Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI

Multidisciplinary functional MR imaging for prostate cancer

Various functional magnetic resonance (MR) imaging techniques are used for evaluating prostate cancer including diffusion-weighted imaging, dynamic contrast-enhanced MR imaging, and MR spectroscopy. These techniques provide unique information that is helpful to differentiate prostate cancer from non-cancerous tissue and have been proven to improve the diagnostic performance of MRI not only for cancer detection, but also for staging, post-treatment monitoring, and guiding prostate biopsies. However, each functional MR imaging technique also has inherent challenges. Therefore, in order to make accurate diagnoses, it is important to comprehensively understand their advantages and limitations, histologic background related with image findings, and their clinical relevance for evaluating prostate cancer. This article will review the basic principles and clinical significance of functional MR imaging for evaluating prostate cancer.

Technologies for localization and diagnosis of prostate cancer

The gold standard for detecting prostate cancer (PCa), systematic biopsy, lacks sensitivity as well as grading accuracy. PSA screening leads to over-treatment of many men, and it is unclear whether screening reduces PCa mortality. This review provides an understanding of the difficulties of localizing and diagnosing PCa. It summarizes recent developments of ultrasound (including elastography) and MRI, and discusses some alternative experimental techniques, such as resonance sensor technology and vibrational spectroscopy. A comparison between the different methods is presented. It is concluded that new ultrasound techniques are promising for targeted biopsy procedures, in order to detect more clinically significant cancers while reducing the number of cores. MRI advances are very promising, but MRI remains expensive and MR-guided biopsy is complex. Resonance sensor technology and vibrational spectroscopy have shown promising results in vitro. There is a need for large prospective multicentre trials that unambiguously prove the clinical benefits of these new techniques.

Dynamic contrast-enhanced magnetic resonance imaging in prostate cancer

Prostate cancer remains a major health concern for the male population. During the past decade, a dramatic increase in prostate-specific antigen and transurethral resection of the prostate has resulted in increased detection rate of small lesions and increased incidence of this disease. Needle biopsies in asymptomatic men have also contributed to the increased incidence of prostate cancer, leading to an increasing incidence-to-mortality ratio. Magnetic resonance imaging (MRI) is the modality of choice in prostate cancer patients with increased prostate-specific antigen levels before or after prostate cancer diagnosis confirmed by biopsy and T2-weighted imaging (T2W) has been used as a standard technique in detection. During the last decade, dynamic contrast-enhanced MRI has emerged as one of the main techniques used in multiparametric MRI of the prostate gland in cancer patients. Dynamic contrast-enhanced MRI has been routinely used for detection and diagnosis of the tumor, for staging and monitoring the therapeutic response, as well as for guiding targeted biopsies in patients with suspected prostate cancer or with a negative biopsy result. In this article, we are going to review the analysis techniques of dynamic contrast-enhanced MRI and its various clinical applications in prostate cancer patients.

Thirty-two-channel coil 3T magnetic resonance-guided biopsies of prostate tumor suspicious regions identified on multimodality 3T magnetic resonance imaging: technique and feasibility

OBJECTIVES

To test the technique and feasibility of translating tumor suspicious region maps in the prostate, obtained by multimodality, anatomic, and functional 3T magnetic resonance imaging (MRI) data to 32-channel coil, T2-weighted (T2-w), 3T MR images, for directing MR-guided biopsies. Furthermore, to evaluate the practicability of MR-guided biopsy on a 3T MR scanner using a 32-channel coil and a MR-compatible biopsy device.

MATERIALS AND METHODS

Twenty-one patients with a high prostate-specific antigen (>4.0 ng/mL) and at least 2 prior negative transrectal ultrasound-guided biopsies of the prostate underwent an endorectal coil 3T MRI, which included T2-w, diffusion weighted and dynamic contrast enhanced MRI. From these multimodality images, tumor suspicious regions (TSR) were determined. The 3D localization of these TSRs within the prostatic gland was translated to the T2-w MR images of a subsequent 32-channel coil 3T MRI. These were then biopsied under 3T MR guidance.

RESULTS

In all patients, TSRs could be identified and accurately translated to subsequent 3T MR images and biopsied under MR guidance. Median MR biopsy procedure time was 35 minutes. Of the 21 patients, 8 (38%) were diagnosed with prostate cancer, 6 (29%) had evidence of prostatitis, 6 (29%) had combined inflammatory and atrophic changes, and only 1 (5%) patient had no identifiable pathology.

CONCLUSIONS

Multimodality, 3T MRI determined TSRs could effectively be translated to T2-weighted images, to be used for MR biopsies. 3T MR-guided biopsy based on these translated TSRs was feasible, performed in a clinical useful time, and resulted in a high number of positive results.

Automated calibration for computerized analysis of prostate lesions using pharmacokinetic magnetic resonance images

The feasibility of an automated calibration method for estimating the arterial input function when calculating pharmacokinetic parameters from Dynamic Contrast Enhanced MRI is shown. In a previous study, it was demonstrated that the computer aided diagnoses (CADx) system performs optimal when per patient calibration was used, but required manual annotation of reference tissue. In this study we propose a fully automated segmentation method that tackles this limitation and tested the method with our CADx system when discriminating prostate cancer from benign areas in the peripheral zone. A method was developed to automatically segment normal peripheral zone tissue (PZ). Context based segmentation using the Otsu histogram based threshold selection method and by Hessian based blob detection, was developed to automatically select PZ as reference tissue for the per patient calibration. In 38 consecutive patients carcinoma, benign and normal tissue were annotated on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. A feature set comprising pharmacokinetic parameters was computed for each ROI and used to train a support vector machine (SVM) as classifier. In total 42 malignant, 29 benign and 37 normal regions were annotated. The diagnostic accuracy obtained for differentiating malignant from benign lesions using a conventional general patient plasma profile showed an accuracy of 0.65 (0.54-0.76). Using the automated segmentation per patient calibration method the diagnostic value improved to 0.80 (0.71-0.88), whereas the manual segmentation per patient calibration showed a diagnostic performance of 0.80 (0.70-0.90). These results show that an automated per-patient calibration is feasible, a significant better discriminating performance compared to the conventional fixed calibration was obtained and the diagnostic accuracy is similar to using manual per-patient calibration.

A comprehensive segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI

Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%.