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癌症筛查:最新进展与未来方向。

Cancer screening: recent developments and future directions.

作者信息

Levman Jacob, Broza Yoav Y

机构信息

Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada.

Nova Scotia Health Authority, Halifax, NS, Canada.

出版信息

Sci Rep. 2025 Mar 19;15(1):9436. doi: 10.1038/s41598-025-89820-5.

DOI:10.1038/s41598-025-89820-5
PMID:40108205
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11923198/
Abstract

Cancer is among the most common causes of mortality worldwide. Screening for cancer involves surveillance of populations towards identification of cancer that was unknown to the patient. Therapeutic treatments for cancer generally are more effective when applied earlier on in the condition’s development, as such, screening for cancer has the potential to improve the standard for patient care and improving mortality and morbidity associated with the disease. Cancer screening has increasingly become dependent on advanced technologies to assist in the identification and characterization of tumours. This article Collection showcases current research towards the development of new methods for cancer screening, including the development of new advanced technologies in this domain such as novel methods incorporating liquid biopsies, tailed primer isothermal amplification assays, and infrared spectroscopy. As is common in modern research, approaches make use of computational techniques as a critical component in cancer screening, and research is highlighted on the use of artificial intelligence, which is now a common technological innovation contributing to the overall cancer screening process in research studies. Future directions for cancer screening are discussed.

摘要

癌症是全球最常见的死因之一。癌症筛查是指对人群进行监测,以发现患者未知的癌症。癌症的治疗通常在病情发展早期应用时更有效,因此,癌症筛查有可能提高患者护理标准,并降低与该疾病相关的死亡率和发病率。癌症筛查越来越依赖先进技术来辅助肿瘤的识别和特征描述。本文集展示了当前关于开发癌症筛查新方法的研究,包括该领域新先进技术的开发,如结合液体活检、尾引物等温扩增分析和红外光谱的新方法。正如现代研究中常见的那样,方法将计算技术作为癌症筛查的关键组成部分,并且重点介绍了人工智能的应用研究,人工智能现在是一项常见的技术创新,有助于整体癌症筛查研究过程。本文还讨论了癌症筛查的未来方向。

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本文引用的文献

1
Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125.使用卷积神经网络进行间接参考区间估计及其在癌抗原 125 中的应用。
Sci Rep. 2024 Aug 20;14(1):19332. doi: 10.1038/s41598-024-70074-6.
2
Automatized self-supervised learning for skin lesion screening.自动化的皮肤病变筛查自监督学习。
Sci Rep. 2024 Jun 3;14(1):12697. doi: 10.1038/s41598-024-61681-4.
3
Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study.利用机器学习技术解锁完整的血细胞计数作为乳腺癌风险分层工具:一项大规模回顾性研究。
Sci Rep. 2024 May 12;14(1):10841. doi: 10.1038/s41598-024-61215-y.
4
Cryptogenic non-cirrhotic HCC: Clinical, prognostic and immunologic aspects of an emerging HCC etiology.隐匿性非肝硬化 HCC:一种新兴 HCC 病因的临床、预后和免疫学特征。
Sci Rep. 2024 Feb 21;14(1):4302. doi: 10.1038/s41598-024-52884-w.
5
Protoporphyrin IX in serum of high-grade glioma patients: A novel target for disease monitoring via liquid biopsy.血清高水平原卟啉 IX 作为高级别脑胶质瘤患者液体活检疾病监测的新靶点。
Sci Rep. 2024 Feb 21;14(1):4297. doi: 10.1038/s41598-024-54478-y.
6
Novel urine cell-free DNA methylation markers for hepatocellular carcinoma.新型肝细胞癌尿液游离 DNA 甲基化标志物。
Sci Rep. 2023 Dec 7;13(1):21585. doi: 10.1038/s41598-023-48500-y.
7
A novel tailed primer nucleic acid test for detection of HPV 16, 18 and 45 DNA at the point of care.一种新型的长尾引物核酸检测方法,可在即时护理点检测 HPV 16、18 和 45 DNA。
Sci Rep. 2023 Nov 21;13(1):20397. doi: 10.1038/s41598-023-47582-y.
8
Multifractal analysis of cellular ATR-FTIR spectrum as a method for identifying and quantifying cancer cell metastatic levels.基于细胞 ATR-FTIR 光谱的多重分形分析作为一种鉴定和量化癌细胞转移水平的方法。
Sci Rep. 2023 Nov 2;13(1):18935. doi: 10.1038/s41598-023-46014-1.
9
CXCL5 and CXCL14, but not CXCL16 as potential biomarkers of colorectal cancer.CXCL5 和 CXCL14,但不是 CXCL16,可作为结直肠癌的潜在生物标志物。
Sci Rep. 2023 Oct 17;13(1):17688. doi: 10.1038/s41598-023-45093-4.
10
Detection method has independent prognostic significance in the PLCO lung screening trial.检测方法在 PLCO 肺癌筛查试验中具有独立的预后意义。
Sci Rep. 2023 Aug 17;13(1):13382. doi: 10.1038/s41598-023-40415-y.