University Breast Unit, Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany; MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, USA. Electronic address: https://twitter.com/@andrepfob.
MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, USA. Electronic address: https://twitter.com/@DrCGibbons.
Eur J Cancer. 2022 Dec;177:1-14. doi: 10.1016/j.ejca.2022.09.018. Epub 2022 Sep 28.
Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate.
We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE.
In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound.
The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems.
乳腺超声可以识别在乳腺 X 线摄影中未检测到的额外癌，但假阳性发现率较高。我们评估了使用智能多模态剪切波弹性成像（SWE）是否可以在不降低乳腺癌检出率的情况下减少不必要的活检数量。
我们使用 SWE、临床和患者信息训练、测试和验证机器学习算法，以对乳腺肿块进行分类。我们使用了 2016 年至 2019 年来自七个国家的 12 个研究地点的 857 名女性的 B 型超声、SWE 和随后的组织病理学评估数据。算法在 12 个地点中的 11 个地点的数据上进行了训练和测试，并在额外的地点数据上进行了外部验证。我们将结果与组织病理学评估进行了比较，并比较了 B 型超声、传统 SWE 和智能多模态 SWE 的诊断性能。
在外部验证集（n=285）中，智能多模态 SWE 的敏感性为 100%（95%CI，97.1-100%，126/126），特异性为 50.3%（95%CI，42.3-58.3%，80/159），曲线下面积为 0.93（95%CI，0.90-0.96）。与传统 SWE 和 B 型超声相比，诊断性能显著提高（P<0.001）。与传统 SWE 不同，智能多模态 SWE 的阳性预测值明显高于 B 型超声。与 B 型超声相比，智能多模态 SWE 减少了 50.3%（79 例与 159 例，P<0.001）的不必要活检，而不会遗漏癌症。
通过使用智能多模态 SWE，可以安全地避免大多数不必要的乳腺活检。这些结果可能有助于减轻患者、提供者和医疗保健系统的诊断负担。
