Evidence map›Paper›PMID 33994790›Full record

ArticleBreast cancer : basic and clinical research2021

Application of OSNA Nomogram in Patients With Macrometastatic Sentinel Lymph Node: A Retrospective Assessment of Accuracy.

Francesca Combi, Alessia Andreotti, Anna Gambini, Enza Palma, Simona Papi, Alice Biroli, Stefania Zaccarelli, Guido Ficarra, Giovanni Tazzioli

Open access · goldAbstract read
In one paragraph

Article in Breast cancer : basic and clinical research, 2021. 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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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Francesca CombiClinical and Experimental Medicine, University of Modena and Reggio Emilia, Modena, Italy.ORCID https://orcid.org/0000-0001-7780-2851
Alessia AndreottiDivision of Breast Surgical Oncology, Department of Medical and Surgical Maternal-Infantile and Adult Sciences, University Hospital of Modena, Modena, Italy.
Anna GambiniDivision of Breast Surgical Oncology, Department of Medical and Surgical Maternal-Infantile and Adult Sciences, University Hospital of Modena, Modena, Italy.
Enza PalmaDivision of Breast Surgical Oncology, Department of Medical and Surgical Maternal-Infantile and Adult Sciences, University Hospital of Modena, Modena, Italy.
Simona PapiDivision of Breast Surgical Oncology, Department of Medical and Surgical Maternal-Infantile and Adult Sciences, University Hospital of Modena, Modena, Italy.
Alice BiroliFaculty of Medicine and Surgery, University of Modena and Reggio Emilia, Modena, Italy.
Stefania ZaccarelliDepartment of Pathology, University Hospital of Modena, Modena, Italy.
Guido FicarraDepartment of Pathology, University Hospital of Modena, Modena, Italy.
Giovanni TazzioliDivision of Breast Surgical Oncology, Department of Medical and Surgical Maternal-Infantile and Adult Sciences, University Hospital of Modena, Modena, Italy.
University of Modena and Reggio Emilia · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAlmost 50% to 70% of patients who undergo axillary lymph node dissection (ALND) because of a single metastatic sentinel lymph node (SLN) have no further metastatic nodes at the axillary histology. On these grounds, the one-step nucleic acid amplification (OSNA) nomogram was designed and validated. As a mathematical model, calculated through tumor size (expressed in millimeters) and CK19 mRNA copy number, it is thought to predict nonsentinel lymph node (NSLN) status. The aim of the study is to verify the diagnostic accuracy of the OSNA nomogram in a group of patients with macrometastatic SLN, with a retrospective analysis.

methodsThe OSNA nomogram was retrospectively applied to a group of 66 patients with macrometastatic SLN who underwent ALND. The result of the final histology of the axillary cavity was compared to the nomogram prediction. We calculated the prevalence of NSLN metastasis in patients who underwent ALND, sensitivity and specificity, negative and positive predictive value of the nomogram.

resultsIn patients with macrometastasis in SLN, the prevalence of patients with metastatic NSLN was 45%. The sensitivity of the nomogram was excellent (90%). The specificity was low (36%). Positive predictive value amounted to 54%, while negative predictive value was good (81%).

conclusionsThese results suggest that the OSNA nomogram is a valid instrument that can help choose the best surgical strategy for the treatment of axillary cavity. The mathematical model is useful to avoid surgery in a selected group of patients because it accurately predicts NSLN status.

Indexed as

axillaBreast neoplasmslymph node excisionnomogramsnucleic acid amplification techniquessentinel lymph node

Identifiers

PMID33994790
PMCPMC8113365
OpenAlexW3163186566

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