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Conditional probabilistic diffusion model driven synthetic radiogenomic applications in breast cancer.

Lianghong Chen1, Zi Huai Huang2, Yan Sun1,2

  • 1Department of Computer Science, Western University, London, Ontario, Canada.

Plos Computational Biology
|October 7, 2024
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Summary

This study uses a novel Conditional Probabilistic Diffusion Model (CPDM) to create synthetic breast cancer (BC) MRIs from genomic data. These synthetic images aid in predicting BC subtypes and patient survival, advancing precision medicine.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Bioinformatics

Background:

  • Breast cancer (BC) heterogeneity poses challenges for diagnosis and treatment.
  • Limited availability of paired multi-omic and medical imaging data hinders research.
  • Radiogenomics requires robust methods to link genomic profiles with imaging features.

Purpose of the Study:

  • To develop a Conditional Probabilistic Diffusion Model (CPDM) for synthesizing Magnetic Resonance Images (MRIs) from multi-omic data in breast cancer.
  • To overcome the challenge of limited paired imaging and genomic datasets.
  • To explore the utility of generated synthetic MRIs for predicting clinical attributes and patient survival.

Main Methods:

  • Employed a Conditional Probabilistic Diffusion Model (CPDM) to synthesize MRIs using gene expression, copy number variation, and DNA methylation data.
  • Generated synthetic MRIs for 726 TCGA-BRCA patients lacking actual MRI data.
  • Validated CPDM performance using Frechet's Inception Distance (FID), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM).

Main Results:

  • The CPDM successfully generated synthetic MRIs with high fidelity (FID=2.02, MSE=0.02, SSIM=0.59).
  • Synthetic MRIs predicted ER+/HER2+ subtypes with high accuracy (AUROC=0.82, AUPRC=0.84).
  • Predicted patient survival using synthetic MRIs achieved a strong Concordance-index (C-index) of 0.88, outperforming baseline models.

Conclusions:

  • CPDMs can effectively generate realistic MRIs from multi-omic data for breast cancer patients.
  • Synthetic MRIs hold significant potential for radiogenomic research and understanding BC heterogeneity.
  • This approach advances precision medicine by enabling early detection and personalized treatment strategies.