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.

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.