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.

Description

Acknowledgements: This work was supported by The Mark Foundation for Cancer Research [RG95043] and the Cancer Research UK Cambridge Centre [CTRQQR-2021/100012]. MCO received support from an NIHR i4i FAST grant [NIHR206092], Academy of Medical Sciences Springboard award [G117526], the EPSRC Tier-2 capital grant [EP/P020259/1], and GE HealthCare. This research was supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the Cambridge Experimental Cancer Medicine Centre (ECMC) [ECMCQQR-2022/100003]. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. SWB is supported by the Cancer Research UK Cambridge Centre [CTRQQR-2021/100012]. HP is supported by NIHR BioResource and AstraZeneca. HC is supported by the University of Cambridge Harding Distinguished Postgraduate Scholars Programme.

Funder: This work was supported by The Mark Foundation for Cancer Research [RG95043] and the Cancer Research UK Cambridge Centre [CTRQQR-2021/100012]. MCO received support from an NIHR i4i FAST grant [NIHR206092], Academy of Medical Sciences Springboard award [G117526], and the EPSRC Tier-2 capital grant [EP/P020259/1]. This research was supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the Cambridge Experimental Cancer Medicine Centre (ECMC) [ECMCQQR-2022/100003]. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Keywords

32 Biomedical and Clinical Sciences, 3211 Oncology and Carcinogenesis, 3204 Immunology, Networking and Information Technology R&D (NITRD), Human Genome, Genetics, Precision Medicine, Bioengineering, Machine Learning and Artificial Intelligence, Biomedical Imaging, Cancer, Cancer, 3 Good Health and Well Being

Journal Title

NPJ Precis Oncol

Journal ISSN

2397-768X
2397-768X

Volume Title

8

Publisher

Springer Nature

Publisher DOI

https://doi.org/10.1038/s41698-024-00656-0

Rights and licensing

Except where otherwised noted, this item's license is described as Attribution 4.0 International

Sponsorship

Engineering and Physical Sciences Research Council (EP/P020259/1)
Cancer Research UK (ECMCQQR-2022/100003)

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