Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI

G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman

Annual Meeting of the Radiological Society of North America (2011)

Abstract

PURPOSE Interpretation of multi-parametric MRI findings in the peripheral zone (PZ) or the transition zone (TZ) of the prostate is different. Therefore, this study investigates the performance of zone-specific computer-aided detection (CAD) as opposed to whole-prostate CAD. METHOD AND MATERIALS 117 consecutive prostate MRI?s from 2009 were extracted from our database. 71/117 MRI?s showed no malignant findings, 26/117 patients had a PZ tumor, 20/117 a TZ tumor. The MRI?s were acquired on a 3T MR scanner (Siemens Trio Tim, Erlangen, Germany) and included T2-weighted images (T2WI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion-weighted images (DWI). From DCE-MRI and DWI pharmacokinetic parameters (PK) and ADC maps were calculated respectively. Lesion locations were indicated by an expert radiologist. Histology was obtained using MR-guided biopsy or prostatectomy. A two-stage classification strategy was used. The prostate was segmented using an atlas based method including PZ and TZ. First stage voxel classification resulted in a likelihood map, in which local maxima were detected. Then, a region was segmented for each local maximum. Second stage classification resulted in a malignancy likelihood per region. Voxel features used were the T2WI intensities, PK and ADC values and blob detection values for T2WI, ADC and PK images. For the second stage 25th- and 75th-percentiles within the segmented regions were calculated for all voxel features including the initial likelihood map. Classification in both stages was performed using a whole-prostate classifier or two separate zone-specific classifiers. The first stage used linear discriminant classifiers, the second stage support vector machine classifiers. Validation was performed in a leave-one-patient-out manner. FROC calculation and statistical analysis were performed using the JAFROC software package. The figure-of-merit (FOM) used is the area under the alternative FROC (AFROC) curve. RESULTS Zone-specific CAD was significantly better than whole-prostate CAD (FOM 0.63 vs. 0.48, p < 0.05). At 0.1, 1.0 and 3.0 false positives per patient the sensitivity of the zone-specific system was 0.23, 0.5 and 0.87 compared to 0.05, 0.22 and 0.47. CONCLUSION A zone-specific CAD system has significantly higher performance than a whole-prostate CAD system. CLINICAL RELEVANCE/APPLICATION CAD can help the radiologist read prostate MRI and might reduce oversight and perception errors in both PZ and TZ.