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. MATERIAL AND 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.
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. MATERIAL AND 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.
Authors: Shekoofeh Azizi; Sharareh Bayat; Pingkun Yan; Amir Tahmasebi; Guy Nir; Jin Tae Kwak; Sheng Xu; Storey Wilson; Kenneth A Iczkowski; M Scott Lucia; Larry Goldenberg; Septimiu E Salcudean; Peter A Pinto; Bradford Wood; Purang Abolmaesumi; Parvin Mousavi Journal: Int J Comput Assist Radiol Surg Date: 2017-06-20 Impact factor: 2.924
Authors: Shekoofeh Azizi; Nathan Van Woudenberg; Samira Sojoudi; Ming Li; Sheng Xu; Emran M Abu Anas; Pingkun Yan; Amir Tahmasebi; Jin Tae Kwak; Baris Turkbey; Peter Choyke; Peter Pinto; Bradford Wood; Parvin Mousavi; Purang Abolmaesumi Journal: Int J Comput Assist Radiol Surg Date: 2018-03-27 Impact factor: 2.924