title: Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis.
abstract: In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.
authors.affiliation: Radboud University Nijmegen Medical Centre, Department of Radiology, 6500 HB, Nijmegen. p.vos@rad.umcn.nl
authors.forename: P C
authors.initials: PC
authors.lastname: Vos
authors.forename: J O
authors.initials: JO
authors.lastname: Barentsz
authors.forename: N
authors.initials: N
authors.lastname: Karssemeijer
authors.forename: H J
authors.initials: HJ
authors.lastname: Huisman
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis; D000368:Aged; D000465:Algorithms; D001331:Automation; D001706:Biopsy; D015331:Cohort Studies; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012372:ROC Curve