Radiology and multi-scale data integration for precision oncology

被引:3
作者
Paverd, Hania [1 ,2 ,3 ]
Zormpas-Petridis, Konstantinos [4 ]
Clayton, Hannah [2 ,3 ]
Burge, Sarah [3 ]
Crispin-Ortuzar, Mireia [2 ,3 ]
机构
[1] Cambridge Univ Hosp NHS Fdn Trust, Cambridge, England
[2] Univ Cambridge, Dept Oncol, Cambridge, England
[3] Univ Cambridge, Canc Res UK Cambridge Ctr, Cambridge, England
[4] Fdn Policlin Univ Agostino Gemelli IRCCS, Rome, Italy
基金
英国工程与自然科学研究理事会;
关键词
MULTIMODAL DATA INTEGRATION; ARTIFICIAL-INTELLIGENCE; DE-IDENTIFICATION; LUNG-CANCER; SURVIVAL; FEATURES; MODEL;
D O I
10.1038/s41698-024-00656-0
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
引用
收藏
页数:9
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