• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer.

作者信息

Wiesweg M, Mairinger F, Reis H, Goetz M, Walter R F H, Hager T, Metzenmacher M, Eberhardt W E E, McCutcheon A, Köster J, Stuschke M, Aigner C, Darwiche K, Schmid K W, Rahmann S, Schuler M

机构信息

Department of Medical Oncology, West German Cancer Center; Division of Thoracic Oncology, Ruhrlandklinik; Genome Informatics, Institute of Human Genetics.

Institute of Pathology, West German Cancer Center.

出版信息

Ann Oncol. 2019 Apr 1;30(4):655-657. doi: 10.1093/annonc/mdz049.

DOI:10.1093/annonc/mdz049
PMID:30753264
Abstract
摘要

相似文献

1
Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer.基于机器学习的非小细胞肺癌免疫检查点抑制剂治疗预测指标
Ann Oncol. 2019 Apr 1;30(4):655-657. doi: 10.1093/annonc/mdz049.
2
EGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancer.非小细胞肺癌中 EGFR 突变亚型与免疫检查点阻断治疗的反应。
Ann Oncol. 2019 Aug 1;30(8):1311-1320. doi: 10.1093/annonc/mdz141.
3
Predictive effect of PD-L1 expression for immune checkpoint inhibitor (PD-1/PD-L1 inhibitors) treatment for non-small cell lung cancer: A meta-analysis.程序性死亡配体 1（PD-L1）表达对免疫检查点抑制剂（PD-1/PD-L1 抑制剂）治疗非小细胞肺癌的预测作用：一项荟萃分析。
Int Immunopharmacol. 2020 Mar;80:106214. doi: 10.1016/j.intimp.2020.106214. Epub 2020 Jan 23.
4
Emerging Blood-Based Biomarkers for Predicting Response to Checkpoint Immunotherapy in Non-Small-Cell Lung Cancer.用于预测非小细胞肺癌中检查点免疫疗法反应的新兴血液生物标志物
Front Immunol. 2020 Oct 16;11:603157. doi: 10.3389/fimmu.2020.603157. eCollection 2020.
5
Efficacy of anti-PD-1 antibodies in NSCLC patients with an EGFR mutation and high PD-L1 expression.抗 PD-1 抗体在 EGFR 突变和高 PD-L1 表达的 NSCLC 患者中的疗效。
J Cancer Res Clin Oncol. 2021 Jan;147(1):245-251. doi: 10.1007/s00432-020-03329-0. Epub 2020 Jul 23.
6
Immunotherapy in non-small cell lung cancer harbouring driver mutations.携带驱动基因突变的非小细胞肺癌的免疫治疗
Cancer Treat Rev. 2021 May;96:102179. doi: 10.1016/j.ctrv.2021.102179. Epub 2021 Mar 19.
7
Clinical Implications of Circulating Tumor DNA Tumor Mutational Burden (ctDNA TMB) in Non-Small Cell Lung Cancer.循环肿瘤 DNA 肿瘤突变负荷（ctDNA TMB）在非小细胞肺癌中的临床意义。
Oncologist. 2019 Jun;24(6):820-828. doi: 10.1634/theoncologist.2018-0433. Epub 2019 Mar 13.
8
KEYNOTE-024: Unlocking a pathway to lung cancer cure?KEYNOTE-024：开启肺癌治愈之路？
J Thorac Cardiovasc Surg. 2018 Apr;155(4):1777-1780. doi: 10.1016/j.jtcvs.2017.10.155. Epub 2017 Dec 23.
9
Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy.非小细胞肺癌活检的自动图像分析预测抗 PD-L1 治疗反应。
J Immunother Cancer. 2019 May 6;7(1):121. doi: 10.1186/s40425-019-0589-x.
10
There and back again: An immunotherapy tale.往返之间：一个免疫疗法的故事。
J Thorac Cardiovasc Surg. 2018 Apr;155(4):1771-1774. doi: 10.1016/j.jtcvs.2017.11.102. Epub 2017 Dec 23.

引用本文的文献

1
Artificial intelligence and anti-cancer drugs' response.人工智能与抗癌药物的反应。
Acta Pharm Sin B. 2025 Jul;15(7):3355-3371. doi: 10.1016/j.apsb.2025.05.009. Epub 2025 May 21.
2
Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.预测肺癌患者免疫检查点抑制剂治疗的生存获益：一种使用真实世界数据的机器学习方法。
Int J Clin Pharm. 2024 Oct 29. doi: 10.1007/s11096-024-01818-7.
3
Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning.
通过机器学习预测胶质母细胞瘤患者的免疫治疗结果
Cancers (Basel). 2024 Jan 18;16(2):408. doi: 10.3390/cancers16020408.
4
Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach.基于机器学习方法的 LUAD 突变亚型特异性药物反应的广泛预测。
Oncol Res. 2023 Dec 28;32(2):409-419. doi: 10.32604/or.2023.042863. eCollection 2023.
5
Machine Learning Approaches for Assessing Risk Factors of Adrenal Insufficiency in Patients Undergoing Immune Checkpoint Inhibitor Therapy.用于评估接受免疫检查点抑制剂治疗患者肾上腺功能不全风险因素的机器学习方法
Pharmaceuticals (Basel). 2023 Aug 3;16(8):1097. doi: 10.3390/ph16081097.
6
Cancer-Associated Fibroblasts Influence Survival in Pleural Mesothelioma: Digital Gene Expression Analysis and Supervised Machine Learning Model.癌症相关成纤维细胞影响胸膜间皮瘤的生存：数字基因表达分析和监督式机器学习模型
Int J Mol Sci. 2023 Aug 4;24(15):12426. doi: 10.3390/ijms241512426.
7
Gasdermin D Plays an Oncogenic Role in Glioma and Correlates to an Immunosuppressive Microenvironment.Gasdermin D 在胶质瘤中发挥致癌作用，并与免疫抑制微环境相关。
Biomolecules. 2023 May 29;13(6):904. doi: 10.3390/biom13060904.
8
Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.基于人工智能的非小细胞肺癌转录组RNA序列分析技术选择指南
Front Bioeng Biotechnol. 2023 Feb 15;11:1081950. doi: 10.3389/fbioe.2023.1081950. eCollection 2023.
9
Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review.人工智能辅助肺癌患者预后及药物疗效预测的决策：一项叙述性综述
J Thorac Dis. 2021 Dec;13(12):7021-7033. doi: 10.21037/jtd-21-864.
10
A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.泛癌症分析预测癌症免疫治疗反应的甲基化特征。
Front Immunol. 2021 Dec 9;12:796647. doi: 10.3389/fimmu.2021.796647. eCollection 2021.