Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided detection system which consists of two stages. In the first stage, we detect initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection. The second stage segments the candidate regions and using classification we obtain cancer likelihoods for each candidate. Features represent pharmacokinetic behavior, symmetry and appearance, among others. The system is evaluated on a large consecutive cohort of 347 patients with MR-guided biopsy as the reference standard. This set contained 165 patients with cancer and 182 patients without prostate cancer. Performance evaluation is based on lesion-based free-response receiver operating characteristic curve and patient-based receiver operating characteristic analysis. The system is also compared to the prospective clinical performance of radiologists. Results show a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case. In clinical workflow the system could potentially be used to improve the sensitivity of the radiologist. At the high specificity reading setting, which is typical in screening situations, the system does not perform significantly different from the radiologist and could be used as an independent second reader instead of a second radiologist. Furthermore, the system has potential in a first-reader setting.

Zonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that can differentiate between the two zones: anatomical, intensity and texture. It is evaluated against a multi-parametric multi-atlas based method using 48 multi-parametric MRI studies. Three observers are used to assess inter-observer variability and we compare our results against the state of the art from literature. Results show a mean Dice coefficient of 0.89 ± 0.03 for the central gland and 0.75 ± 0.07 for the peripheral zone, compared to 0.87 ± 0.04 and 0.76 ± 0.06 in literature. Summarizing, a pattern recognition approach incorporating anatomy, intensity and texture has been shown to give good results in zonal segmentation of the prostate.

In patients with PCa, both molecular imaging techniques, MRL and (11)C-choline PET/CT, can detect LNs suspicious for metastasis, irrespective of the existing size and shape criteria for CT and conventional magnetic resonance imaging. On MRL and PET/CT, 61% and 31% of the suspicious LNs were located outside the conventional clinical target volume. Therefore, these techniques could help to individualize treatment selection and enable image-guided radiotherapy for patients with PCa LN metastases.

Multiparametric magnetic resonance imaging (MRI) has the potential of being the ideal prostate cancer (PCa) assessment tool. Information gathered with multiparametric MRI can serve therapy choice, guidance of interventions, and treatments. The purpose of this review is to discuss the potential role of multiparametric MRI in focal therapy with respect to patient selection and directing (robot‐guided) biopsies and intensity‐modulated radiation therapy (IMRT). Multiparametric MRI is a versatile and promising technique. It appears to be the best available imaging technique at the moment in localizing, staging (primary as well as recurrent disease, and local as well as distant disease), determining aggressiveness, and volume of PCa. However, larger study populations in multicenter settings have to confirm these promising results. However, before such studies can be performed more research is needed in order to achieve standardized imaging protocols. J. Magn. Reson. Imaging 2012;35:20‐31. © 2011 Wiley Periodicals, Inc.

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