Evidence map›Paper›PMID 41602430›Full record

ReviewFrontiers in oncology2025

Ultrasound-based radiomics for the evaluation of breast cancer.

Fei-Yi Sun, De-Li Meng, Lin Liu, Xiu-Qun Cao, Lu Fu, Lei Meng, Xin-Wu Cui, Xiao-Fang Pan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Fei-Yi SunDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.
De-Li MengDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.
Lin LiuDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.
Xiu-Qun CaoDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.
Lu FuDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.
Lei MengDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.
Xin-Wu CuiDepartment of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiao-Fang PanDepartment of Medical Ultrasound, Health Medical Department, Central Hospital of Dalian University of Technology, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the most common cancer among women all over the world. Ultrasound examination is instrumental in breast lesion screening, diagnosis and prognosis assessment, relying on non-radiation, inexpensiveness and real-time operation. However, it still has some limitations in diagnostic sensitivity and specificity. Radiomics aims at extracting high-throughput quantitative features from medical images, so as to deeply mine image information and further discover tumor features that cannot be discerned by naked eyes. Ultrasound radiomics models are gradually applied in evaluating breast cancer diagnosis and therapy, aiming to help with the precise diagnosis, prediction and treatment. This review summarizes the recent research progress of ultrasound radiomics in diagnosing benign and malignant breast lesion, predicting molecular subtype, lymph node status, neoadjuvant chemotherapy response and disease prognosis. Besides, the review also discusses the challenges and future research perspectives regarding ultrasound-based radiomics for the evaluation of breast cancer.

Indexed as

artificial intelligencebreast cancerevaluationradiomicsultrasound

Identifiers

PMID41602430
PMCPMC12832378

What OpenQuestion holds

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LicenceCC BY
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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.