Journal Article

Prostate cancer MRI methodological radiomics score: a EuSoMII radiomics auditing group initiative

Vernuccio, Federica, Trotta, Romina, Kalarakis, Georgios, Ghezzo, Samuele, Cuocolo, Renato, Klontzas, Michail E., Cavallo, Armando Ugo, Ponsiglione, Andrea, Fanni, Salvatore Claudio, Triantafyllou, Matthaios, Cannella, Roberto, Ugga, Lorenzo, Stanzione, Arnaldo

2025

Overview

Objectives To evaluate the quality of radiomics research in prostate MRI for the evaluation of prostate cancer (PCa) through the assessment of METhodological RadiomICs (METRICS) score, a new scoring tool recently introduced with the goal of fostering further improvement in radiomics and machine learning methodology. Materials and methods A literature search was conducted from July 1st, 2019, to November 30th, 2023, to identify original investigations assessing MRI-based radiomics in the setting of PCa. Seven readers with varying expertise underwent a quality assessment using METRICS. Subgroup analyses were performed to assess whether the quality score varied according to papers’ categories (diagnosis, staging, prognosis, technical) and quality ratings among these latter. Results From a total of 1106 records, 185 manuscripts were available. Overall, the average METRICS total score was 52% ± 16%. ANOVA and chi-square tests revealed no statistically significant differences between subgroups. Items with the lowest positive scores were adherence to guidelines/checklists (4.9%), handling of confounding factors (14.1%), external testing (15.1%), and the availability of data (15.7%), code (4.3%), and models (1.6%). Conversely, most studies clearly defined patient selection criteria (86.5%), employed a high-quality reference standard (89.2%), and utilized a well-described (85.9%) and clinically applicable (87%) imaging protocol as a radiomics data source. Conclusion The quality of MRI-based radiomics research for PCa in recent studies demonstrated good homogeneity and overall moderate quality. Key Points Question To evaluate the quality of MRI-based radiomics research for PCa, assessed through the METRICS score . Findings The average METRICS total score was 52%, reflecting moderate quality in MRI-based radiomics research for PCa, with no statistically significant differences between subgroups . Clinical relevance Enhancing the quality of radiomics research can improve diagnostic accuracy for PCa, leading to better patient outcomes and more informed clinical decision-making .

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Availability / Check lists / Chi-square test / Clinical medicine / Data collection / Decision making / Deep learning / Diagnostic Radiology / Homogeneity / Humans / Imaging / Imaging Informatics and Artificial Intelligence / Internal Medicine / Interventional Radiology / Literature reviews / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Male / Medical imaging / Medical prognosis / Medicine / Medicine & Public Health / Neuroradiology / Prostate cancer / Prostatic Neoplasms - diagnostic imaging / Prostatic Neoplasms - pathology / Quality assessment / Quality control / Radiology / Radiomics / Software / Statistical analysis / Subgroups / Systematic review / Ultrasound / Variance analysis