Establishment and Validation of a Predictive Nomogram for Postoperative Survival of Stage I Non-Small Cell Lung Cancer

Establishing a prognostic scoring system and exploring prognostic value of examined lymph node numbers for stage I non-small cell lung cancer: a retrospective study of Surveillance, Epidemiology, and End Results (SEER) database and a Chinese cohort

Background

There is currently no recognized assessment system to predict disease outcomes for stage I non-small cell lung cancer (NSCLC). This research aimed to develop a prognostic scoring system for predicting 5-year overall survival (OS) of individuals with stage I NSCLC following definitive therapeutic intervention. Additionally, the optimal number of examined lymph nodes (ELNs) count for tumors no larger than 30 mm was determined.

Methods

Patients (n=22,617) diagnosed with stage I NSCLC from 2007 to 2015 who underwent definitive treatment (pulmonary lobectomy, pulmonary sublobectomy, or radiotherapy) were identified from the Surveillance, Epidemiology, and End Results (SEER) database. There were 400 Chinese patients with stage I NSCLC diagnosed in 2017 enrolled for external validation. The nomogram was constructed based on gradient boosting machine. The optimal ELNs in patients with tumors ≤30 mm and node-negative undergoing pulmonary lobectomy or pulmonary sublobectomy were determined using log-rank test and validated by multivariable analysis.

Results

Age at diagnosis, histology, differentiated grade, tumor staging, number of ELNs, and definitive treatment pattern were recognized as important factors for 5-year OS. The prognostic scoring system exhibited superior discrimination accuracy, calibration ability, and net clinical benefit compared to the tumor, node, metastasis (TNM) staging system. For patients with tumors ≤30 mm, more than 10 and 20 ELNs demonstrated the maximum OS difference during lobectomy and sublobectomy, respectively.

Conclusions

This prognostic scoring system will anticipate the prognosis of stage I NSCLC patients after radical treatment, thereby offering individualized treatment recommendations for both clinicians and patients. A minimum of 10 ELNs during lobectomy and 20 ELNs during sublobectomy are necessary for small-sized NSCLC.

Benefits of adjuvant chemotherapy in elderly patients with stage IB-IIIB non-small cell lung cancer: a propensity-matched analysis

Background

Adjuvant chemotherapy (ACT) is a well-recognized and well-established treatment for surgically resected non-small cell lung cancer (NSCLC), but its suitability for elderly patients remains controversial. Further investigation is warranted to guide ACT decisions in this demographic.

Methods

We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database, focusing on patients aged 70 years or older who underwent surgical resection for stage IB, II, or III NSCLC as per the 7th edition of the American Joint Committee on Cancer staging system (AJCC 7th edition). Propensity score matching (PSM), Kaplan-Meier analysis, and Cox regression were employed for statistical analyses.

Results

There were 503 participants received ACT in this study of 2,000 patients aged 70 or older with stage IB-IIIB NSCLC who underwent surgical resection without preoperative chemotherapy. Overall, ACT did not significantly correlate with extended overall survival (OS) (P=0.07) compared to non-ACT. After 2:1 PSM, the matched cohort comprised 317 non-ACT and 206 ACT recipients. Post-PSM, the ACT group exhibited improved OS (P=0.044) compared to the non-ACT group. Cox regression analysis identified gender, primary tumor site, histologic grade, N stage, and ACT as independent predictors of OS (P<0.05). Subgroup analysis indicated amplified ACT benefits in individuals aged 70-79 years, male, with N1 stage, or those without radiotherapy.

Conclusions

ACT may confer benefits to elderly stage IB-IIIB NSCLC patients, particularly those aged 70-79 years, male, and with N1 stage.

Pulmonary function test-related prognostic models in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy

Background

This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy.

Methods

Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value.

Results

Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination.

Conclusion

The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database

OBJECTIVE

We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma.

METHODS

Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms.

RESULTS

We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification.

CONCLUSION

The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

The nomogram for the prediction of overall survival after surgery in patients in early-stage NSCLC based on SEER database and external validation cohort

BACKGROUND & AIMS

Currently, there is a lack of effective tools for predicting the prognostic outcome of early-stage lung cancer after surgery. We aim to create a nomogram model to help clinicians assess the risk of postoperative recurrence or metastasis.

MATERIALS AND METHODS

This work obtained 16,459 NSCLC patients based on SEER database from 2010 to 2015. In addition, we also enrolled 385 NSCLC patients (2017/01-2019/06) into external validation cohort at Tianjin Medical University General Hospital. Univariable as well as multivariable Cox regression was carried out for identifying factors independently predicting OS. In addition, we built a nomogram by incorporating the above prognostic factors for the prediction of OS.

RESULTS

Tumor size was positively correlated with the risk of poor differentiation. Advanced age, male and adenocarcinoma patients were factors independently predicting poor prognosis. The risk of white race is higher, followed by Black race, Asians and Indians, which is consistent with previous study. Chemotherapy is negatively related to prognostic outcome in patients of Stage IA NSCLC and positively related to that in those of Stage IB NSCLC. Lymph node dissection can reduce the postoperative mortality of patients. AUCs of the nomograms for 1, 2, and 3-year OS was 0.705, 0.712, and 0.714 for training cohort, while those were 0.684, 0.688, and 0.688 for validation cohort.

CONCLUSIONS

The nomogram could be used as a tool to predict the postoperative prognosis of patients with Stage I non-small cell lung cancer.

Development and validation of a nomogram for predicting overall survival in patients with stage III-N2 lung adenocarcinoma based on the SEER database

Background

There is variability in the prognosis of stage III-N2 lung adenocarcinoma (LUAD) patients. The current tumor-node-metastasis (TNM) staging is not sufficient to precisely estimate the prognosis of stage III-N2 LUAD patients. The Surveillance, Epidemiology, and End Results (SEER) database collected first-hand information from a large number of LUAD patients. Based on the SEER database, this study aimed to determine the prognostic factors that affect overall survival (OS) in stage III-N2 LUAD patients and then establish a nomogram for predicting OS in this type of cancer to identify the high-risk population that may require more frequent surveillance or intensive care.

Methods

Data for 1,844 stage III-N2 primary LUAD patients who were registered between 2010 and 2015 were obtained from the SEER database. These patients were randomly assigned to either training (n=1,290) or validation (n=554) cohorts at a 7:3 ratio. The univariate and multivariate Cox regression (UCR and MCR) analyses were performed to find the relevant independent prognostic factors. To predict the OS based on these prognostic factors, a nomogram was then developed. The performance of the nomogram was examined based on the calibration curves, and receiver operating characteristic (ROC) curves. The ability of nomogram to stratify patient risk was validated by Kaplan-Meier survival analysis.

Results

Age, gender, tumor location, T-stage and treatment modality (chemotherapy, radiation therapy, surgery and scope of lymph node dissection) of stage III-N2 LUAD patients were significantly associated with prognosis. The area under the curve (AUC) values of OS predicted by the nomogram constructed with these factors at 12-, 36- and 60-month were 0.784, 0.762 and 0.763 in the training cohort, whereas 0.707, 0.685 and 0.705 in the validation cohort, respectively. Additionally, calibration curves demonstrated concordance between predicted and observed outcomes. Nomogram risk stratification provides a meaningful distinction between patients with various survival risks.

Conclusions

A survival prediction model that may be useful for risk stratification and decision-making is developed and validated for stage III-N2 LUAD patients. A high-risk patient predicted by the prediction model may require more frequent surveillance or intensive care.