Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA.
Ultrasound Med Biol. 2023 Jan;49(1):256-268. doi: 10.1016/j.ultrasmedbio.2022.08.018. Epub 2022 Nov 1.
Traditional breast ultrasound imaging is a low-cost, real-time and portable method to assist with breast cancer screening and diagnosis, with particular benefits for patients with dense breast tissue. We previously demonstrated that incorporating coherence-based beamforming additionally improves the distinction of fluid-filled from solid breast masses, based on qualitative image interpretation by board-certified radiologists. However, variable sensitivity (range: 0.71-1.00 when detecting fluid-filled masses) was achieved by the individual radiologist readers. Therefore, we propose two objective coherence metrics, lag-one coherence (LOC) and coherence length (CL), to quantitatively determine the content of breast masses without requiring reader assessment. Data acquired from 31 breast masses were analyzed. Ideal separation (i.e., 1.00 sensitivity and specificity) was achieved between fluid-filled and solid breast masses based on the mean or median LOC value within each mass. When separated based on mean and median CL values, the sensitivity/specificity decreased to 1.00/0.95 and 0.92/0.89, respectively. The greatest sensitivity and specificity were achieved in dense, rather than non-dense, breast tissue. These results support the introduction of an objective, reader-independent method for automated diagnoses of cystic breast masses.
传统的乳腺超声成像是一种低成本、实时、便携的方法，可用于乳腺癌筛查和诊断，尤其对乳腺组织致密的患者有益。我们之前的研究表明，基于基于相干的波束形成技术可以提高充满液体的与实体乳腺肿块之间的区分能力，这是由经过委员会认证的放射科医生进行定性图像解释得出的。然而，个别放射科医生读者的检测灵敏度（检测充满液体的肿块时的范围为 0.71-1.00）各不相同。因此，我们提出了两种客观的相干度量标准，即滞后一相干度（LOC）和相干长度（CL），以在不要求读者评估的情况下定量确定乳腺肿块的内容。分析了 31 个乳腺肿块的数据。基于每个肿块内的 LOC 均值或中位数，可以在充满液体和实体乳腺肿块之间实现理想的分离（即，1.00 的灵敏度和特异性）。当基于均值和中位数 CL 值进行分离时，灵敏度/特异性降低至 1.00/0.95 和 0.92/0.89。在致密的乳腺组织中，而不是非致密的乳腺组织中，获得了最大的灵敏度和特异性。这些结果支持引入一种客观的、与读者无关的方法，用于自动诊断囊性乳腺肿块。
