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多光谱光声断层扫描中血氧饱和度在卵巢癌诊断中的作用。

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi-spectral photoacoustic tomography.

机构信息

Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.

Department of Electrical and System Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.

出版信息

J Biophotonics. 2021 Apr;14(4):e202000368. doi: 10.1002/jbio.202000368. Epub 2021 Jan 6.

Abstract

In photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO ). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi-pixel method to smooth the measurements before calculating rHbT and %sO . We first perform phantom studies using blood tubes of calibrated %sO to evaluate the accuracy of our %sO estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co-registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90-0.96) on the testing data set was achieved by combining mean %sO , a spectral feature, and the score of the study radiologist.

摘要

在光声断层扫描(PAT)中,通常使用可调谐激光器在多个波长下照射组织,然后利用接收到的光声波来形成相对总血红蛋白(rHbT)和血氧饱和度(%sO )的功能图像。由于测量误差,这些参数的估计可能具有挑战性,特别是在临床研究中。在这项研究中,我们使用多像素方法在计算 rHbT 和 %sO 之前对测量值进行平滑处理。我们首先使用经过校准 %sO 的血试管进行了体模研究,以评估我们的 %sO 估计的准确性。最后,我们展示了通过我们的共注册 PAT 和超声系统对 33 名患有 51 个卵巢肿块的患者进行的 PAT 诊断结果。这些卵巢肿块分为恶性和良性/正常组。使用来自这两组所有卵巢的 PAT 数据计算了 rHbT 和 %sO 的功能图及其直方图以及光谱特征。使用不同的显著特征组合在支持向量机模型上进行了训练。在测试数据集上,通过结合平均 %sO 、光谱特征和研究放射科医生的评分,实现了 0.93 的 ROC(AUC)(0.95%CI:0.90-0.96)。

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