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Predictive potential of photoacoustic spectroscopy in breast tumor detection based on xenograft serum profiles

机译:基于异种移植血清概况的光声光谱技术在乳腺肿瘤检测中的预测潜力

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Breast cancer is the second most common cancer all over the world. Heterogeneity in breast cancer makes it a difficult task to detect with the existing serum markers at an early stage. With an aim to detect the disease early at the pre-malignant level, MCF-7 cells xenografts were developed using female nude mice and blood serum were extracted on days 0th, 10th, 15th & 20th post tumor cells injection (N=12 for each time point). Photoacoustic spectra were recorded on the serum samples at 281nm pulsed laser excitations. A total of 144 time domain spectra were recorded from 48 serum samples belonging to 4 different time points. These spectra were then converted into frequency domain (0-1250kHz) using MATLAB algorithms. Subsequently, seven features (mean, median, mode, variance, standard deviation, area under the curve & spectral residuals after 10th degree polynomial fit) were extracted from them and used for PCA. Further, using the first three Principal components (PCs) of the data, Linear Discriminate Analysis has been carried out. The performance of the analysis showed an accuracy of 82.64% in predicting the samples belonging to various time points under study. Further, region wise analysis of the frequency-domain data showed 95 - 203.13 kHz region most suitable for the discrimination among the 4 time points under study. The analysis provided a clear discrimination in most of the spectral features under study suggesting that the photoacoustic technique has the potential to be a diagnostic tool for early detection of breast tumor development.
机译:乳腺癌是全世界第二大最常见的癌症。乳腺癌的异质性使得在早期阶段很难利用现有的血清标志物进行检测。为了在恶变前早期及早发现疾病,使用雌性裸鼠开发了MCF-7细胞异种移植物,并在注射肿瘤细胞后第0、10、15和20天提取了血清(每次N = 12)时间点)。在281nm脉冲激光激发下在血清样品上记录光声光谱。从属于4个不同时间点的48个血清样品中总共记录了144个时域光谱。然后使用MATLAB算法将这些频谱转换为频域(0-1250kHz)。随后,从中提取七个特征(均值,中位数,众数,方差,标准差,曲线下面积和10次多项式拟合后的光谱残差),并将其用于PCA。此外,使用数据的前三个主成分(PC),进行了线性判别分析。分析的性能表明,在预测属于研究中各个时间点的样本时,准确性为82.64%。此外,对频域数据的区域分析显示,最适合在研究的四个时间点之间进行区分的95-203.13 kHz区域。该分析对正在研究的大多数光谱特征提供了明确的区分,表明光声技术有可能成为早期发现乳腺肿瘤发展的诊断工具。

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