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Radiology and multi-scale data integration for precision oncology.

Hania Paverd1,2,3, Konstantinos Zormpas-Petridis4, Hannah Clayton2,3

  • 1Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

NPJ Precision Oncology
|July 26, 2024
PubMed
Summary

Integrating radiological imaging with other data types offers a powerful approach for precision oncology. This perspective highlights opportunities and challenges in combining imaging-omics data for comprehensive cancer insights.

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Area of Science:

  • Oncology
  • Radiology
  • Bioinformatics
  • Data Science

Background:

  • Multi-omic data fusion is advancing rapidly in cancer research.
  • Radiological imaging offers unique 3D spatial insights into cancer biology.
  • Integrating imaging with other data types remains an underdeveloped area.

Purpose of the Study:

  • To explore the potential of integrating radiological imaging with other data types for precision oncology.
  • To discuss the complexities and challenges associated with medical imaging integration.
  • To highlight opportunities in combining multi-scale spatial data.

Main Methods:

  • Review and synthesis of current approaches in imaging-omics integration.
  • Categorization of different imaging-omics integration strategies.
  • Discussion of recent advancements and future directions.

Main Results:

  • Radiological imaging provides complementary spatial information to other data modalities.
  • Several categories of imaging-omics integration exist, each with unique benefits.
  • Significant opportunities arise from combining multi-scale spatial data for a comprehensive view.

Conclusions:

  • Integrating radiological imaging into data models is crucial for advancing precision oncology.
  • Addressing the challenges in imaging-omics integration will unlock new insights into cancer.
  • This approach promises a more holistic understanding of cancer through multi-modal data fusion.