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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

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Radiomics, a method analyzing quantitative imaging features, reveals a general prognostic phenotype in lung and head-and-neck cancers. This approach, using computed tomography data, can improve cancer treatment decisions.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Radiomics

Background:

  • Human cancers show distinct phenotypes visible through medical imaging.
  • Radiomics quantifies tumor phenotypes using numerous quantitative image features.

Purpose of the Study:

  • To conduct a radiomic analysis of lung and head-and-neck cancers.
  • To identify prognostic imaging features and their association with gene expression.

Main Methods:

  • Extracted 440 radiomic features (intensity, shape, texture) from computed tomography scans of 1,019 cancer patients.
  • Analyzed prognostic power in independent datasets.
  • Performed radiogenomics analysis to link radiomic signatures with gene expression.

Main Results:

  • Numerous radiomic features demonstrated significant prognostic power in both lung and head-and-neck cancer patient datasets.
  • A prognostic radiomic signature, reflecting intratumor heterogeneity, correlated with gene expression patterns.
  • Identified a generalizable prognostic phenotype across different cancer types.

Conclusions:

  • Radiomics can identify a common prognostic phenotype in lung and head-and-neck cancers.
  • This approach has potential clinical impact for cancer treatment decision support.
  • Routine use of imaging in practice offers a low-cost opportunity for improved patient care.