ArticleCancer medicine2023
Risk factors and prognostic factors for pulmonary large cell neuroendocrine carcinoma with brain metastasis.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Prognostic nomograms for overall and cancer-specific survival in individuals with large cell neuroendocrine carcinoma: a population-based study.Translational cancer research · 2026Article
- Primary tumor resection: a new hope or an old illusion for patients with metastatic non-small cell lung neuroendocrine tumors?World journal of surgical oncology · 2025Article
- Large cell neuroendocrine tumor of lung with hemorrhagic cerebral metastasis: A case report.Radiology case reports · 2025Article
- [Advances of Treatment of Pulmonary Large Cell Neuroendocrine Carcinoma].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Review
- Machine learning was used to predict risk factors for distant metastasis of pancreatic cancer and prognosis analysis.Journal of cancer research and clinical oncology · 2023Article
- Risk factors and prognostic factors for pulmonary large cell neuroendocrine carcinoma with brain metastasis.Cancer medicine · 2023Article
- Validity and reliability of a Chinese language suicide screening questionnaire-observer rating (CL-SSQ-OR) assessment for children/adolescents.Frontiers in psychiatry · 2023Article
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5 authors.
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Abstract
backgroundAs 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.
methodsThe 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.
resultsTotally, 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.
conclusionWe 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.
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