Based on SEER Database: Population Distribution, Survival Analysis, and Prognostic Factors of Organ Metastasis of Lung Large Cell Neuroendocrine Carcinoma

Early death incidence and prediction in stage IV large cell neuroendocrine carcinoma of the lung

Nearly half of lung large cell neuroendocrine carcinoma (LCNEC) patients are diagnosed at an advanced stage and face a high early death risk. Our objective was to develop models for assessing early death risk in stage IV LCNEC patients. We used surveillance, epidemiology, and end results (SEER) databases to gather data on patients with stage IV LCNEC to construct models and conduct internal validation. Additionally, we collected a dataset from the Second Affiliated Hospital of Nanchang University for external validation. We used the Pearson correlation coefficient and variance inflation factor to identify collinearity among variables. Logistic regression analysis and least absolute shrinkage and selection operator analysis were employed to identify important independent prognostic factors. Prediction nomograms and network-based probability calculators were developed. The accuracy of the nomograms was evaluated using receiver operating characteristic curves. The goodness of fit of the nomograms was evaluated using the Hosmer-Lemeshow test and calibration curves. The clinical value of the models was assessed through decision curve analysis. We enrolled 816 patients from the surveillance, epidemiology, and end results database and randomly assigned them to a training group and a validation group at a 7:3 ratio. In the training group, we identified 9 factors closely associated with early death and included them in the prediction nomograms. The overall early death model achieved an area under the curve of 0.850 for the training group and 0.780 for the validation group. Regarding the cancer-specific early death model, the area under the curve was 0.853 for the training group and 0.769 for the validation group. The calibration curve and Hosmer-Lemeshow test both demonstrated a high level of consistency for the constructed nomograms. Additionally, decision curve analysis further confirmed the substantial clinical utility of the nomograms. We developed a reliable nomogram to predict the early mortality risk in stage IV LCNEC patients that can be a helpful tool for health care professionals to identify high-risk patients and create personalized treatment plans.

Prognostic factors and predictive models for patients with lung large cell neuroendocrine carcinoma: Based on SEER database

BACKGROUND

Lung Large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive, high-grade neuroendocrine carcinoma with a poor prognosis, mainly seen in elderly men. To date, we have found no studies on predictive models for LCNEC.

METHODS

We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database of confirmed LCNEC from 2010 to 2018. Univariate and multivariate Cox proportional risk regression analyses were used to identify independent risk factors, and then we constructed a novel nomogram and assessed the predictive effectiveness by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 2546 patients with LCNEC were included, excluding those diagnosed with autopsy or death certificate, tumor, lymph node, metastasis (TNM) stage, tumor grade deficiency, etc., and finally, a total of 743 cases were included in the study. After univariate and multivariate analyses, we concluded that the independent risk factors were N stage, intrapulmonary metastasis, bone metastasis, brain metastasis, and surgical intervention. The results of ROC curves, calibration curves, and DCA in the training and validation groups confirmed that the nomogram could accurately predict the prognosis.

CONCLUSIONS

The nomogram obtained from our study is expected to be a useful tool for personalized prognostic prediction of LCNEC patients, which may help in clinical decision-making.

[Development and Validation of A Prognostic Nomogram to Guide Decision-making in Lung Large Cell Neuroendocrine Carcinoma]

BACKGROUND

Lung large cell neuroendocrine carcinoma (LCNEC) is a rare and highly malignant lung tumor with a poor prognosis. Currently, most research on LCNEC is based on retrospective studies and lacks validation in the real world. The study aims to identify independent risk factors and establish and validate a predictive model for the prognosis of LCNEC.

METHODS

Patient data were extracted from Surveillance, Epidemiology, and End Results (SEER) and our department's hospitalization records from 2010 to 2015 and 2016 to 2020, respectively. Kaplan-Meier analysis was used to evaluate overall survival (OS). OS is defined as the time from diagnosis to death or last follow-up for a patient. Univariate and multivariate Cox regression analyses were performed to identify significant prognostic factors and construct a Nomogram for predicting the prognosis of LCNEC.

RESULTS

In total, 1892 LCNEC patients were included and divided into a training cohort (n=1288) and two validation cohorts (n=552, n=52). Univariate Cox regression analysis showed that age, gender, primary tumor site, laterality, T stage, N stage, M stage, surgery, and radiotherapy were factors that could affect the prognosis of LCNEC patients (P<0.05). Multivariate Cox analysis indicated that age, gender, primary tumor site, T stage, N stage, M stage, surgery, and radiotherapy were independent risk factors for the prognosis of LCNEC patients (P<0.05). Calibration curves and the concordance index (internal: 0.744±0.015; external: 0.763±0.020, 0.832±0.055) demonstrated good predictive performance of the model.

CONCLUSIONS

Patients aged ≥65 years, male, with advanced tumor-node-metastasis (TNM) staging, and who have not undergone surgery or radiotherapy have a poor prognosis. Nomogram can provide a certain reference for personalized clinical decision-making for patients.

Risk factors and prognostic factors for pulmonary large cell neuroendocrine carcinoma with brain metastasis

BACKGROUND

As the studies regarding the brain metastasis (BM) of pulmonary large cell neuroendocrine carcinoma (LCNEC) are insufficient, the present research aims to describe the risk factors and prognostic factors that are related to cancer-specific survival (CSS) for LCNEC patients with BM.

METHODS

The data of LCNEC patients between January 2010 and October 2018 were obtained from the SEER database. Binary logistic regression analyses were utilized to screen the possible risk factors related to BM. Prognostic factors for LCNEC patients with BM were indentified by Cox regression analyses. Moreover, a nomogram was established to predict the 6-, 12-, and 18-month CSS rates. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were utilized to assess the discrimination and reliability of the model. Clinical decision curves (DCAs) were used to evaluate the clinical benefits and utility of our model.

RESULTS

Totally, 1875 patients were enrolled, with 294 (15.7%) of them having BM at diagnosis. Multivariate logistic regression analyses revealed that patients with age < 65 (odds ratio, OR = 1.564) and N2 staging (OR = 1.775) had a greater chance of developing BM. Age (≥ 65 vs. < 65: hazard ratio, HR = 1.409), T staging (T1 vs. T0: HR = 4.580; T2 vs. T0: HR = 6.008; T3 vs. T0: HR = 7.065; T4 vs. T0: HR = 6.821), N staging (N2 vs. N0: HR = 1.592; N3 vs. N0: HR = 1.654), liver metastasis (HR = 1.410), primary site surgery (HR = 0.581) and chemotherapy (HR = 0.452) were independent prognostic factors for LCNEC patients with BM. A nomogram prediction model was constructed by incorporating these factors. Using the C-index, calibration curves, ROC curves, and DCAs, we found that the clinical prediction model performed well.

CONCLUSION

We described the risk factors and prognostic factors that were associated with CSS for LCNEC patients with BM. The related nomogram was established and validated to help clinicians formulate more rational and effective treatment strategies.