Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI.
Publication year
2008Source
Medical Physics, 35, 3, (2008), pp. 888-99ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
Radiology
Pathology
Journal title
Medical Physics
Volume
vol. 35
Issue
iss. 3
Page start
p. 888
Page end
p. 99
Subject
NCMLS 4: Energy and redox metabolism; ONCOL 3: Translational research; ONCOL 5: Aetiology, screening and detection; UMCN 1.1: Functional Imaging; UMCN 1.2: Molecular diagnosis, prognosis and monitoringAbstract
A novel automated computerized scheme has been developed for determining a likelihood measure of malignancy for cancer suspicious regions in the prostate based on dynamic contrast-enhanced magnetic resonance imaging (MRI) (DCE-MRI) images. Our database consisted of 34 consecutive patients with histologically proven adenocarcinoma in the peripheral zone of the prostate. Both carcinoma and non-malignant tissue were annotated in consensus on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. The annotations were used as regions of interest (ROIs). A feature set comprising pharmacokinetic parameters and a T1 estimate was extracted from the ROIs to train a support vector machine as classifier. The output of the classifier was used as a measure of likelihood of malignancy. Diagnostic performance of the scheme was evaluated using the area under the ROC curve. The diagnostic accuracy obtained for differentiating prostate cancer from non-malignant disorders in the peripheral zone was 0.83 (0.75-0.92). This suggests that it is feasible to develop a computer aided diagnosis system capable of characterizing prostate cancer in the peripheral zone based on DCE-MRI.
This item appears in the following Collection(s)
- Academic publications [238430]
- Electronic publications [122512]
- Faculty of Medical Sciences [90359]
- Open Access publications [97507]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.