Evidence map›Paper›PMID 40541533›Full record

ArticleUltrasound in medicine & biology2025

Random Forest-Based Detection of Metastases in Clinically Scanned Lymph Nodes Using Quantitative Ultrasound Imaging.

Elmira Ghahramani, Cameron Hoerig, Kirk Wallace, Maoxin Wu, Jonathan Mamou

Abstract read
In one paragraph

Article in Ultrasound in medicine & biology, 2025. 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

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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

1 citing paper in PubMed.

  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

5 authors.

Elmira GhahramaniDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA. Electronic address: elg4017@med.cornell.edu.
Cameron HoerigDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA.
Kirk WallaceGeneral Electric HealthCare Technology and Innovation Center, Niskayuna, NY, USA.
Maoxin WuDepartment of Pathology, Stony Brook Medicine, Stony Brook, NY, USA.
Jonathan MamouDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA.

Funding

In vivo Evaluation of Lymph Nodes Using Quantitative UltrasoundR01CA277038 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI JONATHAN MAMOU · 2023 to 2026
$3.3M
NCI NIH HHS R01 CA277038
6 · The paper itself

Abstract

objectiveQuantitative ultrasound (QUS) imaging has been used to characterize the microstructural properties of tissue using information contained in the backscattered radiofrequency (RF) echo signals. QUS methods were previously applied to detect metastases in excised human lymph nodes (LNs) that were raster scanned using a 30 MHz single-element transducer ex vivo. In the current study, a QUS-based method to detect in vivo LN metastases using a clinical scanner was developed.

methodsParallel RF frames were captured from 46 cervical and axillary LNs in 45 patients and two backscatter coefficient-based and two envelope statistics-based QUS parameters were computed and averaged for each frame. Different combinations of these four QUS parameters, along with the LN's short-axis and short-to-long axis ratio, were used to train random forest models to classify metastatic LNs.

resultsThe average QUS parameters and radiomics features were significantly different between metastatic and benign LNs (p≤10

conclusionThese results demonstrate the potential of QUS imaging using a clinical scanner for identifying metastatic LNs in vivo to help clinicians perform a more selective LN biopsy or excision.

Indexed as

Lymphatic MetastasisLymph NodesAdultAgedFemaleHumansMaleMiddle AgedRandom ForestSensitivity and SpecificityUltrasonographyBackscatter coefficientEnvelope statisticsFine-needle aspiration biopsyHomodyned K-distributionLymph node metastasisRandom forest classification

Identifiers

PMID40541533
PMCPMC12338076

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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.