Factors for incidence risk and prognosis in non-small-cell lung cancer patients with synchronous brain metastasis: a population-based study

Impact of lung adenocarcinoma subtypes on survival and timing of brain metastases

Purpose

Lung cancer is a devastating disease, with brain metastasis being one of the most common distant metastases of lung adenocarcinoma. This study aimed to investigate the prognostic characteristics of individuals with brain metastases originating from invasive lung adenocarcinoma of distinct pathological subtypes, providing a reference for the management of these patients.

Methods

Clinical data from 156 patients with lung adenocarcinoma-derived brain metastases were collected, including age, sex, smoking status, Karnofsky Performance Status scores, pathological subtype, lymph node metastasis, tumor site, treatment mode, T stage, and N stage. Patients were classified into two groups (highly differentiated and poorly differentiated) based on their pathological subtypes. Propensity score matching was used to control for confounding factors. The prognostic value of pathological subtypes was assessed using Kaplan-Meier analysis and Cox proportional hazards regression modeling.

Results

Kaplan-Meier analysis indicated that patients in the moderately to highly differentiated group had better prognoses. Multivariate analysis revealed that being in the poorly differentiated group was a risk factor for poorer prognosis. Thoracic tumor radiation therapy, chemotherapy, and surgery positively influenced the time interval between lung cancer diagnosis and brain metastasis.

Conclusions

The pathological subtypes of lung adenocarcinoma-derived brain metastases are associated with patient prognosis. Patients in the poorly differentiated group have worse prognoses compared to those in the moderately to highly differentiated group. Therefore, patients in the poorly differentiated group may require more frequent follow-ups and aggressive treatment.

Extracranial metastasis sites correlate to the incidence risk of brain metastasis in stage IV non-small cell lung cancer: a population-based study

PURPOSE

The aim of this study was to analyse the correlation of extracranial metastasis sites (ECMs) to the incidence risk of brain metastasis (BMs) in stage IV non-small cell lung cancer (NSCLC).

METHODS

18349 newly diagnosed patients were retrospectively analysed, and 4919 pairs of cases were matched by propensity score matching in a 1:1 ratio. Alternative factors were analysed by multivariable logistic regression analysis. And the interaction analysis and subgroup analysis were carried out.

RESULTS

The incidences of Brain, Lung, Liver, Bone, Multiple and Other metastasis were 26.9%, 20.2%, 4.6%, 19.9%, 16.9% and 38.3%, respectively. Results suggested that Age, Race, Histological type, Grade, T stage, N stage and Organ metastasis site were risk factors (p < 0.05). The interaction analysis suggested interaction effects between the Primary site, T stage, N stage and Organ metastasis site. The subgroup analysis showed that the Organ metastasis site and the risk of BMs were statistically significant except that the Overlapping subgroup (p = 0.267) of the Primary site. And the incidence risk of BMs in Lung metastasis, Liver metastasis and Bone metastasis groups was lower than that in other metastasis group (OR 1, p < 0.05). There was no significant difference between the Multiple metastasis group and the other metastasis group (OR 1.091, p = 0.169).

CONCLUSION

Advanced age, non-Asian/Pacific Islander, non-squamous cell carcinoma, poorly differentiated grade, and higher T/N stage were risk factors for increased BMs in stage IV NSCLC, and the ECMs were associated with the risk of BMs.

Prediction of distant organ metastasis and overall survival of lung cancer patients: a SEER population-based cohort study

Background

Distant organ metastasis is a common event in lung cancer (LC). However, the preferential metastatic pattern of different pathological types of LC and its effect on prognosis have not been comprehensively elucidated. This study aimed to explore the distant metastasis pattern and construct nomograms predicting the metastasis and survival of LC patients using the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

LC data were downloaded from the SEER database to conduct logistic regression and investigate risk factors for developing organ metastasis. A Cox regression analysis was conducted to investigate prognostic factors of LC. A Kaplan-Meier analysis was used to estimate overall survival outcomes. Nomograms were constructed to predict the probability of organ metastasis and the 1-, 3- and 5-year survival probability of LC patients. Receiver operating characteristic curves were used to evaluate the diagnostic accuracy of the nomograms. All statistical analyses were conducted within R software.

Results

The liver is the most common metastatic organ of small cell carcinoma. The brain is the most likely metastasis site of large cell carcinoma, and bone is the most likely metastasis site for squamous cell carcinoma and adenocarcinoma. Patients with triple metastases (brain-bone-liver) have the worst prognosis, and for nonsquamous carcinoma with single organ metastasis, liver metastases conferred the worst prognosis. Our nomograms based on clinical factors could predict the metastasis and prognosis of LC patients.

Conclusion

Different pathological types of LC have different preferential metastatic sites. Our nomograms showed good performance in predicting distant metastasis and overall survival. These results will provide a reference for clinicians and contribute to clinical evaluations and individualized therapeutic strategies.

Factors for incidence risk and prognosis of synchronous brain metastases in pulmonary large cell carcinoma patients: a population-based study

BACKGROUND

Patients with pulmonary large cell carcinoma (LCC) have a high incidence of synchronous brain metastases (SBM) and a poor prognosis. Our study was to evaluate the predictive and prognostic value of the clinical characteristics of pulmonary LCC patients with SBM at initial diagnosis by utilizing the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

LCC patients, diagnosed from 2010 to 2019, were identified from the latest SEER database which was released in April 2022. Logistic regression and Cox regression were used to identify the predictive and prognostic factors for LCC patients with SBM. Propensity score matching (PSM) and Kaplan-Meier analyses were applied to assess different therapy modalities.

RESULTS

A total of 1375 LCC patients were enrolled in this study and 216 (15.7%) of them had SBM at the initial diagnosis. The median overall survival (OS) of LCC patients with SBM was 4 months. Multivariate Cox regression identified age 60-79 (OR 0.57; 95% CI 0.41-0.78; p < 0.001), age ≥ 80 (OR 0.23; 95% CI 0.12-0.45; p < 0.001) and bone metastases (OR 1.75; 95% CI 1.22-2.51; p < 0.001) as significant independent predictors for developing SBM. Multivariable Cox regression revealed that age 60-79, T stage, bone metastases and chemotherapy were independent prognostic factor for OS. The surgery combined with chemotherapy and radiotherapy group, in which all patients were N0 stage and had no other site-specific metastases, exhibited the best median OS of 15 months.

CONCLUSIONS

LCC patients with age < 60 or bone metastases were more likely to have SBM at initial diagnosis. Age, T stage, bone metastases and chemotherapy were independent prognostic factors for OS of LCC patients with SBM. Highly selected patients might achieve the best survival benefit from surgery combined with chemotherapy and radiotherapy.

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

Background

This study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.

Methods

A total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.

Results

For distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.

Conclusions

Our study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.