A modified recursive partitioning analysis for predicting overall survival in patients with non-small cell lung cancer and central nervous system metastases

Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study

OBJECTIVE

A high number of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis who have not had surgery often have a negative outlook. Radiotherapy remains a most common and effective method. Nomograms were developed to forecast the cancer-specific survival (CSS) and overall survival (OS) in NSCLC individuals with nonoperative brain metastases who underwent radiotherapy.

METHODS

Information was gathered from the Surveillance, Epidemiology, and End Results (SEER) database about patients diagnosed with NSCLC who had brain metastases not suitable for surgery. Nomograms were created and tested using multivariate Cox regression models to forecast CSS and OS at intervals of 1, 2, and 3 years.

RESULTS

The research involved 3413 individuals diagnosed with NSCLC brain metastases who had undergone radiotherapy but had not experienced surgery. These participants were randomly divided into two categories. The analysis revealed that gender, age, ethnicity, marital status, tumor location, tumor laterality, tumor grade, histology, T stage, N stage, chemotherapy, tumor size, lung metastasis, bone metastasis, and liver metastasis were significant independent predictors for OS and CSS. The C-index for the training set for predicting OS was .709 (95% CI, .697-.721), and for the validation set, it was .705 (95% CI, .686-.723), respectively. The C-index for predicting CSS was .710 (95% CI, .697-.722) in the training set and .703 (95% CI, .684-.722) in the validation set, respectively. The nomograms model, as suggested by the impressive C-index, exhibits outstanding differentiation ability. Moreover, the ROC and calibration curves reveal its commendable precision and distinguishing potential.

CONCLUSIONS

For the first time, highly accurate and reliable nomograms were developed to predict OS and CSS in NSCLC patients with non-surgical brain metastases, who have undergone radiotherapy treatment. The nomograms may assist in tailoring counseling strategies and choosing the most effective treatment method.

Risk factor of brain metastases and its influence on patient prognosis after complete resection of non-small cell lung cancer

OBJECTIVE

To investigate the risk factors of brain metastases and its influence on the prognosis of patients with complete resected non-small cell lung cancer (NSCLC).

METHODS

The clinical data of 190 patients with completely resected NSCLC were retrospectively analyzed. The effect of brain metastasis on prognosis in patients with NSCLC after complete resection was investigated. The identification of risk factors for brain metastases was conducted by single-factor and Cox multivariate regression analysis.

RESULTS

Among 190 patients, 9 patients were lost to follow up. Finally, 181 patients were included in this study. The median survival time of brain metastases patients (64 cases) was 700 days. At 1 year, 3 years, and 5 years after surgery, the survival rates of patients with brain metastases were 62.5%, 28.13% and 7.81% respectively. Compared with those in patients without brain metastases, significant differences were found for median survival time and survival rates (P < 0.05). The Single-factor and multivariate Cox regression analysis indicated that the level of preoperative carcinoembryonic antigen (CEA), the lymph node ratio (≥ 30%) and non-squamous carcinoma type were risk factors for brain metastasis (P < 0.05).

CONCLUSIONS

Brain metastasis is a risk factor for mortality in NSCLC patients after complete resection. Preoperative CEA levels, lymph node ratio (≥ 30%), and type of non-squamous cell carcinoma were risk factors for brain metastases.

The development and validation of a nomogram for predicting brain metastases in lung squamous cell carcinoma patients: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database

Background

The incidence of brain metastasis (BM) in patients suffering from lung squamous cell carcinoma (LUSC) is lower than that in patients suffering from non-squamous cell carcinoma (NSCC) and there are few studies on BM of LUSC. The purpose of this investigation was to ascertain the risk factors of LUSC, as well as to establish a nomogram prognostic model to predict the incidence of BM in patients with LUSC.

Methods

Patients diagnosed with LUSC between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and the patient data were collated. All patients diagnosed from 2010–2012 were allocated into the training cohort, and the remaining patients diagnosed from 2013–2015 formed the test cohort. Using factors that were screened out through logistic regression analyses, the nomogram in the training cohort was established. It was then evaluated for discrimination and calibration using the test cohort. The performance of the nomogram was assessed by quantifying the area under the receiver operating characteristic (ROC) curve and evaluating the calibration curve.

Results

A total of 26,154 LUSC patients were included in the study. The training cohort consisted of 16,543 patients and there were 8611 patients in the test cohort. Age, marital status, insurance status, histological grade, tumor location, laterality, stage of the cancer, number of metastatic organs, chemotherapy, surgery, and radiotherapy were highly correlated with the incidence of BM. The area under the ROC curve (AUC) of the nomogram for the training cohort and the test cohort were 0.810 [95% confidence interval (CI): 0.796 to 0.823] and 0.805 (95% CI: 0.784 to 0.825), respectively. The slope of the calibration curve was close to 1.

Conclusions

The nomogram was able to accurately predict the incidence of BM. This may be beneficial for the early identification of high-risk LUSC patients and the establishment of individualized treatments.