Use of multiparametric MRI to characterize uterine fibroid tissue types.

BACKGROUND: Although the biological characteristics of uterine fibroids (UF) have implications for therapy choice and effectiveness, there is limited MRI data about these characteristics. Currently, the Funaki classification and Scaled Signal Intensity (SSI) are used to predict treatment outcome but both screening-tools appear to be suboptimal. Therefore, multiparametric and quantitative MRI was studied to evaluate various biological characteristics of UF. METHODS: 87 patients with UF underwent an MRI-examination. Differences between UF tissues and myometrium were investigated using T2-mapping, Apparent Diffusion Coefficient (ADC) maps with different b-value combinations, contrast-enhanced T1-weighted and T2-weighted imaging. Additionally, the Funaki classification and SSI were calculated. RESULTS: Significant differences between myometrium and UF tissue in T2-mapping (p = 0.001), long-TE ADC low b-values (p = 0.002), ADC all b-values (p < 0.001) and high b-values (p < 0.001) were found. Significant differences between Funaki type 3 versus type 1 and 2 were observed in SSI (p < 0.001) and T2-values (p < 0.001). Significant correlations were found between SSI and T2-mapping (p < 0.001; ρs = 0.82), ADC all b-values (p = 0.004; ρs = 0.31), ADC high b-values (p < 0.001; ρs = 0.44) and long-TE ADC low b-values (p = 0.004; ρs = 0.31). CONCLUSIONS: Quantitative MR-data allowed us to distinguish UF tissue from myometrium and to discriminate different UF tissue types and may, therefore, be a useful tool to predict treatment outcome/determine optimal treatment modality.

The Prognostic PDE4D7 Score in a Diagnostic Biopsy Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes.

Purpose. To further validate the prognostic power of the biomarker PDE4D7, we investigated the correlation of PDE4D7 scores adjusted for presurgical clinical variables with longitudinal postsurgical biological outcomes. Methods. RNA was extracted from biopsy punches of resected tumors (550 patients; RP cohort) and diagnostic needle biopsies (168 patients; DB cohort). Cox regression and survival were applied to correlate PDE4D7 scores with patient outcomes. Logistic regression was used to combine the clinical CAPRA score with PDE4D7. Results. In univariate analysis, the PDE4D7 score was significantly associated with PSA recurrence after prostatectomy in both studied patient cohorts' analysis (HR 0.53; 95% CI 0.41-0.67; p<1.0E-04 and HR 0.47; 95% CI 0.33-0.65; p<1.0E-04, respectively). After adjustment for the presurgical clinical variables preoperative PSA, PSA density, biopsy Gleason, clinical stage, percentage tumor in the biopsy (data only available for RP cohort), and percentage of positive biopsies, the HR was 0.49 (95% CI 0.38-0.64; p<1.0E-04) and 0.43 (95% CI 0.29-0.63; p<1.0E-04), respectively. The addition of the PDE4D7 to the clinical CAPRA score increased the AUC by 5% over the CAPRA score alone (0.82 versus 0.77; p=0.004). This combination model stratified 14.6% patients of the DB cohort to no risk of biochemical relapse (NPV 100%) over a follow-up period of up to 15 years. Conclusions. The PDE4D7 score provides independent risk information for pretreatment risk stratification. Combining CAPRA with PDE4D7 scores significantly improved the clinical risk stratification before surgery.

Computed tomography perfusion evaluation after extracranial-intracranial bypass surgery.

OBJECTIVE: Perfusion imaging is increasingly used for postoperative evaluation of extracranial to intracranial (EC-IC) bypass surgery. Altered hemodynamics and delayed arrival of the contrast agent in the area fed by the bypass can influence perfusion measurement. We compared perfusion asymmetry obtained with different algorithms in EC-IC bypass surgery patients. METHODS: We retrospectively identified all patients evaluated with computed tomography perfusion (CTP) between May 2007 and May 2011 after EC-IC bypass surgery at our institution. CTP images were analyzed with three perfusion algorithms that differ among their ability to anticipate for delayed arrival time of contrast material: the delay-sensitive first-moment mean transit time (fMTT), the semi-delay-sensitive standard singular value decomposition (sSVD) and the delay-insensitive block-circulant SVD (bSVD). The interhemispheric difference in bolus arrival time (ΔBAT) was determined to confirm altered hemodynamics. Interhemispheric asymmetry in perfusion values (mean transit time (MTT) difference, cerebral blood flow (CBF) ratio and cerebral blood volume (CBV) ratio) was compared between the three algorithms. Presence of a new infarct in the treated hemisphere was evaluated on follow-up imaging and perfusion asymmetry was compared between patients with and without infarction. RESULTS: Twenty-two patients were included. The median interhemispheric difference in ΔBAT was 0.98 s. The median MTT difference was significantly smaller when calculated with the delay-insensitive algorithm than with the other algorithms (0.44 s versus 0.90 s and 0.93 s, p<0.01). The CBF ratio was similar for all algorithms (111.98 versus 112.59 and 112.60). The CBV ratio was similar for all algorithms (113.20 versus 111.95 and 113.97). There was a significant difference in MTT asymmetry between patients with and without infarction with the delay-insensitive algorithm only (1.57 s versus 0.38 s, p=0.04). CONCLUSION: In patients with EC-IC bypass surgery, delay-sensitive algorithms showed larger MTT asymmetry than delay-insensitive algorithms. Furthermore, only the delay-insensitive method seems to differentiate between patients with and without infarction on follow-up.

Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance.

PURPOSE: To determine the effect of computer-aided diagnosis (CAD) on less-experienced and experienced observer performance in differentiation of benign from malignant prostate lesions at 3-T multiparametric magnetic resonance (MR) imaging. MATERIALS AND METHODS: The institutional review board waived the need for informed consent. Retrospectively, 34 patients were included who had prostate cancer and had undergone multiparametric MR imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced MR imaging prior to radical prostatectomy. Six radiologists less experienced in prostate imaging and four radiologists experienced in prostate imaging were asked to characterize different regions suspicious for cancer as benign or malignant on multiparametric MR images first without and subsequently with CAD software. The effect of CAD was analyzed by using a multiple-reader, multicase, receiver operating characteristic analysis and a linear mixed-model analysis. RESULTS: In 34 patients, 206 preannotated regions, including 67 malignant and 64 benign regions in the peripheral zone (PZ) and 19 malignant and 56 benign regions in the transition zone (TZ), were evaluated. Stand-alone CAD had an overall area under the receiver operating characteristic curve (AUC) of 0.90. For PZ and TZ lesions, the AUCs were 0.92 and 0.87, respectively. Without CAD, less-experienced observers had an overall AUC of 0.81, which significantly increased to 0.91 (P = .001) with CAD. For experienced observers, the AUC without CAD was 0.88, which increased to 0.91 (P = .17) with CAD. For PZ lesions, less-experienced observers increased their AUC from 0.86 to 0.95 (P < .001) with CAD. Experienced observers showed an increase from 0.91 to 0.93 (P = .13). For TZ lesions, less-experienced observers significantly increased their performance from 0.72 to 0.79 (P = .01) with CAD and experienced observers increased their performance from 0.81 to 0.82 (P = .42). CONCLUSION: Addition of CAD significantly improved the performance of less-experienced observers in distinguishing benign from malignant lesions; when less-experienced observers used CAD, they reached similar performance as experienced observers. The stand-alone performance of CAD was similar to performance of experienced observers.

Prostate cancer aggressiveness: in vivo assessment of MR spectroscopy and diffusion-weighted imaging at 3 T.

PURPOSE: To determine the individual and combined performance of magnetic resonance (MR) spectroscopic imaging and diffusion-weighted (DW) imaging at 3 T in the in vivo assessment of prostate cancer aggressiveness by using histopathologically defined regions of interest on radical prostatectomy specimens to define the prostate cancer regions to be investigated. MATERIALS AND METHODS: The local institutional ethics review board approved this retrospective study and waived the informed consent requirement. Fifty-four patients with biopsy-proved prostate cancer underwent clinical MR spectroscopic imaging followed by prostatectomy. Guided by the histopathologic map, all spectroscopy voxels that contained tumor tissue were selected, and metabolite ratios (choline [Cho] plus creatine [Cr]-to-citrate [Cit] and Cho/Cr ratios) were derived. For each spectroscopic voxel, 25th percentile apparent diffusion coefficient (ADC) of the region corresponding to that voxel was determined, representing the most aberrant tumor part on the ADC map, which was often smaller than spectroscopic imaging voxels. Maximum metabolic ratios and minimum 25th percentile ADC of each tumor were related to tumor aggressiveness and were used to differentiate aggressiveness classes. A logistic regression model (LRM) was used to combine data from both modalities. RESULTS: Significant correlation was found between aggressiveness classes and maximum Cho+Cr/Cit ratio (ρ=0.36), maximum Cho/Cr ratio (ρ=0.35), and minimum 25th percentile ADC (ρ=-0.63) in the peripheral zone (PZ). In the transition zone (TZ), the correlation was significant for only Cho+Cr/Cit and Cho/Cr ratios (ρ=0.58 and ρ=0.60, respectively). For differentiation between aggressiveness classes, LRM use did not result in significantly improved differentiation over any individual variables. CONCLUSION: These findings enabled confirmation that MR spectroscopic imaging and DW imaging offer potential for in vivo noninvasive assessment of prostate cancer aggressiveness, and both modalities have comparable performance. The combination did not result in better performance. Nonetheless, the better performances of metabolite ratios in the TZ and of ADCs in the PZ suggest that they have complementary value.

Predictive value of MRI in the localization, staging, volume estimation, assessment of aggressiveness, and guidance of radiotherapy and biopsies in prostate cancer.

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.

Prostate cancer: multiparametric MR imaging for detection, localization, and staging.

This review presents the current state of the art regarding multiparametric magnetic resonance (MR) imaging of prostate cancer. Technical requirements and clinical indications for the use of multiparametric MR imaging in detection, localization, characterization, staging, biopsy guidance, and active surveillance of prostate cancer are discussed. Although reported accuracies of the separate and combined multiparametric MR imaging techniques vary for diverse clinical prostate cancer indications, multiparametric MR imaging of the prostate has shown promising results and may be of additional value in prostate cancer localization and local staging. Consensus on which technical approaches (field strengths, sequences, use of an endorectal coil) and combination of multiparametric MR imaging techniques should be used for specific clinical indications remains a challenge. Because guidelines are currently lacking, suggestions for a general minimal protocol for multiparametric MR imaging of the prostate based on the literature and the authors' experience are presented. Computer programs that allow evaluation of the various components of a multiparametric MR imaging examination in one view should be developed. In this way, an integrated interpretation of anatomic and functional MR imaging techniques in a multiparametric MR imaging examination is possible. Education and experience of specialist radiologists are essential for correct interpretation of multiparametric prostate MR imaging findings. Supportive techniques, such as computer-aided diagnosis are needed to obtain a fast, cost-effective, easy, and more reproducible prostate cancer diagnosis out of more and more complex multiparametric MR imaging data.

Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI.

In this study, computer-assisted analysis of prostate lesions was researched by combining information from two different magnetic resonance (MR) modalities: T2-weighted (T2-w) and dynamic contrast-enhanced (DCE) T1-w images. Two issues arise when incorporating T2-w images in a computer-aided diagnosis (CADx) system: T2-w values are position as well as sequence dependent and images can be misaligned due to patient movement during the acquisition. A method was developed that computes T2 estimates from a T2-w and proton density value and a known sequence model. A mutual information registration strategy was implemented to correct for patient movement. Global motion is modelled by an affine transformation, while local motion is described by a volume preserving non-rigid deformation based on B-splines. The additional value to the discriminating performance of a DCE T1-w-based CADx system was evaluated using bootstrapped ROC analysis. T2 estimates were successfully computed in 29 patients. T2 values were extracted and added to the CADx system from 39 malignant, 19 benign and 29 normal annotated regions. T2 values alone achieved a diagnostic accuracy of 0.85 (0.77-0.92) and showed a significantly improved discriminating performance of 0.89 (0.81-0.95), when combined with DCE T1-w features. In conclusion, the study demonstrated a simple T2 estimation method that has a diagnostic performance such that it complements a DCE T1-w-based CADx system in discriminating malignant lesions from normal and benign regions. Additionally, the T2 estimate is beneficial to visual inspection due to the removed coil profile and fixed window and level settings.

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.

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

A novel automated computerized scheme has been developed for determining a likelihood measure of malignancy for cancer suspicious regions in the prostate based on dynamic contrast-enhanced magnetic resonance imaging (MRI) (DCE-MRI) images. Our database consisted of 34 consecutive patients with histologically proven adenocarcinoma in the peripheral zone of the prostate. Both carcinoma and non-malignant tissue were annotated in consensus on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. The annotations were used as regions of interest (ROIs). A feature set comprising pharmacokinetic parameters and a T1 estimate was extracted from the ROIs to train a support vector machine as classifier. The output of the classifier was used as a measure of likelihood of malignancy. Diagnostic performance of the scheme was evaluated using the area under the ROC curve. The diagnostic accuracy obtained for differentiating prostate cancer from non-malignant disorders in the peripheral zone was 0.83 (0.75-0.92). This suggests that it is feasible to develop a computer aided diagnosis system capable of characterizing prostate cancer in the peripheral zone based on DCE-MRI.