Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States of America.
McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, United States of America.
Physiol Meas. 2024 May 3;45(5). doi: 10.1088/1361-6579/ad3d26.
Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising.In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs).We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape.In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.
皮肤癌是全球诊断出的癌症中最大的一类，也是最可治疗的癌症之一。然而，这取决于早期诊断和正确的皮肤癌类型区分。目前，准确、快速和非侵入性的早期检测方法有限。然而，文献表明良性和恶性皮肤癌之间以及不同类型的皮肤癌之间的阻抗差异，表明基于阻抗区分的方法可能很有前途。在这项工作中，我们提出了一种利用基于差异的电阻抗断层成像（EIT）和石墨烯电子纹身（GET）技术的快速、非侵入性皮肤癌诊断新方法。我们使用计算数值和实验皮肤模型来证明这种首创系统的可行性。我们考虑了皮肤癌病变阻抗、大小、形状和相对于电极的位置的变化，并评估了使用单个和多电极 GET（mGET）阵列的影响。结果表明，该方法有可能基于病变阻抗、大小和位置进行区分，但需要额外的技术来确定形状。通过这种方式，这项工作中提出的结合了 EIT 和 GET 技术的系统，有望成为一种完全非侵入性和快速的皮肤癌诊断方法。
