title: Ultrasound-based characterization of prostate cancer: an in vivo clinical feasibility study.
abstract: This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series.
  
  
  METHODS
  The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm x 1.7 mm.
  
  
  RESULTS
  In a leave-one-patient-out cross-validation strategy, an average classification accuracy of 76% and the area under ROC curve of 0.83 are achieved using two proposed RF time series features. The results statistically significantly outperform those achieved by previously reported features in the literature. The proposed features show the clinical relevance of RF time series for in vivo characterization of cancer.
authors.affiliation: Queen's University, Kingston, Ontario, Canada.
authors.forename: Farhad
authors.initials: F
authors.lastname: Imani
authors.affiliation: The University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Purang
authors.initials: P
authors.lastname: Abolmaesumi
authors.affiliation: University of Western Ontario, London, Ontario, Canada.
authors.forename: Eli
authors.initials: E
authors.lastname: Gibson
authors.affiliation: Queen's University, Kingston, Ontario, Canada.
authors.forename: Amir Khojaste
authors.initials: AK
authors.lastname: Galesh-Khale
authors.affiliation: University of Western Ontario, London, Ontario, Canada.
authors.forename: Mena
authors.initials: M
authors.lastname: Gaed
authors.affiliation: London Health Science Centre, London, Ontario, Canada.
authors.forename: Madeleine
authors.initials: M
authors.lastname: Moussa
authors.affiliation: London Health Science Centre, London, Ontario, Canada.
authors.forename: Jose A
authors.initials: JA
authors.lastname: Gomez
authors.affiliation: University of Western Ontario, London, Ontario, Canada.
authors.forename: Cesare
authors.initials: C
authors.lastname: Romagnoli
authors.affiliation: Kingston General Hospital, Kingston, Ontario, Canada.
authors.forename: D Robert
authors.initials: DR
authors.lastname: Siemens
authors.affiliation: Kingston General Hospital, Kingston, Ontario, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Leviridge
authors.affiliation: Vancouver General Hospital, Vancouver, British Columbia, Canada.
authors.forename: Silvia
authors.initials: S
authors.lastname: Chang
authors.affiliation: University of Western Ontario, London, Ontario, Canada.
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
authors.affiliation: University of Western Ontario, London, Ontario, Canada.
authors.forename: Aaron D
authors.initials: AD
authors.lastname: Ward
authors.affiliation: Queen's University, Kingston, Ontario, Canada.
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005240:Feasibility Studies; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014463:Ultrasonography / Q000379:methods*