Evidence map›Paper›PMID 40807976›Full record

ReviewSensors (Basel, Switzerland)2025

Advances in Photoacoustic Imaging of Breast Cancer.

Yang Wu, Keer Huang, Guoxiong Chen, Li Lin

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
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.

Yang WuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.
Keer HuangCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.
Guoxiong ChenHangzhou First People's Hospital, Hangzhou 310006, China.
Li LinCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.

Funding

the National Key R&D Program of China 2022YFC2407700Zhejiang Administration of Traditional Chinese Medicine joint Research Program GZY-ZJ-KJ-23023Zhejiang Province "Lingyan" R&D Program 2024C03066
6 · The paper itself

Abstract

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities-including mammography, ultrasound, and magnetic resonance imaging-face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques.

Indexed as

Breast NeoplasmsPhotoacoustic TechniquesContrast MediaEarly Detection of CancerFemaleHumansMagnetic Resonance ImagingMammographyContrast Mediabreast cancerdiagnostic accuracyearly screeningphotoacoustic imagingtherapeutic evaluation

Identifiers

PMID40807976
PMCPMC12349475

What OpenQuestion holds

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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.