Prostate Cancer Detection Using Composite Impedance Metric.

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM) - positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. To this end, an electrical impedance acquisition system with a microendoscopic probe was employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A classifier trained using CIM data can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p<0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train a support vector machine classifier with a radial basis function kernel. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues.

IEEE transactions on medical imaging. 2016 Jun 09 [Epub ahead of print]

Shadab Khan, Aditya Mahara, Elias Hyams, Alan Schned, Ryan Halter

E-Newsletters

Newsletter subscription

Free Daily and Weekly newsletters offered by content of interest

The fields of GU Oncology and Urology are rapidly advancing. Sign up today for articles, videos, conference highlights and abstracts from peer-review publications by disease and condition delivered to your inbox and read on the go.

Subscribe