the PubMed, EMBASE, and Cochrane Library and conducted meta-analyses using the R meta-package. Relative risk (RR) with a 95% confidence interval (95% CI) was the main indicator.

Results

Totally, 18 studies were considered eligible, with 4521 patients, including 1518 LNM-positive patients and 3033 LNM-negative patients. The incidence of LNM in Ki67-negative patients was lower than that in Ki67-positive patients (RR = 0.66, 95% CI: 0.44, 0.98). The incidence of LNM in VEGF-A-negative patients was lower than that in VEGF-A-positive patients (RR = 0.64, 95% CI: 0.49, 0.83). The incidence of LNM in VEGF-C negative patients was lower than that in VEGF-C positive patients (RR = 0.68, 95% CI: 0.53, 0.88). The incidence of LNM in VEGF-D negative and positive patients were of no significant differences (RR = 0.84, 95% CI: 0.61, 1.14).

Conclusion

The high expression of Ki67, VEGF-A, and VEGF-C significantly increases the risk of lymph node metastasis in NSCLC, while the VEGF-D expression has no correlation with lymph node metastasis. The expression levels of Ki67, VEGF-A, and VEGF-C show a good potential for lymph node metastasis prediction.