Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study

Seyed Ali Mirshahvalad; Adriano B Dias; Sangeet Ghai; Claudia Ortega; Nathan Perlis; Alejandro Berlin; Lisa Avery; Theodorus van der Kwast; Ur Metser; Patrick Veit-Haibach

doi:10.1016/j.acra.2024.08.004

Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study

Acad Radiol. 2024 Aug 12:S1076-6332(24)00567-1. doi: 10.1016/j.acra.2024.08.004. Online ahead of print.

Affiliations

¹ University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada.
² University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada. Electronic address: adriano.bassodias@uhn.ca.
³ Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
⁴ Department of Radiation Oncology, Princess Margaret Cancer Center, University Health Network & University of Toronto, Toronto, Ontario, Canada.
⁵ Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
⁶ Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada.

PMID: 39138108
DOI: 10.1016/j.acra.2024.08.004

Abstract

Rationale and objectives: To determine the role of dynamic contrast-enhanced (DCE) MRI-radiomics in predicting the International Society of Urological Pathology Grade Group (ISUP-GG) in therapy-naïve prostate cancer (PCa) patients.

Materials and methods: In this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardisation initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling.

Results: 73 index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). 16 features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%.

Conclusion: DCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.

Keywords: Cancer; Contrast enhancement; ISUP; Magnetic resonance imaging; PI-RADS; Prostate; Radiomics.