MRI-Derived Biomarkers and Radiomic Signatures for Early, Dose-Dependent Evaluation of Prostate Cancer Radiotherapy: An Exploratory Study

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Authors: Eleni Bekou, Admir Mulita, Ioannis M. Koukourakis, Nikolaos A. Courcoutsakis, A Kotini, Evlampia Psatha, Georgios Tsakaldimis, Ioannis Seimenis, Michael I. Koukourakis, Efstratios Karavasilis
Cited by: 1
References: 44

Abstract

This study provides an accurate assessment of radiotherapy-induced tissue changes in prostate cancer when relying solely on serum prostate-specific antigen kinetics. The current study aims to explore the role of quantitative magnetic resonance imaging and radiomic analyses. In this exploratory prospective study, 22 patients with histologically confirmed prostate cancer underwent multiparametric magnetic resonance imaging at three time points: pre-treatment, mid-treatment, and two months post-radiotherapy. Quantitative imaging analysis included total prostate volume, T2, apparent diffusion coefficient—ADC, and T2* mapping, alongside T2-weighted and diffusion-weighted radiomic feature extraction. Longitudinal changes and dose correlations were analyzed using repeated-measures ANOVA and linear mixed-effects models. Prostate volume increased from 44.22 ± 21.26 cm3 at baseline to 51.11 ± 22.36 cm3 mid-treatment (p < 0.001) and decreased to 37.98 ± 15.5626 cm3 post-treatment (p = 0.034), indicative of temporary radiation-induced glandular edema. T2 relaxation times decreased from 106.00 ± 23.74 ms to 93.33 ± 9.50 ms after therapy (p = 0.023), with androgen deprivation therapy influencing overall values (partial η2 = 0.228, p = 0.028), while ADC and T2* remained largely stable (p > 0.05). Radiomic features, particularly from DWI, exhibited subtle time- and dose-dependent variations. Radiation dose was significantly associated with volume and T2, but not with ADC or T2*. These findings suggest that quantitative MRI biomarkers combined with radiomic analysis may provide objective, non-invasive measures of early prostate cancer radiotherapy-induced changes. These imaging-derived metrics may capture early treatment-related tissue alterations and could provide exploratory signals for early treatment evaluation in prostate cancer, although their relationship with biochemical markers requires further validation.

Fields of Study & Subjects

Prostate Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging MRI in cancer diagnosis

Publication details

Journal / source
Journal of Imaging
Publisher
MDPI AG
Volume
12
Issue
5
Pages
213–213
Publication date
17 May 2026
Language
EN