Cavallo Armando Ugo, Stanzione Arnaldo, Ponsiglione Andrea, Trotta Romina, Fanni Salvatore Claudio, Ghezzo Samuele, Vernuccio Federica, Klontzas Michail E, Triantafyllou Matthaios, Ugga Lorenzo, Kalarakis Georgios, Cannella Roberto, Cuocolo Renato
Istituto Dermopatico dell'Immacolata (IDI) IRCCS, Rome, Italy.
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Eur Radiol. 2025 Mar;35(3):1157-1165. doi: 10.1007/s00330-024-11299-x. Epub 2024 Dec 30.
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.
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.
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.
The quality of MRI-based radiomics research for PCa in recent studies demonstrated good homogeneity and overall moderate quality.
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.
通过评估方法学放射组学（METRICS）评分来评价前列腺MRI中用于评估前列腺癌（PCa）的放射组学研究质量，METRICS评分是最近推出的一种新评分工具，旨在促进放射组学和机器学习方法的进一步改进。
于2019年7月1日至2023年11月30日进行文献检索，以识别在PCa背景下评估基于MRI的放射组学的原始研究。七名专业程度不同的读者使用METRICS进行了质量评估。进行亚组分析以评估质量评分是否因论文类别（诊断、分期、预后、技术）以及这些类别中的质量评级而异。
在总共1106条记录中，有185篇手稿可供使用。总体而言，METRICS总分平均为52%±16%。方差分析和卡方检验显示亚组之间无统计学显著差异。得分最低的项目是遵循指南/清单（4.9%）、混杂因素处理（14.1%）、外部测试（15.1%）以及数据（15.7%）、代码（4.3%）和模型（1.6%）的可用性。相反，大多数研究明确界定了患者选择标准（86.5%），采用了高质量的参考标准（89.2%），并使用了描述详尽（85.9%）且临床适用（87%）的成像方案作为放射组学数据源。
近期研究中基于MRI的PCa放射组学研究质量显示出良好的同质性和总体中等质量。
问题 通过METRICS评分评估基于MRI的PCa放射组学研究质量。发现 METRICS总分平均为52%，反映了基于MRI的PCa放射组学研究质量中等，亚组之间无统计学显著差异。临床意义 提高放射组学研究质量可提高PCa的诊断准确性，从而改善患者预后并使临床决策更明智。
