Evidence map›Paper›PMID 36660756›Full record

ArticleAdvanced biomedical research2022

A Combined Nomogram Model to Preoperatively Predict Positive Sentinel Lymph Biopsy for Breast Cancer In Iranian Population.

Nahid Nafissi, Bushra Zareie, Payman Rezagholi, Hassan Moayeri

Open access · goldAbstract read
In one paragraph

Article in Advanced biomedical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 58% 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

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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

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

4 authors at 3 institutions in 1 country.

Nahid NafissiDepartment of General Surgery, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.
Bushra ZareieDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Payman RezagholiDepartment of Operating Room, Faculty of Nursing and Midwifery, Kurdistan University of Medical Sciences, Sanandaj, Iran.
Hassan MoayeriDepartment of Surgery, School of Medicine, Kowsar Hospital, Kurdistan University of Medical Sciences, Sanandaj, Iran.
Kurdistan University of Medical Sciences · IRHamedan University of Medical Sciences · IRIran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Axillary dissection in breast cancer provides useful information on the degree of axillary nodule involvement, which serves as a reliable indicator for the prognosis and staging of breast cancer in patients. The aim of this study was to develop and validate the nomogram model by combining prognostic factors and clinical features to predict the node status of preoperative breast guard positive node cancer. Materials and Methods: Subjects consisted of patients referring to hospitals with the diagnosis of breast cancer. Patients were allowed to substitute molecular subtypes with data on breast cancer diagnosis and prognosis as well as sentinel node status. The bootstrap review was used for internal validation. The predicted performance was evaluated based on the area under the receiver operating characteristic curve. According to the logistic regression analysis, the nomograms reported material strength between predictors and final status reliability. Results: 1172 patients participated in the study, of whom only 539 patients had axillary lymph node involvement. The subtype, family history, calcification, and necrosis were not significantly related to axillary lymph node involvement. Tumor size, histological type, and lymphovascular invasion in multivariate logistic regression were significantly and directly correlated with axillary lymph node involvement. Conclusion: Nomograms, depending on the population, help make decisions to prevent axillary surgery. It seems that the prediction model presented in this study, based on the results of the neuromography, can help surgeons make a more informed decision on underarm surgery. Moreover, in some cases, their surgical program will be informed by accurate medical care and preclusion of major surgeries such as ALND.

Indexed as

Breast cancernomogramsentinel lymph node biopsy

Identifiers

PMID36660756
PMCPMC9843596
OpenAlexW4317567529

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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.