Electrical impedance-based tissue classification for bladder tumor differentiation.

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.