Evidence map›Paper›PMID 40628955›Full record

ArticleScientific reports2025

Quantitative microvessel orientation biomarkers derived from contrast free ultrasound imaging for cancer diagnosis.

Pradeep Kumar Chaudhary, Nicholas B Larson, Azra Alizad, Mostafa Fatemi

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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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0citing papers in PubMed
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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.

No citing paper in PubMed yet.

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.

Pradeep Kumar ChaudharyDepartment of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Nicholas B LarsonDepartment of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Azra Alizad *Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA. Alizad.azra@mayo.edu.
Mostafa Fatemi *Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA. fatemi.mostafa@mayo.edu.

Funding

Using Label-free Ultrasound Angio-Morphometry Analysis for Improving Ultrasound Specificity in Breast Cancer Detection and Treatment MonitoringR01CA239548 · NCI · MAYO CLINIC ROCHESTER · PI ALIZAD, AZRA, FATEMI, MOSTAFA · 2019 to 2023
$2.9M
NCI NIH HHS R01 CA239548NIH HHS R01CA239548
6 · The paper itself

Abstract

Microvessel orientations are known to differ between benign and malignant tumors. This article introduces novel orientation-based quantitative biomarkers for contrast-free ultrasound microvasculature imaging to distinguish malignant from benign breast lesions. The proposed biomarkers were computed in both polar and Cartesian coordinates using images acquired by a high-definition microvasculature imaging technique. Seven biomarkers were derived based on the histogram, gradient angle, angle of penetration, and penetration factor of the microvessel. These biomarkers are first evaluated using simulated microvessel images and subsequently validated in in vivo studies on human breast masses. The new biomarkers demonstrated statistical significance in differentiating benign from malignant breast masses. In the in vivo study, the area under the receiver operating characteristic curve (AUC) for the proposed biomarkers was 0.91 (95% confidence interval [CI]: 0.86, 0.97). When the Breast Imaging Reporting and Data System (BI-RADS) score was included in the classification model, the AUC improved to 0.97 (95% CI: 0.91,1.00). These orientation-based biomarkers show promise in enhancing the diagnostic performance of ultrasound for classification of breast masses.

Indexed as

Biomarkers, TumorBreast NeoplasmsMicrovesselsUltrasonography, MammaryAdultArea Under CurveFemaleHumansMiddle AgedROC CurveUltrasonographyBiomarkers, Tumor

Identifiers

PMID40628955
PMCPMC12238592

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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.