Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging

Sep 21, 2026

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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. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images across Gleason score-based risk categories (n=15). DNA from formalin-fixed paraffin-embedded (FFPE) samples supported gene expression analysis of androgen receptor, apoptosis, and hypoxia using Chromosome Analysis Suite (ChAS). Pearson correlation and ROC analysis assessed relationships and predictive accuracy, showing significant links between radiomic texture and copy number variation (CNV) and improved prediction of clinically significant PCa.

LanguageEnglish

CategoryResearch & Report

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Pages14

Reading Time35 min

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Abstract

Introduction

Materials and Methods

Results and Discussion

Conclusions

Article Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging Chidozie N. Ogbonnaya 1, Basim S. O. Alsaedi 2, Abeer J. Alhussaini 1, Robert Hislop 3, Norman Pratt 3, J. Douglas Steele 1, Neil Kernohan 4 and Ghulam Nabi 1, * Citation: Ogbonnaya, C.N.; Alsaedi, B.S.O.; Alhussaini, A.J.; Hislop, R.; Pratt, N.; Steele, J.D.; Kernohan, N.; Nabi, G. Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging. Int. J. Mol. Sci. 2024, 25, 5379. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijms25105379 Academic Editor: Xiangdong Xue Received: 13 April 2024 Revised: 6 May 2024 Accepted: 13 May 2024 Published: 15 May 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; [chidozie.ogbonnaya@yahoo.co.uk](chidozie.ogbonnaya@yahoo.co.uk) (C.N.O.); [a.j.a.h.m.alhussaini@dundee.ac.uk](a.j.a.h.m.alhussaini@dundee.ac.uk) (A.J.A.); [d.steele@dundee.ac.uk](d.steele@dundee.ac.uk) (J.D.S.) 2 Statistics Department, University of Tabuk, Tabuk 47512, Saudi Arabia; [balsaedi@ut.edu.sa](balsaedi@ut.edu.sa) 3 Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; [robert.hislop@nhs.scot](robert.hislop@nhs.scot) (R.H.); [norman.pratt@nhs.scot](norman.pratt@nhs.scot) (N.P.) 4 Department of Pathology, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; [neil.kernohan@nhs.scot](neil.kernohan@nhs.scot) * Correspondence: [g.nabi@dundee.ac.uk](g.nabi@dundee.ac.uk) Abstract: 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. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffinembedded (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. 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 . Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer. Keywords: radiogenomics; biparametric magnetic resonance imaging; copy n

FAQs

What is the purpose of the study on radiogenomics in localized prostate cancer?

The study aims to build a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes using prostatectomy histopathology as the reference standard.

Which imaging modalities and radiomic inputs were used?

Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images obtained before biopsy using biparametric MR imaging.

How were molecular features measured and how was prediction assessed?

DNA from FFPE samples was analyzed for gene expression related to androgen receptor, apoptosis, and hypoxia using ChAS. The relationship between gene expression changes and radiomic texture features was evaluated with Pearson correlation, and predictive performance was assessed with ROC analysis.

Sep 21, 2026

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