Computer-aided detection of prostate cancer.
BACKGROUND
Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in developed countries. Detection of prostate carcinoma at an early stage is crucial for successful treatment.


METHODS
A method for the analysis of transrectal ultrasound images aimed at computer-aided diagnosis of prostate cancer is tested in this paper. First, two classifiers based on k-nearest neighbors and Hidden Markov models are compared. Second, the diagnostic capacity of our system is tested by means of a set of experiments where humans with varying degrees of experience classified a set of ultrasound images with and without the aid of the computer-aided system. The corpus used in this study was specifically acquired for this purpose. It consists of 4944 ultrasound images corresponding to 303 patients, and is publicly available for non-commercial use upon request.


RESULTS
The best classification results achieve an area under the receiver operating characteristic curve of 61.6%. However, the diagnostic capacity of an expert urologist using the computer-aided system improves only slightly compared with his/her capacity without the aid of the system.


CONCLUSIONS
Despite the difficulty of this task, the obtained results indicate that discrimination between cancerous and non-cancerous tissue is possible to a certain degree. The computer-aided system helps an inexperienced user to make a better diagnosis, however it must be able to perform better in order to be useful in a real-world clinical context.
Llobet
Rafael
R
Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovatión, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. rllobet@iti.upv.es
Pérez-Cortés
Juan C
JC
Toselli
Alejandro H
AH
Juan
Alfons
A
D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D013030:Spain