Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis.
Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis.
复制标题
用于乳腺癌诊断的无创多模式声对比诱导光谱系统的临床研究。
DOI：
10.1118/1.3689811
复制
发表时间：
2012
期刊：
影响因子：
3.8
通讯作者：
Liao,L
中科院分区：
文献类型：
--
作者：
Yan,K;Yu,Y;Tinney,E;Baraldi,R;Liao,L
PurposeTo提出一种非侵入性的多模态声造影诱导光谱（SCIS）系统乳腺cancerdetection.MethodsAn IRB批准的临床研究进行了评估其诊断能力。共有66例受试者入组并签署了知情同意书。研究数据分为健康乳腺组织（26），组织学证实的癌症（14）和良性肿块（26）。在每项研究期间收集漫反射光强度和低强度聚焦超声（LIFU）信号以及超声图像。利用小波技术分析了波长685和830 nm处的光强比，以比较LIFU在癌组织和非癌组织中的效果。对超声图像进行处理，获得组织纹理参数，如相关性、能量、对比度、均匀性等。采用后向逐步回归方法确定与组织类型相关的统计学显著性因素结果光信号的比较表明，LIFU在非癌组织中引起短暂的波动，但在恶性组织中不引起波动，由高频分量的平均绝对偏差（RMAD）之比量化。统计学分析显示，肿瘤与非癌性肿块的RMAD比值有显著性差异（p <0.01）。对于组织纹理参数，发现能量和相关性与组织类型在统计学上相关。使用加权因子开发癌症表征模型以区分肿瘤与良性肿块。通过改变估计癌症输出因子上限的阈值，获得灵敏度和特异性之间的权衡，从中生成受试者工作特征（ROC）曲线。使用10个建模数据集优化表征模型，并使用从数据库随机生成的另外10个验证数据集进行验证。优化结果表明，可以实现0.93的AUC。阈值为0.3，灵敏度为96.0%，特异性为84.1%，阴性预测值（NPV）为97.3%，可以achieved.ConclusionsThe的多模式系统在表征乳腺癌与良性肿块的可行性。
PurposeTo present a noninvasive multimodal sono‐contrast induced spectroscopy (SCIS) system for breast cancer detection.MethodsAn IRB approved clinical study was carried out to evaluate its diagnostic power. A total of 66 subjects were enrolled with informed consent. The study data were grouped into healthy breast tissue (26), histologically proven cancer (14), and benign mass (26). The diffuse reflectance optical intensity and low intensity focused ultrasound (LIFU) signals, as well as ultrasound images, were collected during each study. The ratio of optical intensities at wavelengths 685 and 830 nm was analyzed using wavelet technique to compare the LIFU effects in cancer and noncancerous tissues. The ultrasound images were also processed to obtain tissue texture parameters, such as correlation, energy, contrast, homogeneity, etc. Backward stepwise regression method was performed to identify the statistically significant factors correlating to tissue types (cancer vs benign mass).ResultsComparison of the optical signals showed that LIFU induced transitory fluctuation in noncancerous tissue, but not in malignant tissue, as quantified by the ratio of mean absolute deviation (RMAD) of the high frequency component. Statistical analysis revealed that the RMAD ratios were significantly different in tumor vs noncancerous masses (p≪ 0.01). For tissue texture parameters, energy and correlation were found to statistically correlate with the tissue types. A cancer characterization model was developed using the weighted factors to differentiate the tumor from the benign mass. Trade‐off between sensitivity and specificity was obtained by varying the threshold value that estimated the upper‐bound of the cancer output factor, from which the receiver‐operating characteristic (ROC) curve was generated. The characterization model was optimized using ten modeling datasets and verified using another ten validation datasets randomly generated from the database. The optimization results show that an AUC of 0.93 can be achieved. With threshold 0.3, sensitivity of 96.0%, specificity of 84.1%, and negative predictive value (NPV) of 97.3% can be achieved.ConclusionsThe feasibility of the multimodal system in characterizing breast cancer vs benign mass is established.
用于乳腺癌诊断的多模态声对比近红外光谱系统：系统设计和安全考虑
DOI：
--
发表时间：
2010
期刊：
International Conference on BioMedical Engineering and Informatics
影响因子：
--
作者：
K. Yan;Ke Huang;T. Podder;Yan Yu;L. Liao
通讯作者：
L. Liao
用于乳腺癌诊断的无创多模式声对比近红外光谱系统
DOI：
10.1109/bibe.2010.55
发表时间：
2010
期刊：
2010 IEEE International Conference on BioInformatics and BioEngineering
影响因子：
--
作者：
K. Yan;T. Podder;Ke Huang;Yan Yu;L. Liao
通讯作者：
L. Liao
用于乳腺癌检测的声对比光谱
影响因子：
5.9
作者：
Yan Yu;L. Liao
通讯作者：
L. Liao