Veil Carina, Krauß Franziska, Amend Bastian, Fend Falko, Sawodny Oliver
Institute for System Dynamics, University of Stuttgart, Waldburgstr. 19, 70563, Stuttgart, Germany.
Department of Urology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
Sci Rep. 2025 Jan 4;15(1):825. doi: 10.1038/s41598-024-84844-9.
Including sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove. Over the years of research, it became obvious that electrical impedance spectroscopy is a very promising tool for tissue differentiation, but also a very sensitive one. While differentiation in preliminary studies shows great potential, challenges arise when transferring this concept to real, intraoperative conditions, mainly due to the influence of preoperative radiotherapy, possibly different tumor types, and mechanical tissue deformations due to peristalsis or unsteady contact force of the sensor. This work proposes a patient-based classification approach that evaluates the distance of an unknown measurement to a healthy reference of the same patient, essentially a relative classification of the difference in impedance that is robust against inter-individual differences and systematic errors. A diversified dataset covering multiple disturbance scenarios is recorded. Two alternatives to define features from the impedance data are investigated, namely using measurement points and model-based parameters. Based on the distance of the feature vector of a unknown measurement to a healthy reference, a Gaussian process classifier is trained. The approach achieves a high classification accuracy of up to 100% on noise-free impedance data recorded under controlled conditions. Even when the differentiation is more ambiguous due to external disturbances, the presented approach still achieves a classification accuracy of 80%. These results are a starting point to tackle intraoperative bladder tissue characterization and decrease the recurrence rate.
在医学干预中纳入传感器信息旨在通过术中对组织进行表征，以支持外科医生决定后续的行动步骤。对于膀胱癌来说，一个重要问题是由于未能切除整个肿瘤而导致肿瘤复发。阻抗测量有助于对膀胱组织进行分类，并为外科医生提供关于切除多少组织的指示。经过多年研究，很明显电阻抗光谱法是一种非常有前途的组织分化工具，但也是一种非常敏感的工具。虽然初步研究中的分化显示出巨大潜力，但将这一概念应用于实际术中情况时会出现挑战，主要是由于术前放疗的影响、可能不同的肿瘤类型以及由于蠕动或传感器不稳定接触力导致的机械组织变形。这项工作提出了一种基于患者的分类方法，该方法评估未知测量值与同一患者健康参考值之间的距离，本质上是对阻抗差异的相对分类，对个体差异和系统误差具有鲁棒性。记录了一个涵盖多种干扰场景的多样化数据集。研究了从阻抗数据定义特征的两种替代方法，即使用测量点和基于模型的参数。基于未知测量值的特征向量与健康参考值之间的距离，训练了一个高斯过程分类器。该方法在受控条件下记录的无噪声阻抗数据上实现了高达100%的高分类准确率。即使由于外部干扰导致分化更加模糊，所提出的方法仍能达到80%的分类准确率。这些结果是解决术中膀胱组织表征问题并降低复发率的一个起点。
