European radiology · 2025
Diagnostic accuracy of node-RADS for the detection of lymph node invasion: a systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Lu Feng, Zhao Yanjun, Wang Zhongjuan, Feng Ninghan
Аннотация
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.
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.