Evidence map›Paper›PMID 41961314›Full record

ArticleAbdominal radiology (New York)2026

CT-based Node-RADS for metastatic lymph node detection in colon cancer and influence of microsatellite instability.

Martina Conca, Giovanni Maria Rodà, Maurizio Cè, Letizia Di Meglio, Ejona Duka, Renato Fabrizio, Ludovica Baldari, Luigi Boni, Gianpaolo Carrafiello

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Martina ConcaUniversity of Milan, Milan, Italy.
Giovanni Maria RodàFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Maurizio CèFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy. maurizioce.md1@gmail.com.
Letizia Di MeglioFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Ejona DukaFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Renato FabrizioUniversity of Milan, Milan, Italy.
Ludovica BaldariFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Luigi BoniUniversity of Milan, Milan, Italy.
Gianpaolo CarrafielloUniversity of Milan, Milan, Italy.

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6 · The paper itself

Abstract

purposeTo evaluate the diagnostic accuracy of the CT-based Node-RADS score in differentiating benign and metastatic lymph nodes in colon cancer compared with standard size parameters and to assess the influence of microsatellite instability on diagnostic performance.

methodsThis retrospective study included 166 patients with histologically confirmed colon cancer who underwent preoperative contrast-enhanced CT. Two radiologists, blinded to histopathological findings, evaluated a total of 166 lymph nodes under the supervision of a third radiologist, calculating Node-RADS scores and long and short diameters. Univariate and multivariate analyses were performed, stratifying data based on histopathological findings and microsatellite instability. Diagnostic performance for the various radiological variables was assessed using Receiver Operating Characteristic (ROC) curves. Predictive logistic models were created.

resultsSixty-nine lymph nodes (42%) were histologically positive. Interobserver agreement for the Node-RADS score was excellent (κ = 0.88). All Node-RADS features were significantly associated with nodal status (p < 0.001), with texture showing the strongest correlation (Cramér's V = 0.74) and emerging as the only independent predictor in multivariable analysis (OR 5.10, 95% CI 2.46-12.05, p < 0.001). Overall, the Node-RADS score demonstrated good discriminative ability (AUC = 0.79, 95% CI 0.72-0.86), although not superior to short axis diameter (AUC = 0.75, 95% CI 0.68-0.83). Optimal diagnostic accuracy (0.77, 95% CI 0.70-0.93) was observed using a cutoff between Node-RADS categories 3 and 4. In tumors with microsatellite instability, Node-RADS performance decreased (AUC = 0.42, 95% CI 0.19-0.66) compared with microsatellite-stable tumors (AUC = 0.83, 95% CI 0.76-0.9). Repeated 5-fold cross-validation showed that the composite Node-RADS score was not inferior to logistic regression models including individual imaging features (AUC 0.786-0.802; all DeLong p > 0.01).

conclusionThe Node-RADS score demonstrates good diagnostic performance for nodal staging in colon cancer; however, its superiority over standard size criteria was not confirmed. Its diagnostic performance declines in tumors with microsatellite instability, underscoring the potential influence of microsatellite status on the reliability of CT-based lymph node assessment.

Indexed as

Colonic NeoplasmsLymphatic MetastasisMicrosatellite InstabilityTomography, X-Ray ComputedAdultAgedAged, 80 and overContrast MediaFemaleHumansLymph NodesMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityContrast MediaColonic NeoplasmsLymph NodesMicrosatellite InstabilityRadiology Reporting and Data SystemsTomographyX-Ray Computed

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.