Diagnostic accuracy of node-RADS for the detection of lymph node invasion: a systematic review and meta-analysis.

Abstract

Objective: To conduct a meta-analysis assessing the diagnostic performance of the node reporting and data system (Node-RADS) for detecting lymph node (LN) invasion.

Method: We performed a systematic literature search of online scientific publication databases from inception up to July 31, 2024. We used the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) to assess the study quality, and heterogeneity was determined by the Q-test and measured with I² statistics. We employed the hierarchic summary ROC (HSROC) model to estimate the summary sensitivity and specificity. Subgroup analyses were conducted according to the imaging modality and cutoff values.

Results: A total of 13 studies involving 1341 participants met the inclusion criteria. Pooled summary estimates of sensitivity, specificity, and area under the curve of HSROC were 0.79 (95% CI: 0.66-0.88), 0.86 (95% CI: 0.80-0.90), and 0.90 (95% CI: 0.87-0.92). Subgroup analysis showed that the pooled sensitivity and specificity for CT were 0.74 (95% CI: 0.63-0.83) and 0.84 (95% CI: 0.74-0.91), whereas for MRI were 0.84 (95% CI: 0.59-0.95) and 0.88 (95% CI: 0.81-0.93), respectively.

Conclusion: Node-RADS demonstrates the promising potential for the prediction of LN invasion, with high specificity but moderate sensitivity, particularly with optimal cutoff value ≥ 3. Indirect comparisons showed no significant difference between CT and MRI regarding overall diagnostic accuracy.

Key points: Question Since the Node-RADS has been proposed, a number of studies have assessed its diagnostic performance for evaluating LN invasion. Findings Node-RADS demonstrated high specificity but moderate sensitivity, and cutoff ≥ 3 is the optimal threshold; indirect comparison suggested no significant difference between CT and MRI. Clinical relevance This study synthesized currently available evidence on studies of utilizing Node-RADS for assessing LNI in patients with various cancers, providing valuable insights for radiologists for utilizing this new risk scoring system in clinical practice.