title: Comparative study of separation between ex vivo prostatic malignant and benign tissue using electrical impedance spectroscopy and electrical impedance tomography.
abstract: OBJECTIVE
  Currently no efficient and reliable technique exists to routinely assess surgical margins during a radical prostatectomy. Electrical impedance spectroscopy (EIS) has been reported as a potential technique to provide surgeons with real-time intraoperative margin assessment. In addition to providing a quantified measure of margin status, a co-registered electrical impedance tomography (EIT) image presented on a surgeon's workstation could add value to the margin assessment process.
  
  
  METHODS
  To investigate this, we conducted a comparative study between EIS and EIT to evaluate the potential these technologies might have for margin assessment. EIS and EIT data was acquired from ex vivo human prostates using a multi-electrode endoscopic impedance acquisition probe.
  
  
  RESULTS
  EIS and EIT show good predictive performance with a 0.76 and 0.80 area-under-curve (AUC), respectively, when considering discrete frequencies only. A machine learning (ML) algorithm is implemented to combine features, which improves the AUCs of EIS and EIT to 0.84 and 0.85, respectively. Single-step EIT takes significantly less time to reconstruct than multi-step EIT, yet provides similarly accurate classification results, making the single-step approach a potential candidate for real-time margin assessment. While the ML-based approach clearly exhibits benefits as compared to the single feature assessment, the decision to use EIS versus EIT is unclear since each approach performs better for different subsets of tissue classifications.
  
  
  CONCLUSIONS
  The results presented in this paper corroborate our previous studies and present the strongest evidence yet that an intraoperative-capable impedance probe can be used to distinguish benign from malignant prostate tissues. An in vivo study with a large cohort will be necessary to definitively determine the preferred approach and to show the clinical effectiveness of using this technology for margin assessment.
authors.affiliation: Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, United States of America.
authors.forename: Ethan K
authors.initials: EK
authors.lastname: Murphy
authors.forename: Aditya
authors.initials: A
authors.lastname: Mahara
authors.forename: Shadab
authors.initials: S
authors.lastname: Khan
authors.forename: Elias S
authors.initials: ES
authors.lastname: Hyams
authors.forename: Alan R
authors.initials: AR
authors.lastname: Schned
authors.forename: Jason
authors.initials: J
authors.lastname: Pettus
authors.forename: Ryan J
authors.initials: RJ
authors.lastname: Halter
mesh_terms: D058266:Dielectric Spectroscopy; D017097:Electric Impedance; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008297:Male; D011467:Prostate / Q000166:cytology* / Q000000981:diagnostic imaging / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D014054:Tomography