Evidence map›Paper›PMID 38288830›Full record

ArticleCurrent radiopharmaceuticals2024

Development and Validation of a Nomogram for Predicting Breast Malignancy in Male Patients Based on Clinical and Ultrasound Features.

Wei-Hong Dong, Gang Wu, Nan Zhao, Juan Zhang

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Current radiopharmaceuticals, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 98% 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.

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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
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

4 authors at 1 institution in 1 country.

Wei-Hong DongDepartment of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China.
Gang WuDepartment of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China.
Nan ZhaoDepartment of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China.
Juan ZhangDepartment of Ultrasound, Henan Provincial People's Hospital, Zhengzhou, 450000, Henan Province, China.
Henan Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to construct a nomogram based on clinical and ultrasound (US) features to predict breast malignancy in males.

methodsThe medical records between August, 2021 and February, 2023 were retrospectively collected from the database. Patients included in this study were randomly divided into training and validation sets in a 7:3 ratio. The models for predicting the risk of malignancy in male patients with breast lesions were virtualized by the nomograms.

resultsAmong the 71 enrolled patients, 50 were grouped into the training set, while 21 were grouped into the validation set. After the multivariate analysis was done, pain, BI-RADS category, and elastography score were identified as the predictors for malignancy risk and were selected to generate the nomogram. The C-index was 0.931 for the model. Concordance between predictions and observations was detected by calibration curves and was found to be good in this study. The model achieved a net benefit across all threshold probabilities, which was shown by the decision curve analysis (DCA) curve.

conclusionWe successfully constructed a nomogram to evaluate the risk of breast malignancy in males using clinical and US features, including pain, BI-RADS category, and elastography score, which yielded good predictive performance.

Indexed as

Breast Neoplasms, MaleElasticity Imaging TechniquesNomogramsAdultAgedHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentUltrasonography, MammaryBI-RADSBreast cancerbreast lesions.elastographynomogramultrasound

Identifiers

PMID38288830
OpenAlexW4391310638

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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.