Evidence map›Paper›PMID 37296471›Full record

ArticleBreast cancer research : BCR2023

Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach.

Giulia Ferroni, Soroosh Sabeti, Tasneem Abdus-Shakur, Lorenzo Scalise, Jodi M Carter, Robert T Fazzio, Nicholas B Larson, Mostafa Fatemi, Azra Alizad

Open access · goldAbstract read
In one paragraph

Article in Breast cancer research : BCR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.8field-weighted citation impact, top 5% 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

12 citing papers in PubMed, 20 citations in OpenAlex.

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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

9 authors at 2 institutions in 3 countries.

Giulia Ferroni *Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Soroosh Sabeti *Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Tasneem Abdus-ShakurDepartment of Radiology, Mayo Clinic College of Medicine and Science, 200 1st. St. SW, Rochester, MN, 55905, USA.
Lorenzo ScaliseDepartment of Industrial Engineering and Mathematical Science, Marche Polytechnic University, 60131, Ancona, Italy.
Jodi M CarterDepartment of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
Robert T FazzioDepartment of Radiology, Mayo Clinic College of Medicine and Science, 200 1st. St. SW, Rochester, MN, 55905, USA.
Nicholas B LarsonDepartment of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, 55905, USA.
Mostafa Fatemi *Department of Physiology and Biomedical Engineering, 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.
Mayo Clinic · USMarche Polytechnic University · IT

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
Multi-parameter Nonlinear Elasticity Mapping of Breast MassesR01CA195527 · NCI · MAYO CLINIC ROCHESTER · PI ALIZAD, AZRA, FATEMI, MOSTAFA · 2016 to 2020
$2.8M
NCI NIH HHS R01 CA195527NCI NIH HHS R01 CA239548
6 · The paper itself

Abstract

purposeChanges in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo. EXPERIMENTAL

designThe proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology.

resultsOut of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%.

conclusionsThe promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.

Indexed as

Breast NeoplasmsNeoplasms, Second PrimaryFemaleHumansLymphatic MetastasisLymph NodesMicrovesselsRetrospective StudiesSensitivity and SpecificityUltrasonographyAxillary lymph node metastasisBreast cancerFlow imagingUltrasound microvessel imagingVessel morphological biomarkers

Identifiers

PMID37296471
PMCPMC10257266
OpenAlexW4380081068

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.