Evidence map›Paper›PMID 32852589›Full record

ArticleEuropean radiology2021

The diagnostic performance of automated versus handheld breast ultrasound and mammography in symptomatic outpatient women: a multicenter, cross-sectional study in China.

Xi Lin, Mengmeng Jia, Xiang Zhou, Lingyun Bao, Yaqing Chen, Peifang Liu, Ruimei Feng, Xi Zhang, Luoxi Zhu, Hui Wang and 5 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.9field-weighted citation impact, top 11% of its field
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

14 citing papers in PubMed, 27 citations in OpenAlex.

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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

15 authors at 6 institutions in 2 countries.

Xi LinDepartment of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
Mengmeng JiaDepartment of Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Xiang ZhouDepartment of Interventional Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Lingyun BaoDepartment of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
Yaqing ChenDepartment of Ultrasound, Xin Hua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
Peifang LiuDepartment of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China.
Ruimei FengDepartment of Cancer Prevention Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
Xi ZhangKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital and Institute, Beijing, 100142, China.
Luoxi ZhuDepartment of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
Hui WangDepartment of Ultrasound, Xin Hua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
Ying ZhuDepartment of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China.
Guoxue TangDepartment of Ultrasound, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
Wenqi FengDepartment of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Anhua LiDepartment of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China. liah@sysucc.org.cn.
Youlin QiaoDepartment of Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. qiaoy@cicams.ac.cn.ORCID http://orcid.org/0000-0001-6380-0871
Chinese Academy of Medical Sciences & Peking Union Medical College · CNSun Yat-sen University · CNHangzhou First People's Hospital · CNShanghai Jiao Tong University · CNTianjin Medical University Cancer Institute and Hospital · CNPeking University · CN

Funding

Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences CIFMS 2017-I2M-B&R-03GE Healthcare 11183574598Peking Union Medical College Doctoral Innovation Fund 2017-1001-18
6 · The paper itself

Abstract

objectivesThe purpose of this study was to evaluate the diagnostic performance of automated breast ultrasound (ABUS) for breast cancer by comparing it to handheld ultrasound (HHUS) and mammography (MG).

methodsA multicenter cross-sectional study was conducted between February 2016 and March 2017 in five tertiary hospitals in China, and 1922 women aged 30-69 years old were recruited. Women aged 30-39 years (group A) underwent ABUS and HHUS, and women aged 40-69 (group B) underwent additional MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). All BI-RADS 4 and 5 cases were confirmed pathologically. Sensitivities and specificities of all modalities were compared.

resultsThere were 83 cancers in 677 women in group A and 321 cancers in 1245 women in group B. In the whole study population, the sensitivities of ABUS and HHUS were 92.8% (375/404) and 96.3% (389/404), and the specificities were 93.0% (1411/1518) and 89.6% (1360/1518), respectively. ABUS had a significantly higher specificity to HHUS (p < 0.01), while HHUS had higher sensitivity (p = 0.01). In group B, the sensitivities of ABUS, HHUS, and MG were 93.5% (300/321), 96.6% (310/321), and 87.9% (282/321). The specificities were 93.0% (859/924), 89.9% (831/924), and 91.6% (846/924). ABUS had significantly higher sensitivity (p = 0.02) and comparable specificity compared with MG (p = 0.14).

conclusionABUS increased sensitivity and had similar specificity compared with mammography in the diagnosis of breast cancer. Additionally, ABUS has comparable performance to HHUS in women aged 30-69 years old. ABUS or HHUS is a suitable modality for breast cancer diagnosis. KEY POINTS: • In breast cancer diagnosis settings, automated breast ultrasound has a higher cancer detection rate, sensitivity, and specificity than mammography, especially in women with dense breasts. • Compared with handheld ultrasound, automated breast ultrasound has higher specificity, lower sensitivity, and comparable diagnostic performance. • Automated breast ultrasound is a suitable modality for breast cancer diagnosis, and may have a potential indication for its further use in the breast cancer early detection.

Indexed as

Breast NeoplasmsOutpatientsAdultAgedChinaCross-Sectional StudiesFemaleHumansMammographyMiddle AgedSensitivity and SpecificityUltrasonography, MammaryBreast cancerBreast ultrasoundDiagnostic performanceMammography

Identifiers

PMID32852589
OpenAlexW3081245113

What OpenQuestion holds

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Registered trials

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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.