OBJECTIVE. This article is a primer on the technical aspects of performing a high-quality dynamic contrast-enhanced MRI (DCE-MRI) examination of the prostate gland. CONCLUSION. DCE-MRI is emerging ...

OBJECTIVE. The purpose of this article is to evaluate the relationship between apparent diffusion coefficient (ADC) values, tumor volume, and total Gleason grade in patients with prostate cancer before radical prostatectomy.MATERIALS AND METHODS. A total of 110 patients with prostate cancer who had undergone endorectal prostate MRI at 1.5 T before radical prostatectomy were included. ADC values were derived by drawing a region of interest on the histologically confirmed tumors. Tumor volume was obtained by manual segmentation on T2-weighted images (T2WIs) and ADC maps. The relationship between the ADC value or tumor volume and the Gleason grade was assessed by using multivariate mixed linear and effect models. Multivariate analysis was performed to evaluate the accuracy of ADC and tumor volume in determining the aggressiveness of prostate cancer.RESULTS. A total of 197 tumors were studied; 128 (65%) tumors were found in the peripheral zone and 69 (35%) were found in the central gland. The ADC value was fo...

Urinary bladder cancer is a heterogeneous disease with a variety of pathologic features, cytogenetic characteristics, and natural histories. It is the fourth most common cancer in males and the tenth most common cancer in females. Urinary bladder cancer has a high recurrence rate, necessitating long-term surveillance after initial therapy. Early detection is important, since up to 47% of bladder cancer-related deaths may have been avoided. Conventional computed tomography (CT) and magnetic resonance (MR) imaging are only moderately accurate in the diagnosis and local staging of bladder cancer, with cystoscopy and pathologic staging remaining the standards of reference. However, the role of newer MR imaging sequences (eg, diffusion-weighted imaging) in the diagnosis and local staging of bladder cancer is still evolving. Substantial advances in MR imaging technology have made multiparametric MR imaging a feasible and reasonably accurate technique for the local staging of bladder cancer to optimize treatment. In addition, whole-body CT is the primary imaging technique for the detection of metastases in bladder cancer patients, especially those with disease that invades muscle.

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

The important role of magnetic resonance imaging (MRI) in the anatomic evaluation, detection, and staging of prostate cancer is well established. This paper focuses on the pertinent embryologic, anatomic, and imaging facts regarding both the normal prostate and the several examples of prostate cancers as well as staging implications. The discussion primarily includes findings related to T2-weighted imaging as opposed to the other functional sequences, including diffusion weighted imaging (DWI) or dynamic contrast enhanced MRI and MR spectroscopic imaging, respectively.