Evidence map›Paper›PMID 36582786›Full record

ArticleFrontiers in oncology2022

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions.

Boyuan Xing, Xiangyi Chen, Yalin Wang, Shuang Li, Ying-Kui Liang, Dawei Wang

Abstract read
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Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Clinical Application of Artificial Intelligence in Breast Ultrasound.Journal of the Korean Society of Radiology · 2025
    Review
  4. Article
  5. Article
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Boyuan XingDepartment of Ultrasound Imaging, The People's Hospital of China Three Gorges University/the First People's Hospital of Yichang, Yichang, Hubei, China.
Xiangyi ChenDepartment of Nuclear Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yalin WangDepartment of Medical Engineering, Medical Supplies Center of PLA General Hospital, Beijing, China.
Shuang LiDepartment of Pathology, The People's Hospital of China Three Gorges University/the First People's Hospital of Yichang, Yichang, Hubei, China.
Ying-Kui LiangDepartment of Nuclear Medicine, The Sixth Medical Center of People's Liberation Army General Hospital, Beijing, China.
Dawei WangDepartment of Medical Engineering, Medical Supplies Center of PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: S-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists' assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation. Methods: The US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two. Results: There were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group ( Conclusions: The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.

Indexed as

BI-RADSbreast cancerbreast imaging reporting and data systemS-detectultrasound

Identifiers

PMID36582786
PMCPMC9792476

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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.