Radiogenomics map-based molecular and imaging phenotypical characterization in localised prostate cancer using pre-biopsy biparametric MR imaging

Chidozie N. Ogbonnaya, Basim S. O. Alsaedi, Abeer J. Alhussaini, Robert Hislop, Norman Pratt, Douglas Steele, Neil Kernohan, Ghulam Nabi (Lead / Corresponding author)

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Objective: To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard.

Methods: Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n=15) across different Gleason scorebased risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model.

Results: A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value= ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95.

Conclusion: Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into prediction model improved prediction accuracy of clinically significant prostate cancer.
Original languageEnglish
Article number5379
Number of pages14
JournalInternational Journal of Molecular Sciences
Issue number10
Publication statusPublished - 15 May 2024


  • Radiogenomics
  • Biparametric Magnetic Resonance Imaging
  • Copy Number Variation


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