Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study.
This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo.


METHODS
We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient.


RESULTS
In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework.


CONCLUSIONS
Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.
Farhad
F
Imani
Purang
P
Abolmaesumi
Eli
E
Gibson
Amir
A
Khojaste
Mena
M
Gaed
Madeleine
M
Moussa
Jose A
JA
Gomez
Cesare
C
Romagnoli
Michael
M
Leveridge
Silvia
S
Chang
D Robert
DR
Siemens
Aaron
A
Fenster
Aaron D
AD
Ward
Parvin
P
Mousavi
D019540:Area Under Curve; D005240:Feasibility Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D011467:Prostate / Q000000981:diagnostic imaging*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D014463:Ultrasonography