Evidence map›Paper›PMID 32043792›Full record

ReviewThe oncologist2020

Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools.

Maria Adele Marino, Daly Avendano, Pedro Zapata, Christopher C Riedl, Katja Pinker

Registry-linked trialOpen access · bronzeAbstract readReview
In one paragraph

Review in The oncologist, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06185946 (Evaluation of Different Imaging Modalities in Regional Nodes Staging in Breast Cancer Patient), which is not on this map. Cited by 126 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
126citing papers in PubMed, 3 pooled it
4.7field-weighted citation impact, top 3% of its field
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.

NCT06185946 unknown statusnot on this mapstarted 2024, after this paper: background citation

Evaluation of Different Imaging Modalities in Regional Nodes Staging in Breast Cancer Patient

TypeobservationalSponsorMennatallah Arous MohamedRan2024 to 2025Enrolled100ConditionsBreast Carcinoma Metastatic in Lymph Node
3 · Its place in the literature

Who cites it

126 citing papers in PubMed, 3 syntheses or guidelines pooled it, 222 citations in OpenAlex.

  1. Pooled it
  2. Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
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  9. AlEuropean journal of nuclear medicine and molecular imaging · 2026
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66 more citing papers are in PubMed but not listed here.

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

5 authors at 3 institutions in 4 countries.

Maria Adele MarinoDepartment of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy.ORCID 0000-0002-5614-2660
Daly AvendanoDepartment of Breast Imaging, Breast Cancer Center TecSalud, Instituto Tecnológico de Estudios Superiores (ITESM) Monterrey, Nuevo Leon, Mexico.
Pedro ZapataDepartment of Radiology, San Felipe de Jesus Hospital, Monterrey, Nuevo Leon, Mexico.
Christopher C RiedlBreast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Katja PinkerBreast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.ORCID 0000-0002-2722-7331
Memorial Sloan Kettering Cancer Center · USTecnológico de Monterrey · MXUniversity of Messina · IT

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

The detection of lymph node metastasis affects the management of patients with primary breast cancer significantly in terms of staging, treatment, and prognosis. The main goal for the radiologist is to determine and detect the presence of metastatic disease in nonpalpable axillary lymph nodes with a positive predictive value that is high enough to initially select patients for upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with different imaging modalities, but ultrasound is the method of choice for evaluating axillary lymph nodes and for performing image-guided lymph node interventions. This review aims to provide a comprehensive overview of the available imaging modalities for lymph node assessment in patients diagnosed with primary breast cancer. IMPLICATIONS FOR PRACTICE: The detection of lymph node metastasis affects the management of patients with primary breast cancer. The main goal for the radiologist is to detect lymph node metastasis in patients to allow for the selection of patients who should undergo upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with mammography, computed tomography, and magnetic resonance imaging, but ultrasonography is the imaging modality of choice for evaluating axillary lymph nodes. A normal axillary lymph node is characterized by a reniform shape, a maximal cortical thickness of 3 mm without focal bulging, smooth margins, and, depending on size, a discernable central fatty hilum.

Indexed as

Breast NeoplasmsAxillaFemaleHumansLymphatic MetastasisLymph Node ExcisionLymph NodesNeoplasm StagingSensitivity and SpecificitySentinel Lymph Node BiopsyAxillaLymph nodesMagnetic resonance imagingPositron emission tomographyUltrasonography

Identifiers

PMID32043792
PMCPMC7011661
OpenAlexW2979706663

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.