Unveiling prognostic factors and predictive modeling in lung adenocarcinoma with neuroendocrine differentiation.

IF 1.7 4区医学 Q4 ONCOLOGY

Translational cancer research Pub Date : 2025-08-31 Epub Date: 2025-08-22 DOI:10.21037/tcr-2025-12

Wei Li, Yuanming Pan, Siyu Cai, Shenglong Xie, Bin He

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引用次数: 0

Abstract

Background: Some lung cancer patients are pathologically confirmed to have lung adenocarcinoma with neuroendocrine differentiation (LUAD-ND). However, research on this subtype remains limited. This study aimed to systematically investigate the metastatic patterns and prognosis-related factors of LUAD-ND, and construct neural network-based prediction models for survival outcomes.

Methods: By analyzing the Surveillance, Epidemiology, and End Results (SEER) database, we employed the Cox proportional hazards model to investigate prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with LUAD-ND. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) and detailed the median survival time and specific time survival probabilities for different features in the LUAD-ND population. Finally, using a neural network algorithm, we developed a predictive model for forecasting LUAD-ND's OS and CSS, evaluating its performance using the area under the receiver operating characteristic curve (AUC).

Results: Most patients with LUAD-ND were diagnosed at an advanced stage. The OS time of patients with LUAD-ND was 12 (95% CI: 10-14) months, and the CSS time was 14 (95% CI: 12-16) months. The most common distant metastatic sites were bone, followed by liver, brain, and lung. Surgery (HR =0.51; 95% CI: 0.31-0.82; P=0.006) and chemotherapy (HR =0.33; 95% CI: 0.21-0.50; P<0.001) were associated with improved OS. Similarly, surgery (HR =0.49; 95% CI: 0.28-0.84; P=0.01) and chemotherapy (HR =0.31; 95% CI: 0.19-0.49; P<0.001) were linked to better CSS. The neural network-based tool can effectively predict the prognosis of LUAD-ND, achieving an AUC of 0.852-0.864 for 6-month OS and 0.835-0.883 for 6-month CSS.

Conclusions: Patients with LUAD-ND face a dismal prognosis, yet chemotherapy and surgical interventions can ameliorate their outcomes. The neural network tool developed in this study yields precise prognostic estimations.

Abstract Image

查看原文本刊更多论文

揭示肺腺癌神经内分泌分化的预后因素及预测模型。

背景：部分肺癌患者病理证实为肺腺癌伴神经内分泌分化（LUAD-ND）。然而，对这一亚型的研究仍然有限。本研究旨在系统探讨LUAD-ND的转移模式及预后相关因素，构建基于神经网络的生存预后预测模型。方法：通过分析监测、流行病学和最终结果（SEER）数据库，采用Cox比例风险模型探讨LUAD-ND患者总生存期（OS）和癌症特异性生存期（CSS）的预后因素。我们计算了LUAD-ND人群中不同特征的风险比（hr）和95%置信区间（ci），并详细描述了中位生存时间和特定时间生存概率。最后，我们利用神经网络算法建立了一个预测LUAD-ND的操作系统和CSS的预测模型，并使用接收器工作特性曲线下面积（AUC）来评估其性能。结果：大多数LUAD-ND患者诊断为晚期。LUAD-ND患者的OS时间为12 （95% CI: 10-14）个月，CSS时间为14 （95% CI: 12-16）个月。最常见的远处转移部位是骨，其次是肝、脑和肺。手术（HR =0.51; 95% CI: 0.31-0.82； P=0.006）和化疗（HR =0.33; 95% CI: 0.21-0.50）结论：LUAD-ND患者预后不佳，但化疗和手术干预可改善其预后。本研究中开发的神经网络工具产生了精确的预测估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

期刊介绍： Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.