Skin Electrical Resistance as a Diagnostic and Therapeutic Biomarker of Breast Cancer Measuring Lymphatic Regions.
Skin Electrical Resistance as a Diagnostic and Therapeutic Biomarker of Breast Cancer Measuring Lymphatic Regions.
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DOI：
10.1109/access.2021.3123569
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sanchez B
中科院分区：
文献类型：
--
作者：
Andreasen N;Crandall H;Brimhall O;Miller B;Perez-Tamayo J;Martinsen OG;Kauwe SK;Sanchez B
由于乳腺癌的存在，与间质基质和淋巴系统改变相关的皮肤变化可能提供显著和可测量的影响。本研究旨在确定皮肤电阻变化是否可以作为与恶性与良性乳腺癌病变患者的生理变化相关的诊断和治疗生物标志物。48名妇女（24名恶性肿瘤，23名良性病变）参加了这项研究。在同一疗程内和首次测量后1周，在乳房淋巴区和非乳房淋巴区重复进行皮肤电阻测量。计算组内相关系数以确定该技术的会话内和会话间再现性。然后将数据标准化为比较乳腺恶性和良性病变之间横截面差异的平均值。对6例接受治疗的患者的6个月纵向数据进行分析，以检测治疗效果。使用标准描述性统计量比较组间的比率差异。皮肤电阻数据用于训练机器学习随机森林分类算法以诊断乳腺癌病变。乳腺良、恶性病变治疗前后比较差异有显著性（P<0.01），治疗前后比较差异有显著性（P<0.05）。诊断算法证明了对乳腺癌进行分类的能力，曲线下面积为0.68，灵敏度为66.3%，特异性为78.5%，阳性预测值为70.7%，阴性预测值为75.1%。乳腺癌患者皮肤电阻的测定可作为乳腺癌筛查和治疗评价的一种简便方法。进一步的工作是必要的，以改善我们的方法，并进一步研究导致所观察到的变化的生物物理机制。
Skin changes associated with alterations in the interstitial matrix and lymph system might provide significant and measurable effects due to the presence of breast cancer. This study aimed to determine if skin electrical resistance changes could serve as a diagnostic and therapeutic biomarker associated with physiological changes in patients with malignant versus benign breast cancer lesions. Forty-eight women (24 with malignant cancer, 23 with benign lesions) were enrolled in this study. Repeated skin resistance measurements were performed within the same session and 1 week after the first measurement in the breast lymphatic region and non-breast lymphathic regions. Intraclass correlation coefficients were calculated to determine the technique’s intrasession and intersession reproducibility. Data were then normalized as a mean of comparing cross-sectional differences between malignant and benign lesions of the breast. Six months longitudinal data from six patients that received therapy were analyzed to detect the effect of therapy. Standard descriptive statistics were used to compare ratiometric differences between groups. Skin resistance data were used to train a machine learning random forest classification algorithm to diagnose breast cancer lesions. Significant differences between malignant and benign breast lesions were obtained (p<0.01), also pre- and post-treatment (p<0.05). The diagnostic algorithm demonstrated the capability to classify breast cancer with an area under the curve of 0.68, sensitivity of 66.3%, specificity of 78.5%, positive predictive value 70.7% and negative predictive value 75.1%. Measurement of skin resistance in patients with breast cancer may serve as a convenient screening tool for breast cancer and evaluation of therapy. Further work is warranted to improve our approach and further investigate the biophysical mechanisms leading to the observed changes.
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DOI：
10.1109/tbme.2021.3063724
发表时间：
2021-10
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Cardoner MMM;Kwon H;Pulido HVG;Nagy J;Rutkove S;Sanchez B
通讯作者：
Sanchez B
影响因子：
3.4
作者：
Grimnes, Sverre;Martinsen, Orjan G.
通讯作者：
Martinsen, Orjan G.
影响因子：
3.2
作者：
Haemmerich, D;Staelin, ST;Webster, JG
通讯作者：
Webster, JG
影响因子：
39.2
作者：
Hubbard RA;Kerlikowske K;Flowers CI;Yankaskas BC;Zhu W;Miglioretti DL
通讯作者：
Miglioretti DL
影响因子：
2.2
作者：
Martinsen, Orjan G.;Grimnes, Sverre;Haug, Erlend
通讯作者：
Haug, Erlend