Evidence map›Paper›PMID 38392013›Full record

ArticleBiosensors2024

Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer.

José Alfonso Cruz-Ramos, Mijaíl Irak Trapero-Corona, Ingrid Aurora Valencia-Hernández, Luz Amparo Gómez-Vargas, María Teresa Toranzo-Delgado, Karla Raquel Cano-Magaña, Emmanuel De la Mora-Jiménez, Gabriela Del Carmen López-Armas

Abstract read
In one paragraph

Article in Biosensors, 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
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.

  1. Observational
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.

José Alfonso Cruz-RamosDepartamento de Clínicas Médicas, Instituto de Patología Infecciosa y Experimental, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara; Guadalajara 44340, Mexico.ORCID 0000-0002-5791-612X
Mijaíl Irak Trapero-CoronaSubdirección de Desarrollo Institucional, Instituto Jalisciense de Cancerología, Guadalajara 44280, Mexico.
Ingrid Aurora Valencia-HernándezDepartamento de Ciencias Computacionales, Instituto Nacional de Astrofísica Óptica y Electrónica, San Andrés Cholula 72840, Mexico.
Luz Amparo Gómez-VargasSubdirección de Desarrollo Institucional, Instituto Jalisciense de Cancerología, Guadalajara 44280, Mexico.
María Teresa Toranzo-DelgadoSubdirección de Desarrollo Institucional, Instituto Jalisciense de Cancerología, Guadalajara 44280, Mexico.
Karla Raquel Cano-MagañaSubdirección de Desarrollo Institucional, Instituto Jalisciense de Cancerología, Guadalajara 44280, Mexico.
Emmanuel De la Mora-JiménezSubdirección de Desarrollo Institucional, Instituto Jalisciense de Cancerología, Guadalajara 44280, Mexico.
Gabriela Del Carmen López-ArmasLaboratorio de Biomédica-Mecatrónica, Subdirección de Investigación y Extensión, Centro de Enseñanza Técnica Industrial Plantel Colomos, Guadalajara 44638, Mexico.ORCID 0000-0001-8571-8764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (-PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% -PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman's r = 0.073,

Indexed as

Brain NeoplasmsBreast NeoplasmsElasticity Imaging TechniquesBiopsyFemaleHumansMammographyProspective StudiesSensitivity and SpecificityUltrasonography, Mammarybreast cancerelastographyfat-to-lesion index

Identifiers

PMID38392013
PMCPMC10886583

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Registered trials

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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.