• 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

肿瘤学中的计算建模与模拟

Computational modeling and simulation in oncology.

作者信息

Baumgartner Christian

机构信息

Department of Computer Science and Biomedical Engineering, Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria.

出版信息

Clin Transl Med. 2025 Sep;15(9):e70456. doi: 10.1002/ctm2.70456.

DOI:10.1002/ctm2.70456
PMID:40910400
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12411925/
Abstract

Computational modeling and simulation are playing an increasingly important role in oncology, bridging biological research, data science and clinical practice to better understand cancer complexity and inform therapeutic development. This special issue presents recent advances in multiscale modeling, artificial intelligence-driven systems, digital twins, and in silico trials, illustrating the evolving potential of computational tools to support innovation from bench to bedside. Together, these contributions outline a future in which precision medicine, adaptive therapies and personalized diagnostics are guided by integrative and predictive modeling approaches.

摘要

计算建模与模拟在肿瘤学中发挥着越来越重要的作用,它连接了生物学研究、数据科学和临床实践,以更好地理解癌症的复杂性并为治疗开发提供信息。本期特刊展示了多尺度建模、人工智能驱动系统、数字孪生和虚拟试验方面的最新进展,说明了计算工具在支持从实验室到临床的创新方面不断发展的潜力。这些贡献共同勾勒出一个未来,在这个未来中,精准医学、适应性疗法和个性化诊断将由综合和预测性建模方法指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a11/12411925/88d9e5c2dfb3/CTM2-15-e70456-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a11/12411925/88d9e5c2dfb3/CTM2-15-e70456-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a11/12411925/88d9e5c2dfb3/CTM2-15-e70456-g001.jpg

相似文献

1
Computational modeling and simulation in oncology.肿瘤学中的计算建模与模拟
Clin Transl Med. 2025 Sep;15(9):e70456. doi: 10.1002/ctm2.70456.
2
Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.人工智能助力心血管肿瘤精准医学:美国心脏协会科学声明
Circ Genom Precis Med. 2025 Apr;18(2):e000097. doi: 10.1161/HCG.0000000000000097. Epub 2025 Feb 24.
3
Integrating Remote Symptom Monitoring, Person-Centered Analytics, and Artificial Intelligence to Advance Precision Health Symptom Science in Oncology.整合远程症状监测、以患者为中心的分析和人工智能,以推动肿瘤学精准健康症状科学的发展。
Semin Oncol Nurs. 2025 Aug;41(4):151901. doi: 10.1016/j.soncn.2025.151901. Epub 2025 May 13.
4
Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives.人工智能进入肿瘤病理学领域:当前应用与未来展望。
Ann Oncol. 2025 Apr 28. doi: 10.1016/j.annonc.2025.03.006.
5
Computational Nuclear Oncology Toward Precision Radiopharmaceutical Therapies: Ethical, Regulatory, and Socioeconomic Dimensions of Theranostic Digital Twins.迈向精准放射性药物治疗的计算核肿瘤学:诊疗数字孪生体的伦理、监管和社会经济维度
J Nucl Med. 2025 May 1;66(5):748-756. doi: 10.2967/jnumed.124.268186.
6
Precision oncology: transforming cancer care through personalized medicine.精准肿瘤学:通过个性化医疗改变癌症治疗方式。
Med Oncol. 2025 Jun 9;42(7):246. doi: 10.1007/s12032-025-02817-y.
7
Artificial-intelligence-driven Innovations in Mechanistic Computational Modeling and Digital Twins for Biomedical Applications.用于生物医学应用的机械计算建模和数字孪生中的人工智能驱动创新。
J Mol Biol. 2025 Apr 30:169181. doi: 10.1016/j.jmb.2025.169181.
8
Precision Neuro-Oncology in Glioblastoma: AI-Guided CRISPR Editing and Real-Time Multi-Omics for Genomic Brain Surgery.胶质母细胞瘤中的精准神经肿瘤学:用于基因组脑手术的人工智能引导的CRISPR编辑和实时多组学技术
Int J Mol Sci. 2025 Jul 30;26(15):7364. doi: 10.3390/ijms26157364.
9
The dawn of a new era: can machine learning and large language models reshape QSP modeling?新时代的曙光:机器学习和大语言模型能否重塑定量系统药理学建模?
J Pharmacokinet Pharmacodyn. 2025 Jun 16;52(4):36. doi: 10.1007/s10928-025-09984-5.
10
Medical digital twins: enabling precision medicine and medical artificial intelligence.医学数字孪生:推动精准医学与医学人工智能发展
Lancet Digit Health. 2025 Jun 14:100864. doi: 10.1016/j.landig.2025.02.004.

本文引用的文献

1
Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell.单细胞测量的临床和转化模式:人工智能单细胞。
Clin Transl Med. 2024 Sep;14(9):e1818. doi: 10.1002/ctm2.1818.
2
The world's first digital cell twin in cancer electrophysiology: a digital revolution in cancer research?世界上首个癌症电生理学数字细胞孪生体:癌症研究的数字革命?
J Exp Clin Cancer Res. 2022 Oct 11;41(1):298. doi: 10.1186/s13046-022-02507-x.
3
Optimizing the future: how mathematical models inform treatment schedules for cancer.
优化未来:数学模型如何为癌症治疗方案提供信息。
Trends Cancer. 2022 Jun;8(6):506-516. doi: 10.1016/j.trecan.2022.02.005. Epub 2022 Mar 9.
4
Complete populations of virtual patients for in silico clinical trials.用于计算机模拟临床试验的虚拟患者完整群体。
Bioinformatics. 2021 Apr 1;36(22-23):5465-5472. doi: 10.1093/bioinformatics/btaa1026.
5
Correction to: A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases.对《一种经过验证的基于单细胞的复杂疾病诊断和治疗靶点识别策略》的更正
Genome Med. 2020 Apr 28;12(1):37. doi: 10.1186/s13073-020-00732-7.
6
A Review of Cell-Based Computational Modeling in Cancer Biology.癌症生物学中基于细胞的计算建模综述
JCO Clin Cancer Inform. 2019 Feb;3:1-13. doi: 10.1200/CCI.18.00069.
7
Multitask learning improves prediction of cancer drug sensitivity.多任务学习提高癌症药物敏感性预测。
Sci Rep. 2016 Aug 23;6:31619. doi: 10.1038/srep31619.
8
Integrative models of vascular remodeling during tumor growth.肿瘤生长过程中血管重塑的整合模型。
Wiley Interdiscip Rev Syst Biol Med. 2015 May-Jun;7(3):113-29. doi: 10.1002/wsbm.1295. Epub 2015 Mar 21.
9
Towards predicting the response of a solid tumour to chemotherapy and radiotherapy treatments: clinical insights from a computational model.从计算模型中获得的有关预测实体瘤对化疗和放疗治疗反应的临床见解。
PLoS Comput Biol. 2013;9(7):e1003120. doi: 10.1371/journal.pcbi.1003120. Epub 2013 Jul 11.
10
Multi-scale modeling of tissues using CompuCell3D.使用CompuCell3D对组织进行多尺度建模。
Methods Cell Biol. 2012;110:325-66. doi: 10.1016/B978-0-12-388403-9.00013-8.