放射学与多尺度数据整合,促进精准肿瘤学。

IF 6.8 1区 医学 Q1 ONCOLOGY NPJ Precision Oncology Pub Date : 2024-07-26 DOI:10.1038/s41698-024-00656-0
Hania Paverd, Konstantinos Zormpas-Petridis, Hannah Clayton, Sarah Burge, Mireia Crispin-Ortuzar
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引用次数: 0

摘要

在这篇视角论文中,我们探讨了将放射成像与其他数据类型整合的潜力,与其他多原子数据的融合相比,这是一个关键但尚未充分开发的领域。放射影像可提供癌症的全面、三维视图,捕捉活检或其他数据模式可能遗漏的特征。本文以精准肿瘤学为背景,探讨了将医学影像纳入数据整合模型的复杂性和挑战。我们介绍了成像-组学整合的不同类别,并讨论了最新进展,强调了将不同尺度的空间数据整合在一起所带来的机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Radiology and multi-scale data integration for precision oncology
In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.
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来源期刊
CiteScore
9.90
自引率
1.30%
发文量
87
审稿时长
18 weeks
期刊介绍: Online-only and open access, npj Precision Oncology is an international, peer-reviewed journal dedicated to showcasing cutting-edge scientific research in all facets of precision oncology, spanning from fundamental science to translational applications and clinical medicine.
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