The four-electrode method was used to measure the ex vivo complex electrical impedance of tissues from 14 hepatic tumors and the surrounding normal liver from six patients. Measurements were done in the frequency range 1-400 kHz. It was found that the conductivity of the tumor tissue was much higher than that of the normal liver tissue in this frequency range (from 0.14 +/- 0.06 S m(-1) versus 0.03 +/- 0.01 S m(-1) at 1 kHz to 0.25 +/- 0.06 S m(-1) versus 0.15 +/- 0.03 S m(-1) at 400 kHz). The Cole-Cole models were estimated from the experimental data and the four parameters (rho(0), rho(infinity), alpha, f(c)) were obtained using a least-squares fit algorithm. The Cole-Cole parameters for the cancerous and normal liver are 9 +/- 4 Omega m(-1), 2.2 +/- 0.7 Omega m(-1), 0.5 +/- 0.2, 140 +/- 103 kHz and 50 +/- 28 Omega m(-1), 3.2 +/- 0.6 Omega m(-1), 0.64 +/- 0.04, 10 +/- 7 kHz, respectively. These data can contribute to developing bioelectric applications for tissue diagnostics and in tissue treatment planning with electrical fields such as radiofrequency tissue ablation, electrochemotherapy and gene therapy with reversible electroporation, nanoscale pulsing and irreversible electroporation.
Background
The study aim was to evaluate validity evidence using idle time as a performance measure in open surgical skills assessment.
Methods
This pilot study tested psychomotor planning skills of surgical attendings (N=6), residents (N=4) and medical students (N=5) during suturing tasks of varying difficulty. Performance data were collected with a motion tracking system. Participants’ hand movements were analyzed for idle time, total operative time and path length. We hypothesized that there will be shorter idle times for more experienced individuals and on the easier tasks.
Results
A total of 365 idle periods were identified across all participants. Attendings had fewer idle periods during three specific procedure steps (p < .001). All participants had longer idle time on friable tissue (p < .005).
Conclusion
Using an experimental model, idle time was found to correlate with experience and motor planning when operating on increasingly difficult tissue types. Further work exploring idle time as a valid psychomotor measure is warranted.
This feasibility study introduces the use of a classifier based on electrical spectroscopy measurements for breast cancer tissue characterization. The classifier is of the support vector machine type, and the vector of data is made of electrical voltage measurements at 12 discrete electrical excitation frequencies over the beta dispersion range of the analyzed tissue and at discrete locations selected from information produced by conventional medical imaging. The database was generated through a mathematical simulation model. The performance of the classifier was evaluated through a test of its ability to distinguish between simulations of malignant and benign tissues in the breast. The results demonstrate the feasibility of the concept and illustrate the tissue characterization ability of this classifier.
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