Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Biomedical Informatics and Computational Biology, University of Minnesota Rochester, Rochester, Minnesota, USA.
Sci Rep. 2019 Apr 5;9(1):5737. doi: 10.1038/s41598-019-41885-9.
Benign and malignant tumors differ in the viscoelastic properties of their cellular microenvironments and in their spatiotemporal response to very low frequency stimuli. These differences can introduce a unique viscoelastic biomarker in differentiation of benign and malignant tumors. This biomarker may reduce the number of unnecessary biopsies in breast patients. Although different methods have been developed so far for this purpose, none of them have focused on in vivo and in situ assessment of local viscoelastic properties in the ultra-low (sub-Hertz) frequency range. Here we introduce a new, noninvasive model-free method called Loss Angle Mapping (LAM). We assessed the performance results on 156 breast patients. The method was further improved by detection of out-of-plane motion using motion compensation cross correlation method (MCCC). 45 patients met this MCCC criterion and were considered for data analysis. Among this population, we found 77.8% sensitivity and 96.3% specificity (p < 0.0001) in discriminating between benign and malignant tumors using logistic regression method regarding the pre known information about the BIRADS number and size. The accuracy and area under the ROC curve, AUC, was 88.9% and 0.94, respectively. This method opens new avenues to investigate the mechanobiology behavior of different tissues in a frequency range that has not yet been explored in any in vivo patient studies.
良性和恶性肿瘤在细胞微环境的黏弹性特性及其对极低频率刺激的时空响应方面存在差异。这些差异可以在良性和恶性肿瘤的鉴别中引入独特的黏弹性生物标志物。该生物标志物可能会减少乳腺患者中不必要的活检数量。尽管迄今为止已经开发出了不同的方法,但它们都没有集中在亚赫兹超低频率范围内对局部黏弹性性质进行体内和原位评估。在这里,我们引入了一种新的、非侵入性的无模型方法,称为损耗角映射(LAM)。我们评估了对 156 名乳腺患者的性能结果。通过使用运动补偿互相关方法(MCCC)检测面外运动,进一步改进了该方法。有 45 名患者符合该 MCCC 标准,并考虑用于数据分析。在这群人中,我们发现使用逻辑回归方法根据 BIRADS 编号和大小的已知信息,在区分良性和恶性肿瘤方面具有 77.8%的灵敏度和 96.3%的特异性(p<0.0001)。该方法的准确性和 ROC 曲线下面积,AUC,分别为 88.9%和 0.94。该方法为研究不同组织在任何体内患者研究中尚未探索过的频率范围内的机械生物学行为开辟了新途径。