Evidence map›Paper›PMID 38867119›Full record

ArticleEuropean radiology2024

Performance of MRI for standardized lymph nodes assessment in breast cancer: are we ready for Node-RADS?

Federica Pediconi, Roberto Maroncelli, Marcella Pasculli, Francesca Galati, Giuliana Moffa, Andrea Marra, Andrea Polistena, Veronica Rizzo

Abstract read
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  7. Node-RADS in cervical cancer: a multi-reader agreement and diagnostic performance study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Federica Pediconi *Department of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.
Roberto Maroncelli *Department of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy. roberto.maroncelli@uniroma1.it.ORCID http://orcid.org/0009-0006-6626-2478
Marcella PasculliDepartment of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.
Francesca GalatiDepartment of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.
Giuliana MoffaDepartment of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.
Andrea MarraDepartment of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.
Andrea PolistenaDepartment of Surgery "Pietro Valdoni", Policlinico "Umberto I", Rome "Sapienza" University of Rome, 00128, Rome, Italy.
Veronica RizzoDepartment of Radiological, Oncological, and Pathological Sciences, Sapienza-University of Rome, 00185, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe Node-RADS score was recently introduced to offer a standardized assessment of lymph node invasion (LNI). We tested its diagnostic performance in accurately predicting LNI in breast cancer (BC) patients with magnetic resonance imaging. The study also explores the consistency of the score across three readers. MATERIALS AND

methodsA retrospective study was conducted on BC patients who underwent preoperative breast contrast-enhanced magnetic resonance imaging and lymph node dissection between January 2020 and January 2023. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated for different Node-RADS cut-off values. Pathologic results were considered the gold standard. The overall diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC). A logistic regression analysis was performed. Cohen's Kappa analysis was used for inter-reader agreement.

resultsThe final population includes 192 patients and a total of 1134 lymph nodes analyzed (372 metastatic and 762 benign). Increasing the Node-RADS cut-off values, specificity and PPV rose from 71.4% to 100% and 76.7% to 100%, respectively, for Reader 1, 69.4% to 100% and 74.6% to 100% for Reader 2, and from 64.3% to 100% and 72% to 100% for Reader 3. Node-RADS > 2 could be considered the best cut-off value due to its balanced performance. Node-RADS exhibited a similar AUC for the three readers (0.97, 0.93, and 0.93). An excellent inter-reader agreement was found (Kappa values between 0.71 and 0.83).

conclusionsThe Node-RADS score demonstrated moderate-to-high overall accuracy in identifying LNI in patients with BC, suggesting that the scoring system can aid in the identification of suspicious lymph nodes and facilitate appropriate treatment decisions. CLINICAL RELEVANCE STATEMENT: Node-RADS > 2 can be considered the best cut-off for discriminating malignant nodes, suggesting that the scoring system can effectively help identify suspicious lymph nodes by staging the disease and providing a global standardized language for clear communication. KEY POINTS: Axillary lymphadenopathies in breast cancer are crucial for determining the disease stage. Node-RADS was introduced to provide a standardized evaluation of breast cancer lymph nodes. RADS > 2 can be considered the best cut-off for discriminating malignant nodes.

Indexed as

Breast NeoplasmsLymphatic MetastasisLymph NodesMagnetic Resonance ImagingSensitivity and SpecificityAdultAgedAged, 80 and overContrast MediaFemaleHumansLymph Node ExcisionMiddle AgedPredictive Value of TestsRetrospective StudiesContrast MediaBreast cancerBreast CE-MRILymph node invasionNode-RADSTNM

Identifiers

PMID38867119
PMCPMC11557668

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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.