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提高手持式乳腺超声检查中病变定位的可重复性。

Improving Lesion Location Reproducibility in Handheld Breast Ultrasound.

作者信息

Chiu James, Bova Davide, Spear Georgia, Ecanow Jacob, Choate Alyssa, Besson Pierre, Caluser Calin

机构信息

Department of Radiology, Endeavor Health, 2650 Ridge Ave, Evanston, IL 60201, USA.

Dacia Medical Clinic, 917 S Oak Park Ave, Suite B, Oak Park, IL 60304, USA.

出版信息

Diagnostics (Basel). 2024 Jul 25;14(15):1602. doi: 10.3390/diagnostics14151602.

DOI:10.3390/diagnostics14151602
PMID:39125478
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11311286/
Abstract

Interoperator variability in the reproducibility of breast lesions found by handheld ultrasound (HHUS) can significantly interfere with clinical care. This study analyzed the features associated with breast mass position differences during HHUS. The ability of operators to reproduce the position of small masses and the time required to generate annotations with and without a computer-assisted scanning device (DEVICE) were also evaluated. This prospective study included 28 patients with 34 benign or probably benign small breast masses. Two operators generated manual and automated position annotations for each mass. The probe and body positions were systematically varied during scanning with the DEVICE, and the features describing mass movement were used in three logistic regression models trained to discriminate small from large breast mass displacements (cutoff: 10 mm). All models successfully discriminated small from large breast mass displacements (areas under the curve: 0.78 to 0.82). The interoperator localization precision was 6.6 ± 2.8 mm with DEVICE guidance and 19.9 ± 16.1 mm with manual annotations. Computer-assisted scanning reduced the time to annotate and reidentify a mass by 33 and 46 s on average, respectively. The results demonstrated that breast mass location reproducibility and exam efficiency improved by controlling operator actionable features with computer-assisted HHUS.

摘要

手持式超声(HHUS)检查发现的乳腺病变再现性方面的操作者间差异会显著干扰临床护理。本研究分析了HHUS检查期间与乳腺肿块位置差异相关的特征。还评估了操作者再现小肿块位置的能力以及使用和不使用计算机辅助扫描设备(DEVICE)生成标注所需的时间。这项前瞻性研究纳入了28例患者,其乳房有34个良性或可能为良性的小肿块。两名操作者为每个肿块生成手动和自动位置标注。在使用DEVICE扫描期间,探头和身体位置系统地变化,描述肿块移动的特征被用于三个逻辑回归模型,这些模型经过训练以区分小乳腺肿块和大乳腺肿块的位移(临界值:10毫米)。所有模型均成功区分了小乳腺肿块和大乳腺肿块的位移(曲线下面积:0.78至0.82)。在DEVICE引导下,操作者间的定位精度为6.6±2.8毫米,手动标注时为19.9±16.1毫米。计算机辅助扫描平均分别将标注和重新识别肿块的时间减少了33秒和46秒。结果表明,通过计算机辅助HHUS控制操作者可操作的特征,可提高乳腺肿块位置的再现性和检查效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/b50074ac0b6d/diagnostics-14-01602-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/126f0550a484/diagnostics-14-01602-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/7f81f32a260c/diagnostics-14-01602-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/a7b4fb86ae56/diagnostics-14-01602-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/236fc7cd9bd4/diagnostics-14-01602-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/5b8ae87e0fd2/diagnostics-14-01602-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/5e136c013240/diagnostics-14-01602-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/849aa6c179c3/diagnostics-14-01602-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/dd4268a6d550/diagnostics-14-01602-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/b50074ac0b6d/diagnostics-14-01602-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/126f0550a484/diagnostics-14-01602-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/7f81f32a260c/diagnostics-14-01602-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/a7b4fb86ae56/diagnostics-14-01602-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/236fc7cd9bd4/diagnostics-14-01602-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/5b8ae87e0fd2/diagnostics-14-01602-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/5e136c013240/diagnostics-14-01602-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/849aa6c179c3/diagnostics-14-01602-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/dd4268a6d550/diagnostics-14-01602-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a5/11311286/b50074ac0b6d/diagnostics-14-01602-g006.jpg

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本文引用的文献

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Artificial Intelligence for Breast US.人工智能在乳腺超声中的应用。
J Breast Imaging. 2023 Feb 6;5(1):11-20. doi: 10.1093/jbi/wbac077.
2
Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy.新辅助化疗后乳腺癌患者乳腺超声和乳腺钼靶检查与术后组织病理学结果对比的准确性
Diagnostics (Basel). 2023 Aug 30;13(17):2811. doi: 10.3390/diagnostics13172811.
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The concordance in lesion detection and characteristics between the Anatomical Intelligence and conventional breast ultrasound Scan method.
解剖智能与传统乳腺超声扫描方法在病灶检测和特征方面的一致性。
BMC Med Imaging. 2021 Jun 21;21(1):102. doi: 10.1186/s12880-021-00628-x.
4
Accurate Estimation of Breast Tumor Size: A Comparison Between Ultrasonography, Mammography, Magnetic Resonance Imaging, and Associated Contributing Factors.乳腺肿瘤大小的准确估计:超声、乳腺X线摄影、磁共振成像及相关影响因素的比较
Eur J Breast Health. 2020 Dec 24;17(1):53-61. doi: 10.4274/ejbh.2020.5888. eCollection 2021 Jan.
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In vivo estimation of target registration errors during augmented reality laparoscopic surgery.在增强现实腹腔镜手术中对目标配准误差的体内估计。
Int J Comput Assist Radiol Surg. 2018 Jun;13(6):865-874. doi: 10.1007/s11548-018-1761-3. Epub 2018 Apr 16.
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The Role of Ultrasound in Screening Dense Breasts-A Review of the Literature and Practical Solutions for Implementation.超声在致密型乳腺筛查中的作用——文献综述与实施的实际解决方案
Diagnostics (Basel). 2018 Mar 16;8(1):20. doi: 10.3390/diagnostics8010020.
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A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.可靠性研究中组内相关系数选择与报告指南
J Chiropr Med. 2016 Jun;15(2):155-63. doi: 10.1016/j.jcm.2016.02.012. Epub 2016 Mar 31.
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Clinical Evaluation of a 3-D Automatic Annotation Method for Breast Ultrasound Imaging.乳腺超声成像三维自动标注方法的临床评估
Ultrasound Med Biol. 2016 Apr;42(4):870-81. doi: 10.1016/j.ultrasmedbio.2015.11.028. Epub 2015 Dec 24.
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Supine breast US: how to correlate breast lesions from prone MRI.仰卧位乳腺超声检查:如何将俯卧位磁共振成像中的乳腺病变进行关联。
Br J Radiol. 2016;89(1059):20150497. doi: 10.1259/bjr.20150497. Epub 2015 Dec 21.
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Ultrasound is at least as good as magnetic resonance imaging in predicting tumour size post-neoadjuvant chemotherapy in breast cancer.在预测乳腺癌新辅助化疗后的肿瘤大小方面,超声至少与磁共振成像一样有效。
Eur J Cancer. 2016 Jan;52:67-76. doi: 10.1016/j.ejca.2015.10.010. Epub 2015 Nov 30.