Luengo Gómez David, García Cerezo Marta, López Cornejo David, Salmerón Ruiz Ángela, González-Flores Encarnación, Melguizo Alonso Consolación, Láinez Ramos-Bossini Antonio Jesús, Prados José, Ortega Sánchez Francisco Gabriel
Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, Spain.
Department of Radiology, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain.
Bioengineering (Basel). 2025 Jul 21;12(7):786. doi: 10.3390/bioengineering12070786.
MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. Sixteen studies ( = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63-0.72), 0.73 (95% CI, 0.68-0.78), and 0.70 (95% CI, 0.65-0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75-0.80) and 6.03 (95% CI, 4.65-7.82). Funnel plot asymmetry and Egger's test ( = 0.025) indicated potential publication bias. Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice.
基于磁共振成像（MRI）的放射组学已成为一种有前景的方法，可用于加强直肠癌（RC）淋巴结分期的无创性术前评估。然而，由于已发表研究中方法的变异性，其临床应用仍然有限。我们进行了一项系统综述和荟萃分析，以综合基于MRI的放射组学模型预测RC患者病理淋巴结状态（pN）的诊断性能。在PubMed、Web of Science和Scopus中进行了系统的文献检索，以查找截至2024年12月31日发表的研究。符合条件的研究将基于MRI的放射组学应用于RC患者的pN预测。我们排除了其他成像来源以及结合放射组学和其他数据（如临床数据）的模型。所有具有可用结局指标的模型都纳入了数据分析。由两名审阅者独立进行数据提取和质量评估（QUADAS-2）。进行了随机效应荟萃分析，包括分层汇总接受者操作特征（HSROC）和限制最大似然估计（REML）分析，以汇总敏感性、特异性、曲线下面积（AUC）和诊断比值比（DOR）。还进行了敏感性分析和发表偏倚评估。纳入了16项研究（n = 3157例患者）。HSROC显示汇总的敏感性、特异性和AUC值分别为0.68（95%CI，0.63 - 0.72）、0.73（95%CI，0.68 - 0.78）和0.70（95%CI，0.65 - 0.75）。通过REML获得的平均汇总AUC和DOR分别为0.78（95%CI，0.75 - 0.80）和6.03（95%CI，4.65 - 7.82）。漏斗图不对称性和Egger检验（P = 0.025）表明存在潜在的发表偏倚。总体而言，基于MRI的放射组学模型在预测RC的pN状态方面显示出中等准确性，一些研究报告了出色的结果。然而，相关方法学方法的异质性，如MRI序列来源或所应用的机器学习方法，以及可能存在的发表偏倚，需要进一步标准化，并阻碍了它们向临床实践的转化。
