Lymph node metastasis outside of a tumor-bearing lobe in primary lung cancer and the status of interlobar fissures: The necessity for removing lymph nodes from an adjacent lobe

Impact of socioeconomic status on cancer staging, survival in non-small cell lung cancer

Purpose

We performed this study to evaluate the association of socioeconomic status (SES) factors with cancer-specific survival (CSS) of patients with non-small cell lung cancer (NSCLC). We further assessed the predictive value of a novel Tumor Node Metastasis (TNM)-SES staging system, combining the TNM stage with the SES stage.

Methods

Using the Surveillance, Epidemiology, and End Results (SEER) database, we selected 40,378 patients diagnosed with NSCLC from 2012 to 2016. Cox regression method and Harrell's concordance index (C-index) were performed to select the SES factors related to CSS and evaluate the predictive ability of the novel TNM-SES stage. We used Kaplan-Meier curves and a log-rank test to conduct a survival analysis.

Results

We identified four SES factors (marriage, insurance, education, and household income) associated with CSS and constructed the SES stage (SES-1 and SES-2). NSCLC patients with SES-2 stage (low SES) was associated with young adult, black race, male, squamous carcinoma, upper lobe site, and advanced stage. SES-2 stage patients were significantly associated with a dismal prognosis of patients with NSCLC, with a 21.0% increased risk (HR = 1.21, 95%CI (1.18-1.24), p < 0.001). The C-index of our novel TNM-SES stage was 0.732 [95% CI (0.728-0.736)], higher than the traditional TNM stage [0.717, 95% CI (0.715-0.719)], indicating superior predictive value.

Conclusion

Our population-based study indicated that SES was significantly associated with cancer staging and SCC in patients with NSCLC. Our novel TNM-SES staging system showed a superior predictive value to the traditional TNM stage. The impact of SES on patients with NSCLC should receive more concern in clinical management.

Risk factors of lobar lymph node metastases in non-primary tumor-bearing lobes among the patients of non-small-cell lung cancer

Purpose

Lobar lymph node metastases in non-primary tumor-bearing lobes (NTBL) are rarely reported. This study examined the risk factors of lobar lymph node metastasis in NTBL.

Methods

We retrospectively studied 301 patients with non-small-cell lung cancer (NSCLC) who underwent surgical pulmonary resection with systematic lymph node dissection plus extended lobar lymph node dissection of NTBL. Patients were classified into positive and negative NTBL groups. Unconditional logistic regression was used to identify the risk factors for lobar lymph node metastasis in NTBL.

Results

NTBL lobar lymph nodes were identified in 38 patients (12.6%). A higher proportion of adenocarcinomas occurred in the positive NTBL group compared to the negative NTBL group (73.7% vs. 46.4%, P = 0.01). Risk of NTBL lobar lymph node metastases was significantly elevated in the lower lobe of primary site compared to the upper lobe (OR = 2.61, 95% CI = 1.26–5.75, P = 0.01), and with adenocarcinomas compared to squamous cell carcinomas (OR = 2.75, 95% CI = 1.09–7.65, P = 0.04). No differences were observed when comparing left and right lobes. NTBL lobar lymph node metastasis was most often observed among patients with larger tumor size, N1/N2 nodal involvement, with lymph vascular invasion (LVI), and visceral pleural invasion (VPI).

Conclusion

NTBL lobar lymph node metastases occurred more often in patients with a primary NSCLC tumor in the lower lobe, with adenocarcinomas, larger tumor size, N1/N2 nodal involvement, LVI or VPI. Extended lymphadenectomy including NTBL nodes may be clinically advantageous when these risk factors are present.

Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. Results: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. Conclusions: a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.