The Prognostic Value Of Lymph Node Ratio In Patients With N2 Stage Lung Squamous Cell Carcinoma: A Nomogram And Heat Map Approach

The effect of lymph node ratio on survival in non-small-cell lung cancer

BACKGROUND

The aim of this study was to evaluate the effect of prognostic factors and lymph node ratio (LNR) on survival in patients with resected non-small-cell lung cancer (NSCLC).

METHODS

Data from 421 patients with NSCLC who underwent complete resection between 2009 and 2015 were evaluated retrospectively. LNR was defined as the ratio of positive lymph nodes to the total number of lymph nodes removed. Associations between overall survival (OS) and LNR, node (N) status, and histopathologic status were evaluated.

RESULTS

The 5-year survival rate was 42.5% among all patients and 26.6% for patients aged 65 years or older. In the multivariate analysis, age ≥65 years, advanced-stage disease, non-squamous cell carcinomas, pN status, and having multiple-station pN2 and multiple-station pN1 disease were found to be poor prognostic factors (p < 0.05). There was no statistical difference in survival between patients with LNR (hazard ratio: 1.04, p = 0.45).

CONCLUSION

The results of our study indicate that pN stage, histopathologic type, pT stage, and geriatric age were the most important poor prognostic factors associated with survival after NSCLC resection. Although LNR is a factor associated with survival in gastrointestinal cancers, it did not impact survival in our study.

A novel prognostic model based on log odds of positive lymph nodes to predict outcomes of patients with anaplastic thyroid carcinoma after surgery

OBJECTIVE

The eighth version of the American Joint Committee on Cancer (8th AJCC) system for anaplastic thyroid carcinoma (ATC) added lymph node (LN) metastasis as the staging element. This study aimed to explore the association between LN status and ATC's prognosis, identify the optimal LN index and establish a novel prognostic model.

DESIGN AND PATIENTS

Data of 199 ATC patients after surgery were collected from the Surveillance, Epidemiology and End Results (SEER) database, then randomly divided into training and validation cohorts.

MEASUREMENTS

We compared the prognostic value of AJCC N status, number of positive LN (PLNN), ratio of LN (LNR) and log odds of positive LN (LODDS). We conducted univariate and multivariate Cox analyses to determine the independent prognostic factors for ATC, and constructed a novel prognostic model. The concordance index (C-index), area under the receiver-operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA) were used to assess the nomogram's predictive performance.

RESULTS

LODDS showed the highest accuracy among four LN systems to predict overall survival (OS) for ATC. In the training cohort, the C-index of the LODDS-based nomogram was 0.738. The AUCs were 0.813, 0.850 and 0.869 for predicting 1-, 2- and 3-year OS, respectively. The calibration plots and DCA indicated the great clinical applicability of the model. The above results were verified in the validation cohort.

CONCLUSIONS

LODDS showed better predictive performance than other LN schemes in ATC. The LODDS-incorporated nomogram has the potential to more precisely predict the prognosis for ATC patients than the AJCC system.

Association of metastatic nodal size with survival in non-surgical non-small cell lung cancer patients: Recommendations for clinical N staging

Background

This study aims to analyze the prognostic significance of the metastatic lymph node (mLN) size in non-small cell lung cancer (NSCLC) patients receiving chemoradiotherapy (CRT) to provide some information for the optimization of clinical nodal (cN) staging.

Methods

A retrospective study with 325 NSCLC patients was conducted between January 2011 and December 2018 at two participating institutes. We evaluated the potential relationship between the mLN size and the survival to propose a potential revised nodal (rN) staging.

Results

Kaplan–Meier analyses showed significant differences in the overall survival (OS) based on the cN staging and the size of mLNs (N0, ≤2 cm, and >2 cm). We found that the nodal size correlated statistically with the response to CRT. The HRs of OS for patients with bulky mLNs increase significantly compared with patients in the non-bulky mLNs group in the cN2-3 group. Interestingly, the HRs of patients with bulky cN2 disease and non-bulky cN3 disease were similar to each other. We classified the patients into five subsets: N0, rN1(cN1), rN2(non-bulky cN2), rN3a(bulky cN2, and non-bulky cN3), and rN3b(bulky cN3). In our study, the rN stage showed better prognostic discrimination than the 8th IASLC cN staging and was an independent prognostic factor for survival.

Conclusions

In addition to the anatomic location, the size of mLNs correlated statistically with the response to CRT and should be incorporated into the cN staging system to predict survival more accurately.

Prognostic nomograms for lung neuroendocrine carcinomas based on lymph node ratio: a SEER database analysis

Objective

The current study aimed to explore the prognostic value of the lymph node ratio (LNR) in patients with lung neuroendocrine carcinomas (LNECs).

Methods

Data for 1564 elderly patients with LNECs between 1998 and 2016 were obtained from the Surveillance, Epidemiology, and End Results database. The cases were assigned randomly to training (n = 1086) and internal validation (n = 478) sets. The association between LNR and survival was investigated by Cox regression.

Results

Multivariate analyses identified age, tumor grade, summary stage, M stage, surgery, and LNR as independent prognostic factors for both overall survival (OS) and lung cancer-specific survival (LCSS). Tumor size was also a prognostic determinant for LCSS. Prognostic nomograms combining LNR with other informative variables showed good discrimination and calibration abilities in both the training and validation sets. In addition, the C-index of the nomograms was statistically superior to the American Joint Committee on Cancer (AJCC) staging system in both the training and validation cohorts.

Conclusions

These nomograms, based on LNR, showed superior prognostic predictive accuracy compared with the AJCC staging system for predicting OS and LCSS in patients with LNECs.

Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.

Construction and validation of a prognostic model for stage IIIC endometrial cancer patients after surgery

BACKGROUND

To explore the most predictive lymph node (LN) scheme for stage IIIC endometrial cancer (EC) patients after hysterectomy and develop a scheme-based nomogram.

METHODS

Data from 2626 stage IIIC EC patients, diagnosed between 2010 and 2014, were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. The predictive ability of four LN schemes was assessed using C-index and Akaike information criterion (AIC). A nomogram based on the most predictive LN scheme was constructed and validated. The comparison of the predictive ability between nomogram and FIGO stage was conducted using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

FIGO stage (stage IIIC1/stage IIIC2) was not an independent risk factor for OS in stage IIIC EC patients (P = 0.672) and log odds of positive lymph nodes (LODDS) had the best predictive ability (C-index: 0.742; AIC: 8228.95). A nomogram based on LODDS was constructed and validated, which had a decent C-index of 0.742 (0.723-0.762). The nomogram showed a better predictive ability than that of the FIGO staging system.

CONCLUSION

FIGO IIIC1/FIGO IIIC2 could not differentiate the prognosis for stage IIIC EC patients. We developed and validated a nomogram based on LODDS to predict OS for post-operative patients with stage IIIC EC.

Comparison of Different Lymph Node Staging Schemes for Predicting Survival Outcomes in Node-Positive Endometrioid Endometrial Cancer Patients

Objective: To compare the prognostic predictive performance of six lymph node (LN) staging schemes: American Joint Committee on Cancer (AJCC) N stage, number of retrieved lymph nodes (NRLN), number of positive lymph nodes (NPLN), number of negative lymph nodes (NNLN), lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) among node-positive endometrioid endometrial cancer (EEC) patients. Methods: A total of 3,533 patients diagnosed with node-positive EEC between 2010 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. We applied X-tile software to identify the optimal cutoff value for different staging schemes. Univariate and multivariate Cox regression models were used to assess the relationships between different LN schemes and survival outcomes [disease-specific survival (DSS) and overall survival (OS)]. Moreover, Akaike information criterion (AIC) and Harrell concordance index (C-index) were used to evaluate the predictive performance of each scheme in both continuous and categorical patterns. Results: N stage (N1/N2) was not an independent prognostic factor for node-positive EEC patients based on multivariate analysis (DSS: p = 0.235; OS: p = 0.145). Multivariate model incorporating LNR demonstrated the most superior goodness of fit regardless of continuous or categorical pattern. Regarding discrimination power of the models, LNR outperformed other models in categorical pattern (OS: C-index = 0.735; DSS: C-index = 0.737); however, LODDS obtained the highest C-index in continuous pattern (OS: 0.736; DSS: 0.739). Conclusions: N stage (N1/N2) was unable to differentiate the prognosis for node-positive EEC patients in our study. However, LNR and LODDS schemes seemed to have a better predictive performance for these patients than other number-based LN schemes whether in DSS or OS, which revealed that LNR and LODDS should be more helpful in prognosis assessment for node-positive EEC patients than AJCC N stage.

Identification of a glycolysis-related gene signature associated with clinical outcome for patients with lung squamous cell carcinoma

Background

Lung squamous cell carcinoma (LUSC), one of the main types of lung cancer, has caused a huge social burden. There has been no significant progress in its therapy in recent years, Resulting in a poor prognosis. This study aims to develop a glycolysis‐related gene signature to predict patients’ survival with LUSC and explore new therapeutic targets.

Methods

We obtained the mRNA expression and clinical information of 550 patients with LUSC from the Cancer Genome Atlas (TCGA) database. Glycolysis genes were identified by Gene Set Enrichment Analysis (GSEA). The glycolysis‐related gene signature was established using the Cox regression analysis.

Results

We developed five glycolysis‐related genes signature (HKDC1, AGL, ALDH7A1, SLC16A3, and MIOX) to calculate each patient's risk score. According to the risk score, patients were divided into high‐ and low‐risk groups and exhibited significant differences in overall survival (OS) between the two groups. The ROC curves showed that the AUC was 0.707 for the training cohort and 0.651 for the validation cohort. Additionally, the risk score was confirmed as an independent risk factor for LUSC patients by Cox regression analysis.

Conclusion

We built a gene signature to clarify the connection between glycolysis and LUSC. This model performs well in evaluating patients’ survival with LUSC and provides new biomarkers for targeted therapy.

The Neutrophil Percentage-to-Albumin Ratio as a New Predictor of All-Cause Mortality in Patients with Cardiogenic Shock

Background

Although the neutrophil percentage-to-albumin ratio (NPAR) has proven to be a robust systemic inflammation-based predictor of mortality in a wide range of diseases, the prognostic value of the NPAR in critically ill patients with cardiogenic shock (CS) remains unknown. This study aimed at investigating the association between the admission NPAR and clinical outcomes in CS patients using real-world data.

Methods

Critically ill patients diagnosed with CS in the Medical Information Mart for Intensive Care-III (MIMIC-III) database were included in our study. The study endpoints included all-cause in-hospital, 30-day, and 365-day mortality in CS patients. First, the NPAR was analyzed as a continuous variable using restricted cubic spline Cox regression models. Second, X-tile analysis was used to calculate the optimal cut-off values for the NPAR and divide the cohort into three NPAR groups. Moreover, multivariable Cox regression analyses were used to assess the association of the NPAR groups with mortality.

Results

A total of 891 patients hospitalized with CS were enrolled in this study. A nonlinear relationship between the NPAR and in-hospital and 30-day mortality was observed (all P values for nonlinear trend<0.001). According to the optimal cut-off values by X-tile, NPARs were divided into three groups: group I (NPAR < 25.3), group II (25.3 ≤ NPAR < 34.8), and group III (34.8 ≤ NPAR). Multivariable Cox analysis showed that higher NPAR was independently associated with increased risk of in-hospital mortality (group III vs. group I: hazard ratio [HR] 2.60, 95% confidence interval [CI] 1.72-3.92, P < 0.001), 30-day mortality (group III vs. group I: HR 2.42, 95% CI 1.65-3.54, P < 0.001), and 365-day mortality (group III vs. group I: HR 6.80, 95% CI 4.10-11.26, P < 0.001) in patients with CS.

Conclusions

Admission NPAR was independently associated with in-hospital, 30-day, and 365-day mortality in critically ill patients with CS.

Prognostic value of log odds of positive lymph nodes in node-positive lung squamous cell carcinoma patients after surgery: a SEER population-based study

Background

Log odds of positive lymph nodes (LODDS) is a novel and promising ratio-based lymph node (LN) staging system in many malignancies. This study aimed to evaluate the prognostic value of LODDS, and comprehensively compare the prognostic predictive performance of LODDS with the American Joint Committee on Cancer (AJCC) N classification, number of positive lymph node (NPLN), and lymph node ratio (LNR) among node-positive lung squamous cell carcinoma (SCC) patients after surgery.

Methods

We identified 2,561 patients with N1/N2 stage SCC diagnosed between 2004 and 2014 from the Surveillance, Epidemiology, and End Results (SEER) database. X-tile analysis was used to calculate the optimal cut-off value for each staging system. Univariable and Multivariable Cox regression analyses were used to assess the association of cancer-specific survival (CSS), and overall survival (OS) with N, NPLN, LNR, and LODDS, separately, and integrally. Moreover, linear trend χ² score, likelihood ratio (LR) test, Akaike information criterion (AIC), and Harrell concordance index (C-index) were adopted as criteria for assessing the predictive ability of each model.

Results

The optimal cut-off values for NPLN, LNR, and LODDS were 3, 0.28, and −0.37, respectively. N, NPLN, LNR, and LODDS were identified as independent prognostic predictors for CSS and OS in patients with SCC when each of them was incorporated into multivariable Cox model separately. Additionally, LODDS had the higher linear trend χ² score, higher LR χ² test score, lower AIC, and higher C-index compared to the other three systems. Moreover, a combination of N, NPLN, and LODDS was superior to any staging system alone for predicting prognosis.

Conclusions

LODDS showed better predictive performance than N, NPLN, and LNR among patients with node-positive SCC after surgery. A combination of LODDS and the current AJCC TNM classification has the potential for becoming a better staging method to more precisely predicting prognosis.

Prognostic value of baseline hemoglobin-to-red blood cell distribution width ratio in small cell lung cancer: A retrospective analysis

Background

This study aimed to investigate the prognostic value of baseline hemoglobin‐to‐red blood cell distribution width ratio (HRR) in patients with small cell lung cancer (SCLC).

Methods

We retrospectively analyzed the medical records of patients with newly diagnosed SCLC who had received first‐line chemotherapy at the Department of Pulmonary Oncology of the PLA 307 Hospital between January 2008 and October 2018. The optimal cutoff value of the continuous variables was determined using the X‐tile software. Univariate and multivariate analyses were conducted using Cox proportional hazard models. The Kaplan‐Meier method was used for survival analysis, with differences tested using the log‐rank test.

Results

A total of 146 patients were included. The cutoff value for HRR was determined as 0.985. Statistically significant differences were observed in sex, smoking history, stage, radiotherapy combination, neutrophil‐to‐lymphocyte ratio, platelet‐to‐lymphocyte ratio, hemoglobin, and red blood cell distribution width between the high and low HRR groups. The median overall survival (OS) was nine and 17.5 months in the low and high HRR groups, respectively (P < 0.001). The median progression‐free survival (PFS) was five and 8.5 months, respectively (P < 0.001). Univariate and multivariate analyses showed low HRR to be an independent predictor of a poor prognosis for OS (hazard ratio = 3.782; 95% confidence interval, 2.151–6.652; P < 0.001) and PFS (hazard ratio = 2.112; 95% confidence interval, 1.195–3.733; P = 0.01) in SCLC.

Conclusion

Low HRR was associated with poorer OS and PFS in patients with SCLC and can be a potentially valuable prognostic factor for these patients.

Key points

The prognostic value of the baseline hemoglobin‐to‐red blood cell distribution width ratio was evaluated in patients with small cell lung cancer.

In this population, this ratio was an independent predictor of overall survival and progression‐free survival. This ratio, an inexpensive and routine parameter, can be used as a prognostic factor in small cell lung cancer.