The diagnostic value of radiomics-based machine learning for lymph node metastasis in prostate cancer: a systematic
ZengHui Liu1,2, Yin Yang3, Xiaodong Guan4
1First Clinical College, Changzhi Medical College, Changzhi, Shanxi, China.
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 (SROC) of 0.93 (95% CI: 0.77-0.98). Models based on MRI had a SEN of 0.84 (95% CI: 0.78-0.89), SPC of 0.86 (95% CI: 0.71-0.94), and a SROC of 0.90 (95% CI: 0.71-0.97). Radiomics-based ML models yielded a SEN of 0.85 (95% CI: 0.76-0.91), a SPC of 0.77 (95% CI: 0.66-0.86), and an area under the receiver operating characteristic (AUROC) of 0.89 (95% CI: 0.72-0.96). In contrast, deep learning (DL) models based on radiomics demonstrated a higher SEN of 0.88 (95% CI: 0.75-0.95), SPC of 0.97 (95% CI: 0.58-1.00), and a SROC of 0.95 (95% CI: 0.19-1.00).
Conclusions:
Radiomics demonstrates promising diagnostic performance in detecting LNM in PC. DL models show superior accuracy. Nevertheless, given the limited sample sizes, insufficient external validation, and heterogeneity in imaging protocols, future research should incorporate more multi-center images from different regions. Meanwhile, it is necessary to develop standardized imaging and segmentation protocols to improve transparency and reduce heterogeneity, thereby building more widely applicable and high-performance radiomics-based machine learning models to improve the performance of early detection of LNM in PC patients.
Systematic Review Registration:
https://www.crd.york.ac.uk/prospero/, identifier PROSPERO CRD420251085724.
