Purpose:To develop a multi-parametric model suitable for prospectively identifying prostate cancer in peripheral zone (PZ) using magnetic resonance imaging (MRI). Materials and Methods:Twenty-five radical prostatectomy patients (median age, 63 years; range, 44 -72 years) had T2-weighted, diffusion-weighted imaging (DWI), T2-mapping, and dynamic contrast-enhanced (DCE) MRI at 1.5 Tesla (T) with endorectal coil to yield parameters apparent diffusion coefficient (ADC), T2, volume transfer constant (K trans ) and extravascular extracellular volume fraction (v e ). Whole-mount histology was generated from surgical specimens and PZ tumors delineated. Thirty-eight tumor outlines, one per tumor, and pathologically normal PZ regions were transferred to MR images. Receiver operating characteristic (ROC) curves were generated using all identified normal and tumor voxels.Step-wise logistic-regression modeling was performed, testing changes in deviance for significance. Areas under the ROC curves (A z ) were used to evaluate and compare performance. .719], which was significantly higher than A z,T2 , A z,Ktrans , and A z,ve (P Ͻ 0.002). A z,LR-3p tended to be greater than A z,ADC , however, this result was not statistically significant (P ϭ 0.090). Conclusion:Using logistic regression, an objective model capable of mapping PZ tumor with reasonable performance can be constructed.

MR imaging-derived parameters measured in the prostate were significantly related to the proportion of specific histologic components that differ between normal and malignant PZ tissue. These relationships may help define imaging-related histologic prognostic parameters for prostate cancer.

Sparse prostate tumors have similar ADC and T2 values to those of normal PZ tissue. This may limit MR imaging detection and the assessment of tumor volume of some cancers.

Prostate cancer is one of the leading causes of death from cancer among men in the United States. Currently, high-resolution magnetic resonance imaging (MRI) has been shown to have higher accuracy than trans-rectal ultrasound (TRUS) when used to ascertain the presence of prostate cancer. As MRI can provide both morphological and functional images for a tissue of interest, some researchers are exploring the uses of multispectral MRI to guide prostate biopsies and radiation therapy. However, success with prostate cancer localization based on current imaging methods has been limited due to overlap in feature space of benign and malignant tissues using any one MRI method and the interobserver variability. In this paper, we present a new unsupervised segmentation method for prostate cancer detection, using fuzzy Markov random fields (fuzzy MRFs) for the segmentation of multispectral MR prostate images. Typically, both hard and fuzzy MRF models have two groups of parameters to be estimated: the MRF parameters and class parameters for each pixel in the image. To date, these two parameters have been treated separately, and estimated in an alternating fashion. In this paper, we develop a new method to estimate the parameters defining the Markovian distribution of the measured data, while performing the data clustering simultaneously. We perform computer simulations on synthetic test images and multispectral MR prostate datasets to demonstrate the efficacy and efficiency of the proposed method and also provide a comparison with some of the commonly used methods.

The automated methods presented here can help diagnose and detect prostate cancer, and improve segmentation results. For that purpose, multispectral MRI provides better information about cancer and normal regions in the prostate when compared to methods that use single MRI techniques; thus, the different MRI measurements provide complementary information in the automated methods. Moreover, the use of supervised algorithms in such automated methods remain a good alternative to the use of unsupervised algorithms.

No abstract

Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.

After calf muscle exercise, no statistically significant differences in T2* relaxation times or arterial spin labeling signal, indicative of differences in muscle oxygenation and perfusion status, were found between patients with chronic exertional compartment syndrome and control subjects.