Evidence map›Paper›PMID 41270708›Full record

ArticleUltrasonics2026

Feasibility of deep learning-based cancer detection in ultrasound microvascular images.

Kathlyne Jayne B Bautista, Thomas M Kierski, Isabel G Newsome, Hae Rim Lee, Wesley R Legant, David S Lalush, Paul A Dayton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ultrasonics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

7 authors.

Kathlyne Jayne B BautistaLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA. Electronic address: kathlyne@unc.edu.
Thomas M KierskiLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA.
Isabel G NewsomeLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA.
Hae Rim LeeLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA.
Wesley R LegantLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA; Department of Pharmacology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
David S LalushLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA.
Paul A DaytonLampe Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; North Carolina State University, Raleigh, NC, 27606, USA.

Funding

Academic-Industrial Partnership for Translation of Acoustic AngiographyR01CA189479 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAYTON, PAUL A · 2014 to 2025
$4.7M
Next-generation imaging to interrogate biological systems across time and spaceR35GM158040 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LEGANT, WESLEY R. · 2025 to 2025
$2.3M
NCI NIH HHS R01 CA189479NIGMS NIH HHS R35 GM158040
6 · The paper itself

Abstract

Acoustic angiography is a superharmonic contrast-enhanced ultrasound modality that maps 3-D microvasculature with fine spatial resolutions and has demonstrated potential to improve disease detection. However, the application of acoustic angiography for cancer detection currently faces challenges. Quantitative analysis relies on time-consuming, manual segmentation of individual vessels, and inter-operator variability limits reader-based discrimination. This feasibility study aims to address the limitations of current approaches with deep learning for efficient and accurate detection of tumor-associated vasculature in vivo and to validate against quantitative methods that evaluate vascular morphology. Convolutional neural networks (CNNs), namely EfficientNet, ResNet, and DenseNet, were trained on a newly collected dataset of acoustic angiography volumes (n = 195 with 98 controls and 97 tumors) in rodents using a nested cross-validation study. The best performing model, 3-D EfficientNet-B0, achieved a mean classification accuracy of 0.928 ± 0.034 with high sensitivity and specificity, comparable to previously published results. Comparison with quantitative methods in tumor cases showed correlation between high network attention regions and morphological features typically associated with malignant vessels, including increased density and tortuosity. These results highlight the efficiency and accuracy of end-to-end CNNs for tumor detection in acoustic angiography volumes, validated by known markers of malignancy.

Indexed as

AngiographyDeep LearningMicrovesselsNeoplasmsAnimalsFeasibility StudiesHumansRatsSensitivity and SpecificityUltrasonographyContrast-enhanced ultrasoundConvolutional neural networksDeep learningMicrobubbleMicrovascular imagingSuperharmonic imagingUltrasound

Identifiers

PMID41270708
PMCPMC13544616

What OpenQuestion holds

Textmetadata
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.