Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
Department of Dermatology, Mackay Memorial Hospital, Taipei, Taiwan.
PLoS One. 2014 Apr 4;9(4):e93647. doi: 10.1371/journal.pone.0093647. eCollection 2014.
Similar clinical appearances prevent accurate diagnosis of two common skin diseases, clavus and verruca. In this study, electrical impedance is employed as a novel tool to generate a predictive model for differentiating these two diseases.
We used 29 clavus and 28 verruca lesions. To obtain impedance parameters, a LCR-meter system was applied to measure capacitance (C), resistance (Re), impedance magnitude (Z), and phase angle (θ). These values were combined with lesion thickness (d) to characterize the tissue specimens. The results from clavus and verruca were then fitted to a univariate logistic regression model with the generalized estimating equations (GEE) method. In model generation, log ZSD and θSD were formulated as predictors by fitting a multiple logistic regression model with the same GEE method. The potential nonlinear effects of covariates were detected by fitting generalized additive models (GAM). Moreover, the model was validated by the goodness-of-fit (GOF) assessments.
Significant mean differences of the index d, Re, Z, and θ are found between clavus and verruca (p<0.001). A final predictive model is established with Z and θ indices. The model fits the observed data quite well. In GOF evaluation, the area under the receiver operating characteristics (ROC) curve is 0.875 (>0.7), the adjusted generalized R2 is 0.512 (>0.3), and the p value of the Hosmer-Lemeshow GOF test is 0.350 (>0.05).
This technique promises to provide an approved model for differential diagnosis of clavus and verruca. It could provide a rapid, relatively low-cost, safe and non-invasive screening tool in clinic use.
相似的临床外观使得两种常见皮肤疾病——胼胝和疣——难以准确诊断。本研究采用电阻抗作为一种新的工具，生成一种用于区分这两种疾病的预测模型。
我们使用了 29 个胼胝和 28 个疣病变。为了获得阻抗参数，我们使用了 LCR 仪表系统来测量电容 (C)、电阻 (Re)、阻抗幅度 (Z) 和相位角 (θ)。这些值与病变厚度 (d) 结合起来，以对组织标本进行特征描述。然后，使用广义估计方程 (GEE) 方法，将胼胝和疣的结果拟合到一个单变量逻辑回归模型中。在模型生成中，通过使用相同的 GEE 方法拟合多变量逻辑回归模型，将 log ZSD 和 θSD 公式化为预测因子。通过拟合广义加性模型 (GAM) 来检测协变量的潜在非线性效应。此外，还通过拟合优度 (GOF) 评估来验证模型。
在胼胝和疣之间，发现指数 d、Re、Z 和 θ 的均值差异具有统计学意义 (p<0.001)。最终建立了一个包含 Z 和 θ 指数的预测模型。该模型很好地拟合了观测数据。在 GOF 评估中，接受者操作特征 (ROC) 曲线下的面积为 0.875(>0.7)，调整后的广义 R2 为 0.512(>0.3)，Hosmer-Lemeshow GOF 检验的 p 值为 0.350(>0.05)。
该技术有望为胼胝和疣的鉴别诊断提供一种经过验证的模型。它可以为临床应用提供一种快速、相对低成本、安全且非侵入性的筛查工具。
