title: A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging.
abstract: Multiparametric (mp)-Magnetic Resonance Imaging (MRI) is emerging as a powerful test to diagnose and stage prostate cancer (PCa). However, its interpretation is a time consuming and complex feat requiring dedicated radiologists. Computer-aided diagnosis (CAD) tools could allow better integration of data deriving from the different MRI sequences in order to obtain accurate, reproducible, non-operator dependent information useful to identify and stage PCa. In this paper, we present a fully automatic CAD system conceived as a 2-stage process. First, a malignancy probability map for all voxels within the prostate is created. Then, a candidate segmentation step is performed to highlight suspected areas, thus evaluating both the sensitivity and the number of false positive (FP) regions detected by the system. Training and testing of the CAD scheme is performed using whole-mount histological sections as the reference standard. On a cohort of 56 patients (i.e. 65 lesions) the area under the ROC curve obtained during the voxel-wise step was 0.91, while, in the second step, a per-patient sensitivity of 97% was reached, with a median number of FP equal to 3 in the whole prostate. The system here proposed could be potentially used as first or second reader to manage patients suspected to have PCa, thus reducing both the radiologist's reporting time and the inter-reader variability. As an innovative setup, it could also be used to help the radiologist in setting the MRI-guided biopsy target.
authors.affiliation: Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy. Electronic address: valentina.giannini@ircc.it.
authors.forename: Valentina
authors.initials: V
authors.lastname: Giannini
authors.affiliation: Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy.
authors.forename: Simone
authors.initials: S
authors.lastname: Mazzetti
authors.affiliation: Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy.
authors.forename: Anna
authors.initials: A
authors.lastname: Vignati
authors.affiliation: Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy.
authors.forename: Filippo
authors.initials: F
authors.lastname: Russo
authors.affiliation: Division of Pathology at the University of Turin, San Luigi Hospital, Regione Gonzole, 10, 10043 Orbassano, Italy.
authors.forename: Enrico
authors.initials: E
authors.lastname: Bollito
authors.affiliation: Division of Urology and Department of Oncology at the University of Turin, San Luigi Hospital, Regione Gonzole, 10, 10043 Orbassano, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Porpiglia
authors.affiliation: Department of Medical Physics at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Stasi
authors.affiliation: Department of Radiology at Candiolo Cancer Institute-FPO, IRCCS, Strada Provinciale 142 km 3.95, 10060 Candiolo, Italy.
authors.forename: Daniele
authors.initials: D
authors.lastname: Regge
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Computer aided detection; Image analysis; Multiparametric MRI; Prostate cancer; SVM classifier