Risk factors of lymph node metastasis in patients with non‐small cell lung cancer ≤ 2 cm in size: <scp>A</scp> monocentric population‐based analysis

Yu, Xiyan; Li, Yanwen; Shi, Chunlei; Han, Baohui

doi:10.1111/1759-7714.12490

Cited by 32 publications

(35 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…17,18 In our study, it was not found to be a relevant factor for nodal involvement in univariate analysis (P = 0.56), which was also observed in the study by Fuwa et al 27 According to multivariate analysis, however, age seemed to be an independent risk factor (OR 0.65, P < 0.0001), which means younger patients were more prone to having lymph node metastases. There was no statistical difference between AD and SCC in the light of the study by Yu et al 28 However, in our study, we found that patients with tumor histology of SCC were much more likely to have lymph node metastases (30.4%) compared with AD patients.…”

Section: Discussioncontrasting

confidence: 76%

Lymph node metastasis in Chinese patients with clinical T1 non‐small cell lung cancer: A multicenter real‐world observational study

Chen

Xiao-jun

et al. 2019

Thoracic Cancer

View full text Add to dashboard Cite

Background Approximately 8.3–15.9% of patients with clinical stage I non‐small cell lung cancer are subsequently shown to have lymph node metastasis. However, the clinical characteristics of patients with lymph node metastasis in China are not fully understood. Methods This is a multicenter retrospective analysis of pathological T1 non‐small cell lung cancer patients who underwent surgical resection from 2 January 2014 to 27 December 2017. Clinical and pathological information was collected with the assistance of the Large‐scale Data Analysis Center of Cancer Precision Medicine‐LinkDoc database. The clinical and pathological factors associated with lymph node metastasis were analyzed by univariate and multivariate logistic regression. Results A total of 10 885 participants (51.6% women; 15.3% squamous cell carcinoma) were included in the analysis. The median age was 60.0 years (range 12.9–86.6 years). A total of 1159 patients (10.6%) had metastases in mediastinal nodes (N2), and 640 patients (5.9%) had metastasis in pulmonary lymph nodes (N1). Most patients had T1b lung cancer (4766, 43.8%). Of the patients, 3260 (29.9%) were current or former smokers. The univariate and multivariate analyses showed that younger age, squamous cell carcinoma, poor differentiation, larger tumor size, carcinoembryonic antigen level ≥5 ng/mL, and vascular invasion (+) were significantly associated with higher percentages of lymph node metastases ( P < 0.001 for all). Conclusion This real‐world study showed the significant association of lymph node metastasis with age, tumor size, histology and differentiation, carcinoembryonic antigen levels, and status of vascular invasion. Female patients with T1a adenocarcinoma in the right upper lobe barely had lymph node metastasis.

show abstract

Section: Discussioncontrasting

confidence: 76%

Lymph node metastasis in Chinese patients with clinical T1 non‐small cell lung cancer: A multicenter real‐world observational study

Chen

Xiao-jun

et al. 2019

Thoracic Cancer

View full text Add to dashboard Cite

show abstract

“…Previous studies have revealed some possible predictive factors for LNM in NSCLC. Yu et al reported several independent risk factors including tumor size, pleural invasion, and carcinoembryonic antigen [33]. Pani et al found that histologic subtypes could be related to lymph node status [34].…”

Section: Discussionmentioning

confidence: 99%

Is it feasible to predict lymph node metastasis intraoperatively or postoperatively in early-stage lung adenocarcinoma: the application of machine learning algorithms?

Chong

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundLymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma.MethodsA total of 709 lung adenocarcinoma patients with tumor size ≤ 2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model’s predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors.ResultsLNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) > 0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of 5 variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves. The cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%).ConclusionsIncorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms.Trial registration: NA

show abstract

“…Lung cancer is the leading cause of malignancy‐related death worldwide . Nowadays, radical surgery is regarded not only as the optimal therapeutic option for early‐stage non‐small‐cell lung cancer (NSCLC), but also as the cornerstone of multidisciplinary treatments for more advanced NSCLC .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4] Nowadays, radical surgery is regarded not only as the optimal therapeutic option for early-stage non-small-cell lung cancer (NSCLC), but also as the cornerstone of multidisciplinary treatments for more advanced NSCLC. [4][5][6][7] Since the 1990s, single lobectomy via video-assisted thoracoscopic surgery (VATS), a minimally invasive procedure providing access to the chest cavity, has been developed as a modern surgical modality for operable NSCLC, offering more advantages than conventional thoracotomy in terms of the cosmetic wounds, pain and stress control, preservation of pulmonary function, and enhanced recovery. 6,[8][9][10][11][12] Despite advances in surgical techniques and perioperative care, however, the morbidity rate after VATS lobectomy remains 26.2-36.3%.…”

Section: Introduction Rationalementioning

confidence: 99%

Body surface area as a novel risk factor for chylothorax complicating video‐assisted thoracoscopic surgery lobectomy for non‐small cell lung cancer

Wang

Zhou

et al. 2018

Thoracic Cancer

View full text Add to dashboard Cite

BackgroundThe study was conducted to demonstrate the predictive value of body surface area (BSA) for chylothorax complicating video‐assisted thoracoscopic surgery (VATS) lobectomy for non‐small cell lung cancer (NSCLC).MethodsLarge‐scale retrospective analysis was conducted on the data of 1379 patients who underwent VATS lobectomy between January 2014 and October 2017 at our institution. Receiver operating characteristic analysis was conducted to determine a threshold BSA value for the prediction of chylothorax. This optimal BSA cutoff, other clinicopathological variables, and P < 0.15 were included into a multivariable logistic regression model to determine the risk factors for chylothorax.ResultsTwenty‐six patients (1.9%) developed postoperative chylothorax. The mean BSA in patients with chylothorax was significantly higher than in patients without (1.84 ± 0.14 vs. 1.73 ± 0.16 m2; P = 0.001). A BSA of 1.69 m2 was identified as the threshold value with maximum joint sensitivity (96.2%) and specificity (43.8%). Patients with BSA > 1.69 m2 had a significantly higher incidence of chylothorax (3.0% vs. 0.3%; P < 0.001) and a longer hospital stay (log rank P < 0.001) than patients with BSA ≤ 1.69 m2. Multivariable logistic regression analysis suggested that BSA > 1.69 m2 (odds ratio 7.35, 95% confidence interval 1.54–35.71; P = 0.013) was predictive of postoperative chylothorax.ConclusionsBSA can serve as a novel categorical predictor for chylothorax complicating VATS lobectomy for NSCLC. It may be more helpful to incorporate a BSA cutoff into routine risk stratification tools for lung cancer surgery.

show abstract

Risk factors of lymph node metastasis in patients with non‐small cell lung cancer ≤ 2 cm in size: A monocentric population‐based analysis

Cited by 32 publications

References 21 publications

Lymph node metastasis in Chinese patients with clinical T1 non‐small cell lung cancer: A multicenter real‐world observational study

Lymph node metastasis in Chinese patients with clinical T1 non‐small cell lung cancer: A multicenter real‐world observational study

Is it feasible to predict lymph node metastasis intraoperatively or postoperatively in early-stage lung adenocarcinoma: the application of machine learning algorithms?

Body surface area as a novel risk factor for chylothorax complicating video‐assisted thoracoscopic surgery lobectomy for non‐small cell lung cancer

Contact Info

Product

Resources

About