The diagnostic value of radiomics-based machine learning for lymph node metastasis in prostate cancer: a systematic review and meta-analysis
Abstract
Background: The precise and noninvasive diagnosis of preoperative lymph node metastasis (LNM) in prostate cancer (PC) is challenging. Some studies have studied the application of radiomics-based machine learning (ML) for detecting LNM in PC. However, systematic evidence regarding its diagnostic performance is still lacking.
Aim: Our study aimed to systematically evaluate the accuracy of radiomics-based ML models in diagnosing LNM in PC, offering evidence-based support for the use of ML in clinical decision-making.
Methods: Cochrane, PubMed, EMBASE, and Web of Science were searched for eligible studies on the diagnostic performance of radiomics-based ML for LNM in PC until June 11, 2025. The risk of bias in the included studies was evaluated via the Radiomics Quality Score (RQS). Meta-analysis of sensitivity (SEN) and specificity (SPC) was performed using a bivariate mixed-effects model. Subgroup analyses were performed in the meta-analysis based on imaging modality and modeling approach. We conducted meta-analysis on the training and validation sets, respectively.
Results: A total of 22 studies were included, comprising 13 studies on positron emission tomography (PET)/computed tomography (CT)-based radiomics and nine studies on magnetic resonance imaging (MRI)-based radiomics. In the validation sets, models based on PET/CT yielded a pooled SEN of 0.89 (95% confidence interval (CI): 0.75-0.96), SPC of 0.82 (95% CI: 0.63-0.93), and a summary receiver operating characteristic…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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