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Unraveling Tumor Heterogeneity in Gynecological Cancer Using a Radiogenomics Approach.

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Radiogenomics non-invasively assesses tumor heterogeneity in ovarian and endometrial cancers by linking imaging to genomic data. This approach aids in predicting biomarkers, treatment response, and patient prognosis for personalized care.

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Area of Science:

  • Integrative oncology and medical imaging
  • Radiogenomics for gynecological malignancies

Background:

  • Ovarian cancer (OC) and endometrial cancer (EC) are heterogeneous gynecological cancers with complex molecular subtypes.
  • Traditional biopsy methods struggle to capture the full spatial and temporal tumor complexity.
  • Radiogenomics offers a non-invasive method to analyze tumor heterogeneity by integrating imaging and genomic data.

Purpose of the Study:

  • To critically review and synthesize research on radiogenomics applications in OC and EC.
  • To assess the correlation between imaging phenotypes and molecular biomarkers.
  • To demonstrate radiogenomics' potential in enhancing tumor characterization, biomarker prediction, and guiding treatment decisions.

Main Methods:

  • Narrative review synthesizing current literature on radiogenomics in OC and EC.
  • Evaluation of studies utilizing CT, MRI, and PET imaging.
  • Analysis focused on correlating imaging phenotypes with molecular biomarkers, gene expression, and clinical outcomes.

Main Results:

  • Radiogenomics successfully linked imaging features to key molecular alterations (e.g., BRCA mutations, HRD in OC; POLE mutations, MSI, TMB in EC).
  • Radiomic feature models showed significant performance in predicting prognosis, treatment response, and recurrence risk.
  • Established associations between imaging phenotypes and specific molecular biomarkers in both OC and EC.

Conclusions:

  • Radiogenomics enables non-invasive assessment of spatial and molecular heterogeneity in OC and EC.
  • Integrating imaging and genomic data improves predictions for biomarkers, therapy response, and survival.
  • Clinical integration requires methodological standardization, prospective validation, and incorporation into precision oncology workflows.