放射基因组学是胶质母细胞瘤精准医疗的综合方法。

IF 4.7 2区 医学 Q1 ONCOLOGY Current Oncology Reports Pub Date : 2024-10-01 Epub Date: 2024-07-16 DOI:10.1007/s11912-024-01580-z
Isabella Sanchez, Ruman Rahman
{"title":"放射基因组学是胶质母细胞瘤精准医疗的综合方法。","authors":"Isabella Sanchez, Ruman Rahman","doi":"10.1007/s11912-024-01580-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.</p><p><strong>Recent findings: </strong>Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.</p>","PeriodicalId":10861,"journal":{"name":"Current Oncology Reports","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480134/pdf/","citationCount":"0","resultStr":"{\"title\":\"Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.\",\"authors\":\"Isabella Sanchez, Ruman Rahman\",\"doi\":\"10.1007/s11912-024-01580-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose of review: </strong>Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.</p><p><strong>Recent findings: </strong>Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.</p>\",\"PeriodicalId\":10861,\"journal\":{\"name\":\"Current Oncology Reports\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11480134/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Oncology Reports\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11912-024-01580-z\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Oncology Reports","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11912-024-01580-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/16 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

综述目的:异柠檬酸脱氢酶野生型胶质母细胞瘤是成人中最具侵袭性的原发性脑肿瘤。尽管接受了多模式治疗,但其浸润性和异质性使其预后不容乐观。为了提高胶质母细胞瘤治疗的存活率,越来越多的人提倡精准医疗;然而,传统的神经成像技术不足以提供准确诊断这种复杂疾病所需的细节:最新研究结果:先进的磁共振成像技术可以更全面地了解肿瘤微环境。将弥散和灌注磁共振成像技术相结合,形成多参数扫描,可提高诊断能力,并克服标准成像对肿瘤特征描述不可靠的问题。深度学习算法的最新进展证明了其在图像识别任务中的卓越能力。将这些算法与多参数扫描相结合,可确保捕捉到整个肿瘤,从而改变对患者的诊断和监测。作为必然结果,放射组学已成为一种强大的方法,通过从放射扫描中提取信息,并将这些肿瘤特征转化为定量数据,为诊断、预后、治疗和肿瘤反应提供见解。放射基因组学将成像特征与基因组图谱联系起来,在描述胶质母细胞瘤的特征和确定治疗反应方面显示出其能力,有望彻底改变胶质母细胞瘤的管理。将深度学习算法整合到放射基因组学模型中,建立了一种预测胶质母细胞瘤分子特征的自动化、可重复性高的方法,可进一步帮助预后判断和靶向治疗。然而,在将这一工作流程应用于临床实践之前,必须首先克服各种挑战,包括缺乏大型队列、缺乏标准化指南以及深度学习算法的 "黑箱 "性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine.

Purpose of review: Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.

Recent findings: Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.50
自引率
0.00%
发文量
187
审稿时长
6-12 weeks
期刊介绍: This journal aims to review the most important, recently published clinical findings in the field of oncology. By providing clear, insightful, balanced contributions by international experts, the journal intends to serve all those involved in the care of those affected by cancer. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas, such as cancer prevention, leukemia, melanoma, neuro-oncology, and palliative medicine. Section Editors, in turn, select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An international Editorial Board reviews the annual table of contents, suggests articles of special interest to their country/region, and ensures that topics are current and include emerging research. Commentaries from well-known figures in the field are also provided.
期刊最新文献
Paediatric Acute Lymphoblastic Leukaemia: A Narrative Review of Current Knowledge and Advancements. Telemedicine and Virtual Interventions in Cancer Rehabilitation: Practical Application, Complications and Future Potentials. Balancing Fertility Preservation and Treatment Efficacy in (Neo)adjuvant Therapy for Adolescent and Young Adult Breast Cancer Patients: a Narrative Review. Existing Health Inequities in the Treatment of Advanced and Metastatic Cancers. CAR T-cell Therapy for Central Nervous System Lymphoma.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1