Developing a quantitative ultrasound breast scanner for identifying early response of breast cancer to chemotherapy

项目总结 在这项计划中，我们将在Qt超声波®乳腺断层扫描仪上开发新的图像模式，并 证明采用新图像模式的扫描仪可以准确地识别乳腺癌的反应 患者接受化疗。乳腺癌治疗中满足不了的临床需求是提供廉价和 在治疗过程中及早确定患者对化疗反应的准确方法。对许多乳房来说 癌症，患者对初始治疗缺乏反应预示着不良的结果，而病理完成 反应与延长生存期密切相关。传统的临床替代物的反应基于 身体评估、乳房X光检查和标准临床超声等解剖信息提供 对治疗反应的早期评估很差。我们证明了超声的定量分析 使用传统手持式临床扫描仪的背向散射(QUSB)可以提供有希望的响应指标 在治疗开始后一周内，乳腺癌患者接受新辅助化疗(NAC/AC)。这些QUSB 结果将大大受益于体积精度的提高，这是传统扫描平台所不能做到的 支持，例如信号中的传输和衰减损耗。 我们建议通过将QUSB与新上市的Qt超声波®乳房集成来解决这些问题 扫描仪。这款FDA批准并上市的扫描仪可以提供包括声音在内的乳房3D定量图像 速度、(SOS)反射率(R)和衰减(A)值。Qt超声波®乳房扫描仪矫正屈光 和界面衰减损耗。它还提供了多个视角的组合，这将进一步改进 QUSB变量。扫描仪的SOS、R、A和mm~3体积测量精度参数提供 补充量化特征可能有助于早期识别反应的稳健性。 我们相信，QUSB QT超声®乳房扫描仪可以在一定程度上比MRI更好地识别反应。 即使响应识别仅具有可比性，QUSB QT超声波®也可以节省成本、易于使用、 无创自然对比度和患者接受度将显著简化乳腺癌的治疗 并提供相当大的实用优势和更高的准确性。因此，我们的科学前提是 集成在Qt超声波®乳房扫描仪上的QUSB将改进对早期 乳腺癌患者对NAC的反应。我们的初步数据表明QUSB技术可以识别 无反应者，并预测患者的结果。我们的初步结果还表明，QUSB技术 与Qt超声波®平台集成，并从改进的QUSB偏差和差异中受益。因此， 该提案包括三个目标。第一个具体目标是实现、测试和验证QUSB技术 最新的QT超声波®乳房扫描仪，用于临床数据采集。第二个具体目标是量化 QUSB QT超声波®定量数据识别无响应者的能力。最终的具体目标是 量化QUSB QT超声®定量数据的容量，以预测患者结果。

PROJECT SUMMARY In this proposal, we will develop new images modes on the QT Ultrasound® tomographic breast scanner and demonstrate that the scanner with the new image modes can accurately identify the response of breast cancer patients to chemotherapy. A poorly met clinical need in breast cancer therapy is providing inexpensive and accurate ways to identify patient responses to chemotherapy early during the course of therapy. For many breast cancers, lack of patient response to initial therapy is predictive of poor outcome, whereas pathological complete response strongly correlates with extended survival. Conventional clinical surrogates of response based on anatomical information such as physical assessments, mammography and standard clinical ultrasound provide poor early assessments of treatment response. We demonstrated that quantitative analysis of ultrasound backscatter (QUSB) using conventional hand-held clinical scanners can provide promising metrics of response of breast cancer to neoadjuvant chemotherapy (NAC/AC) within one week of therapy initiation. These QUSB results would benefit greatly from improved volumetric accuracy that conventional scanning platforms do not support, such as transmission and attenuation losses in the signals. We propose to solve these issues by integrating QUSB with the newly available QT Ultrasound® breast scanner. This FDA cleared and marketed scanner delivers 3D quantitative images of the breast including sound speed, (SOS) reflectivity (R) and attenuation (A) values. The QT Ultrasound® breast scanner corrects refractive and interface attenuation losses. It also provides compounding of multiple angles of view that will further improve QUSB variance. The scanner’s parameters of SOS, R, A, and mm3 volume measuring accuracy provide supplementary quantitative features likely to contribute robustness to early identification of response. We believe that the QUSB+QT Ultrasound® breast scanner can identify response at a level better than MRI. Even if response identification is only comparable, QUSB+QT Ultrasound® savings in costs, ease of use, noninvasive native contrast, and patient acceptance would markedly simplify management of breast cancer therapy and deliver considerable practical advantage and increased accuracy. Therefore, our scientific premise is that QUSB integrated on the QT Ultrasound® breast scanner will provide improved identification of early response of breast cancer patients to NAC. Our preliminary data demonstrate that QUSB techniques can identify nonresponders and predict patient outcomes. Our preliminary results also demonstrate that QUSB techniques integrate with the QT Ultrasound® platform and benefit from improved QUSB bias and variance. Therefore, the proposal consists of three aims. The first specific aim is to implement, test and validate QUSB techniques on the latest QT Ultrasound® breast scanner for clinical data acquisition. The second specific aim is to quantify the capacity of QUSB+QT Ultrasound® quantitative data to identify nonresponders. The final specific aim is to quantify the capacity of QUSB+QT Ultrasound® quantitative data to predict patient outcomes.