• 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

逻辑回归和其他统计工具在诊断生物标志物研究中的应用。

Logistic regression and other statistical tools in diagnostic biomarker studies.

机构信息

Pharmaceutical Biology Department, Faculty of Pharmacy and Biotechnology, German University in Cairo, Cairo, 11835, Egypt.

出版信息

Clin Transl Oncol. 2024 Sep;26(9):2172-2180. doi: 10.1007/s12094-024-03413-8. Epub 2024 Mar 26.

DOI:10.1007/s12094-024-03413-8
PMID:38530558
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11333519/
Abstract

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

摘要

生物标志物是多种过程的测量指标,通常用作疾病诊断的临床工具。虽然生物标志物从实验室到临床的发展过程很复杂,但最初的探索阶段通常侧重于通过利用各种统计方法来描述生物标志物的潜力,这些方法可用于评估其判别性能、建立适当的截断值,将连续数据转换为确认或排除诊断的适当二进制响应,或在针对混杂因素进行测试时建立稳健的关联。本篇综述旨在对诊断生物标志物研究中最常用的工具进行简要介绍,重点介绍逻辑回归,以评估生物标志物的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2136/11333519/0a7fd77e16a2/12094_2024_3413_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2136/11333519/0a7fd77e16a2/12094_2024_3413_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2136/11333519/0a7fd77e16a2/12094_2024_3413_Fig1_HTML.jpg

相似文献

1
Logistic regression and other statistical tools in diagnostic biomarker studies.逻辑回归和其他统计工具在诊断生物标志物研究中的应用。
Clin Transl Oncol. 2024 Sep;26(9):2172-2180. doi: 10.1007/s12094-024-03413-8. Epub 2024 Mar 26.
2
Zinc α2-glycoprotein as a potential novel urine biomarker for the early diagnosis of prostate cancer.锌 α2-糖蛋白作为一种潜在的新型尿液生物标志物用于前列腺癌的早期诊断。
BJU Int. 2012 Dec;110(11 Pt B):E688-93. doi: 10.1111/j.1464-410X.2012.11501.x. Epub 2012 Sep 28.
3
Biomarkers: diagnostic highlights and surrogate end points. Cambridge Healthtech Institute's biomarker series: biomarker validation: bringing discovery to the clinic & cancer biomarkers: from discovery to clinical practice. May 3-5, 2004, Philadelphia, Pennsylvania, USA.生物标志物:诊断要点与替代终点。剑桥健康科技研究所生物标志物系列:生物标志物验证:从发现到临床应用以及癌症生物标志物:从发现到临床实践。2004年5月3日至5日,美国宾夕法尼亚州费城
Pharmacogenomics. 2004 Jul;5(5):459-61. doi: 10.1517/14622416.5.5.459.
4
Prognostic immunophenotypic biomarker studies in diffuse large B cell lymphoma with special emphasis on rational determination of cut-off scores.弥漫性大 B 细胞淋巴瘤预后免疫表型标志物研究,特别强调截取值的合理确定。
Leuk Lymphoma. 2010 Feb;51(2):199-212. doi: 10.3109/10428190903370338.
5
ProteinChips: the essential tools for proteomic biomarker discovery and future clinical diagnostics.蛋白质芯片:蛋白质组学生物标志物发现及未来临床诊断的关键工具。
Clin Chem Lab Med. 2005;43(12):1279-80. doi: 10.1515/CCLM.2005.221.
6
Biomarker evaluation and comparison using the controls as a reference population.以对照组作为参考人群进行生物标志物评估和比较。
Biostatistics. 2009 Apr;10(2):228-44. doi: 10.1093/biostatistics/kxn029. Epub 2008 Aug 28.
7
Revisiting the technical validation of tumour biomarker assays: how to open a Pandora's box.重新审视肿瘤生物标志物检测的技术验证:如何打开潘多拉的盒子。
BMC Med. 2011 Apr 19;9:41. doi: 10.1186/1741-7015-9-41.
8
Multidimensional biomarker approach integrating tumor markers, inflammatory indicators, and disease activity indicators may improve prediction of rheumatoid arthritis-associated interstitial lung disease.整合肿瘤标志物、炎症指标和疾病活动指标的多维生物标志物方法可能会改善类风湿关节炎相关间质性肺病的预测。
Clin Rheumatol. 2024 Jun;43(6):1855-1863. doi: 10.1007/s10067-024-06984-7. Epub 2024 May 5.
9
Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate.基于连续协变量的时间依赖型ROC曲线的非参数估计。
Stat Med. 2016 Mar 30;35(7):1090-102. doi: 10.1002/sim.6769. Epub 2015 Oct 20.
10
Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves.生物标志物数据分析:日志、优势比和受试者工作特征曲线。
Curr Opin HIV AIDS. 2010 Nov;5(6):473-9. doi: 10.1097/COH.0b013e32833ed742.

引用本文的文献

1
Cost-Efficient Early Diagnostic Tool for Lung Cancer: Explainable AI in Clinical Systems.肺癌的经济高效早期诊断工具:临床系统中的可解释人工智能
Technol Cancer Res Treat. 2025 Jan-Dec;24:15330338251370239. doi: 10.1177/15330338251370239. Epub 2025 Aug 14.
2
Likelihood of blood culture positivity using SeptiCyte RAPID.使用SeptiCyte RAPID进行血培养阳性的可能性。
medRxiv. 2025 May 13:2025.05.09.25327025. doi: 10.1101/2025.05.09.25327025.
3
Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study.

本文引用的文献

1
Bayesian Strategies in Rare Diseases.贝叶斯策略在罕见病中的应用。
Ther Innov Regul Sci. 2023 May;57(3):445-452. doi: 10.1007/s43441-022-00485-y. Epub 2022 Dec 24.
2
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.基于机器学习的疾病风险预测的特征选择方法综述
Front Bioinform. 2022 Jun 27;2:927312. doi: 10.3389/fbinf.2022.927312. eCollection 2022.
3
Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance.阿尔茨海默病血浆生物标志物的混杂因素及其对临床性能的影响。
利用中国深圳的常规血液和生化指标构建职业性噪声性听力损失风险预测模型:一项预测建模研究
BMJ Open. 2025 Apr 28;15(4):e097249. doi: 10.1136/bmjopen-2024-097249.
4
Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis.用于预测特发性肉芽肿性乳腺炎复发的深度学习模型
J Inflamm Res. 2025 Feb 26;18:2943-2953. doi: 10.2147/JIR.S499512. eCollection 2025.
5
Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis.使用机器学习模型对乳腺癌患者进行预后预测:一项回顾性分析。
Gland Surg. 2024 Sep 30;13(9):1575-1587. doi: 10.21037/gs-24-106. Epub 2024 Sep 27.
6
A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.一种使用RNA测序、临床和合并症数据预测COVID-19严重程度的机器学习模型。
Diagnostics (Basel). 2024 Jun 18;14(12):1284. doi: 10.3390/diagnostics14121284.
7
Surprising and novel multivariate sequential patterns using odds ratio for temporal evolution in healthcare.利用优势比探索医疗保健中时间演变的令人惊讶和新颖的多元序贯模式。
BMC Med Inform Decis Mak. 2024 Jun 13;24(1):165. doi: 10.1186/s12911-024-02566-4.
Alzheimers Dement. 2023 Apr;19(4):1403-1414. doi: 10.1002/alz.12787. Epub 2022 Sep 24.
4
LncRNA MSC-AS1 Is a Diagnostic Biomarker and Predicts Poor Prognosis in Patients With Gastric Cancer by Integrated Bioinformatics Analysis.长链非编码RNA MSC-AS1作为一种诊断生物标志物,通过综合生物信息学分析预测胃癌患者预后不良。
Front Med (Lausanne). 2021 Dec 2;8:795427. doi: 10.3389/fmed.2021.795427. eCollection 2021.
5
Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm.基于lasso 算法的职业人群代谢综合征相关生物标志物的筛选
Front Public Health. 2021 Oct 12;9:743731. doi: 10.3389/fpubh.2021.743731. eCollection 2021.
6
The Bayesian approach to evidence-based decision making.基于证据的决策制定的贝叶斯方法。
J Hepatobiliary Pancreat Sci. 2021 Jun;28(6):457-460. doi: 10.1002/jhbp.997.
7
Long non-coding RNA pairs to assist in diagnosing sepsis.长非编码 RNA 对协助诊断脓毒症。
BMC Genomics. 2021 Apr 16;22(1):275. doi: 10.1186/s12864-021-07576-4.
8
Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock.长链非编码 RNA THAP9-AS1 和 TSPOAP1-AS1 为小儿感染性休克提供潜在的诊断特征。
Biomed Res Int. 2020 Dec 1;2020:7170464. doi: 10.1155/2020/7170464. eCollection 2020.
9
Long Noncoding RNA and Predictive Model To Improve Diagnosis of Clinically Diagnosed Pulmonary Tuberculosis.长链非编码RNA与预测模型以改善临床诊断肺结核的诊断
J Clin Microbiol. 2020 Jun 24;58(7). doi: 10.1128/JCM.01973-19.
10
A review of feature selection methods in medical applications.医学应用中的特征选择方法综述。
Comput Biol Med. 2019 Sep;112:103375. doi: 10.1016/j.compbiomed.2019.103375. Epub 2019 Jul 31.