Evidence map›Paper›PMID 41057952›Full record

ArticleBreast cancer research : BCR2025

Artificial intelligence-assisted ultrasound screening for breast cancer in China: a prospective, clustered, controlled, population-based study.

Jie Shen, Yajing Liu, Aihong Liu, Xiaoqin Gu, Jin Zhou, Peng Jiang, Miao Mo, Li Zhang, Chen Yang, Changming Zhou and 6 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06521788 (Application Research of B-ultrasound Assisted by Artificial Intelligence in Breast Cancer Screening), which is not on this 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

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.

NCT06521788 nacompletednot on this map

Application Research of B-ultrasound Assisted by Artificial Intelligence in Breast Cancer Screening

TypeinterventionalSponsorFudan UniversityRan2020 to 2023Enrolled21,790ConditionsBreast Cancer, ScreeningArmsAI-assisted breast ultrasound, Routine breast ultrasound
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

16 authors.

Jie Shen *Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Yajing Liu *Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Aihong Liu *Department of Ultrasound, Shanghai Pudong New Area Health Care Hospital for Women and Children, Shanghai, 201200, China.
Xiaoqin Gu *Shanghai Hongkou District Maternal and Child Health Care Center, 76 Balin Road, Shanghai, 200437, China.
Jin ZhouDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Peng JiangDepartment of Ultrasound, Shanghai Pudong New Area Health Care Hospital for Women and Children, Shanghai, 201200, China.
Miao MoDepartment of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Li ZhangDepartment of Cancer Control and Prevention, Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, 200136, China.
Chen YangDepartment of Cancer Control and Prevention, Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, 200136, China.
Changming ZhouDepartment of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Zezhou WangDepartment of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
Zhenyu XieTangqiao Community Health Service Center, Pudong New Area, 131 Pujian Road, Shanghai, 200127, China. xiezhenyu@sina.com.
Wen YaoShanghai Hongkou District Maternal and Child Health Care Center, 76 Balin Road, Shanghai, 200437, China. yw612423@163.com.
Shichong ZhouDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China. sczhou@hotmail.com.
Ying ZhengDepartment of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China. zhengying@fudan.edu.cn.
Cai ChangDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.

Funding

Shanghai Aging and Maternal and Child Health Research Project 2020YJZX0206
6 · The paper itself

Abstract

introductionBreast cancer Mammography (MAM) screening was proven to improve survival worldwide. However, younger patients with higher breast density made MAM less effective in China. It is necessary to establish Chinese-specific effective screening strategies. This study aims to explore the efficacy of artificial intelligence (AI)-assisted ultrasound breast cancer screening in China.

methodsEligible participants were those aged 35-69 years and were attending the Chinese "Two Cancer (breast and cervical cancer) Screening" program. Two districts were selected as cluster to receive either AI-assisted ultrasound screening or routine ultrasound screening. We obtained data on cancer diagnosis through active follow-up and linkage with municipal cancer registry. The primary outcome was improved screening sensitivity enabling the detection of more true-positive cases. This study is registered at ClinicalTrials.gov under the number NCT06521788 (Initial Release Date: 07/22/2024).

resultsA total of 21,790 individuals in two districts were included in this study, with 8,736 participants in Hongkou district receiving AI-assisted ultrasound screening and 13,054 in Pudong district undergoing routine ultrasound screening. Of the 21,790 screened participants, 232 (10.7‰) tested positive, with AI detecting similar positivity rates compared to routine screening (12.2‰ vs. 9.6‰, P = 0.07). After one year of follow-up, 49 participants were diagnosed with breast cancer: 30 were screen-detected cancers, and 19 were interval cancers. The AI group demonstrated a significantly higher screening sensitivity (75%, 95% CI 54.8-88.6) compared to the routine group (42.8%, 95% CI 22.6-65.6). AI-assisted screening identified more breast cancers than the routine screening group (AI: 21 of 8736; routine: 9 of 13,054, P = 0.001). However, there was no significant difference between the two groups in terms of interval cancer detection (AI: 7 of 8736; routine: 12 of 13,054, P = 0.789). Furthermore, the proportion of early-stage cancers among screen-detected cases was significantly higher in the AI group (95.2%, 20/21) than in the routine group (88.9%, 8/9; p < 0.001).

conclusionsAI-assisted ultrasound screening significantly increases the detection rate of early breast cancers. Trial registration This study is registered at ClinicalTrials.gov under the number NCT06521788 (Initial Release Date: 07/22/2024).

Indexed as

Artificial IntelligenceBreast NeoplasmsEarly Detection of CancerUltrasonography, MammaryAdultAgedChinaFemaleHumansMammographyMass ScreeningMiddle AgedProspective StudiesArtificial intelligenceBreast cancer screeningBreast ultrasound

Identifiers

PMID41057952
PMCPMC12506423

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Registered trials

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.