Rebecca Rakow-Penner

Novel technique for characterizing prostate cancer utilizing MRI restriction spectrum imaging: proof of principle and initial clinical experience with extraprostatic extension

Background:Standard magnetic resonance imaging (MRI) of the prostate lacks sensitivity in the diagnosis and staging of prostate cancer (PCa). To improve the operating characteristics of prostate MRI in the detection and characterization of PCa, we developed a novel, enhanced MRI diffusion technique using restriction spectrum imaging (RSI-MRI).Methods:We compared the efficacy of our novel RSI-MRI technique with standard MRI for detecting extraprostatic extension (EPE) among 28 PCa patients who underwent MRI and RSI-MRI prior to radical prostatectomy, 10 with histologically proven pT3 disease. RSI cellularity maps isolating the restricted isotropic water fraction were reconstructed based on all b-values and then standardized across the sample with z-score maps. Distortion correction of the RSI maps was performed using the alternating phase-encode technique.Results:27 patients were evaluated, excluding one patient where distortion could not be performed. Preoperative standard MRI correctly identified extraprostatic the extension in two of the nine pT3 (22%) patients, whereas RSI-MRI identified EPE in eight of nine (89%) patients. RSI-MRI correctly identified pT2 disease in the remaining 18 patients.Conclusions:In this proof of principle study, we conclude that our novel RSI-MRI technology is feasible and shows promise for substantially improving PCa imaging. Further translational studies of prostate RSI-MRI in the diagnosis and staging of PCa are indicated.

Prostate diffusion imaging with distortion correction

PURPOSE Diffusion imaging in the prostate is susceptible to distortion from B0 inhomogeneity. Distortion correction in prostate imaging is not routinely performed, resulting in diffusion images without accurate localization of tumors. We performed and evaluated distortion correction for diffusion imaging in the prostate. MATERIALS AND METHODS 28 patients underwent pre-operative MRI (T2, Gadolinium perfusion, diffusion at b=800 s/mm(2)). The restriction spectrum protocol parameters included b-values of 0, 800, 1500, and 4000 s/mm(2) in 30 directions for each nonzero b-value. To correct for distortion, forward and reverse trajectories were collected at b=0 s/mm(2). Distortion maps were generated to reflect the offset of the collected data versus the corrected data. Whole-mount histology was available for correlation. RESULTS Across the 27 patients evaluated (excluding one patient due to data collection error), the average root mean square distortion distance of the prostate was 3.1 mm (standard deviation, 2.2mm; and maximum distortion, 12 mm). CONCLUSION Improved localization of prostate cancer by MRI will allow better surgical planning, targeted biopsies and image-guided treatment therapies. Distortion distances of up to 12 mm due to standard diffusion imaging may grossly misdirect treatment decisions. Distortion correction for diffusion imaging in the prostate improves tumor localization.

MRI-Derived Restriction Spectrum Imaging Cellularity Index is Associated with High Grade Prostate Cancer on Radical Prostatectomy Specimens.

Purpose: We evaluate a novel magnetic resonance imaging (MRI) technique to improve detection of aggressive prostate cancer (PCa). Materials and Methods: We performed a retrospective analysis of pre-surgical prostate MRI scans using an advanced diffusion-weighted imaging technique called restriction spectrum imaging (RSI), which can be presented as a normalized z-score statistic. Scans were acquired prior to radical prostatectomy. Prostatectomy specimens were processed using whole-mount sectioning and regions of interest (ROIs) were drawn around individual PCa tumors. Corresponding ROIs were drawn on the MRI imaging and paired with ROIs in regions with no pathology. RSI z-score and conventional apparent diffusion coefficient (ADC) values were recorded for each ROI. Paired t-test, ANOVA, and logistic regression analyses were performed. Results: We evaluated 28 patients with 64 ROIs (28 benign and 36 PCa). The mean difference in RSI z-score (PCa ROI–Benign ROI) was 2.17 (SE = 0.11; p < 0.001) and in ADC was 551 mm2/s (SE = 80 mm2/s; paired t-test, p < 0.001). The differences in the means among all groups (benign, primary Gleason 3, and primary Gleason 4) was significant for both RSI z-score (F3,64 = 97.7, p < 0.001) and ADC (F3,64 = 13.9, p < 0.001). A t-test was performed on only PCa tumor ROIs (n = 36) to determine PCa aggressiveness (Gleason 3 vs. Gleason 4) revealing that RSI z-score was still significant (p = 0.03), whereas, ADC values were no longer significant (p = 0.08). In multivariable analysis adjusting for age and race, RSI z-score was associated with PCa aggressiveness (OR 10.3, 95% CI: 1.4–78.0, p = 0.02) while ADC trended to significance (p = 0.07). Conclusion: The RSI-derived normalized cellularity index is associated with aggressive PCa as determined by pathologic Gleason scores. Further utilization of RSI techniques may serve to enhance standardized reporting systems for PCa in the future.

Role of Imaging in the Era of Precision Medicine

Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI.

Restriction spectrum imaging (RSI) is a novel diffusion‐weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b‐values in combination with postprocessing techniques allow the simultaneous resolution of length‐scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2‐weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting.

In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI.

Background:Magnetic resonance imaging (MRI) is emerging as a robust, noninvasive method for detecting and characterizing prostate cancer (PCa), but limitations remain in its ability to distinguish cancerous from non-cancerous tissue. We evaluated the performance of a novel MRI technique, restriction spectrum imaging (RSI-MRI), to quantitatively detect and grade PCa compared with current standard-of-care MRI.Methods:In a retrospective evaluation of 33 patients with biopsy-proven PCa who underwent RSI-MRI and standard MRI before radical prostatectomy, receiver-operating characteristic (ROC) curves were performed for RSI-MRI and each quantitative MRI term, with area under the ROC curve (AUC) used to compare each term’s ability to differentiate between PCa and normal prostate. Spearman rank-order correlations were performed to assess each term’s ability to predict PCa grade in the radical prostatectomy specimens.Results:RSI-MRI demonstrated superior differentiation of PCa from normal tissue, with AUC of 0.94 and 0.85 for RSI-MRI and conventional diffusion MRI, respectively (P=0.04). RSI-MRI also demonstrated superior performance in predicting PCa aggressiveness, with Spearman rank-order correlation coefficients of 0.53 (P=0.002) and −0.42 (P=0.01) for RSI-MRI and conventional diffusion MRI, respectively, with tumor grade.Conclusions:RSI-MRI significantly improves upon current noninvasive PCa imaging and may potentially enhance its diagnosis and characterization.

Demonstration of Non-Gaussian Restricted Diffusion in Tumor Cells Using Diffusion Time-Dependent Diffusion-Weighted Magnetic Resonance Imaging Contrast

The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water mobility for probing microstructural properties of biological tissue and has become an effective tool for collecting information about the underlying pathology of cancerous tissue. Measurements using multiple b-values have indicated biexponential signal attenuation, ascribed to “fast” (high ADC) and “slow” (low ADC) diffusion components. In this empirical study, we investigate the properties of the diffusion time (Δ)-dependent components of the diffusion-weighted (DW) signal in a constant b-value experiment. A xenograft gliobastoma mouse was imaged using Δ = 11 ms, 20 ms, 40 ms, 60 ms, and b = 500–4000 s/mm2 in intervals of 500 s/mm2. Data were corrected for EPI distortions, and the Δ-dependence on the DW-signal was measured within three regions of interest [intermediate- and high-density tumor regions and normal-appearing brain (NAB) tissue regions]. In this study, we verify the assumption that the slow decaying component of the DW-signal is non-Gaussian and dependent on Δ, consistent with restricted diffusion of the intracellular space. As the DW-signal is a function of Δ and is specific to restricted diffusion, manipulating Δ at constant b-value (cb) provides a complementary and direct approach for separating the restricted from the hindered diffusion component. We found that Δ-dependence is specific to the tumor tissue signal. Based on an extended biexponential model, we verified the interpretation of the diffusion time-dependent contrast and successfully estimated the intracellular restricted ADC, signal volume fraction, and cell size within each ROI.

Voxel Level Radiologic–Pathologic Validation of Restriction Spectrum Imaging Cellularity Index with Gleason Grade in Prostate Cancer

Purpose: Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions. Experimental Design: Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity. Results: RSI-MRI cellularity index was found to distinguish between prostate cancer and benign tumor (t = 25.48, P < 0.00001). Significant differences were also found between benign tissue and prostate cancer classified as low-grade (GG = 3; t = 11.56, P < 0.001) or high-grade (GG ≥ 4; t = 24.03, P < 0.001). Furthermore, RSI-MRI differentiated between low and high-grade prostate cancer (t = 3.23; P = 0.003). Conclusions: Building on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. Because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may become an option for planning targeted procedures where identifying the area with the most aggressive disease is important. Clin Cancer Res; 22(11); 2668–74. ©2016 AACR.

Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer.

Background High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored. Purpose To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions. Material and Methods This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived (SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials (KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal (KCE). Results Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 – 0.87), 0.69 (95% CI = 0.68–0.70), and 0.86 (95% CI = 0.86–0.87), respectively. Conclusion In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision. KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP.

State of the Art Diffusion Weighted Imaging in the Breast: Recommended Protocol

PurposeReview of breast diffusion MRI and recommend a protocol on how to perform breast diffusion MRI.Recent findingsBreast diffusion MRI may help improve specificity and predict an early response to neoadjuvant chemotherapy.SummaryBreast cancer is the most commonly diagnosed malignancy in American women. Mammography is the only screening modality shown to decrease breast cancer related mortality. However, false positives, false negatives, and dense breast limit mammographic evaluation. Dynamic contrast-enhanced breast MRI (DCE-MR) is the most sensitive imaging modality to evaluate for breast cancer, although specificity remains problematic. Diffusion-weighted imaging (DWI) evaluates the microscopic motion of spins in tissue and is quantified by the apparent diffusion coefficient (ADC). DWI in assessment of breast cancer is under active investigation including the ACRIN 6698 multi-center trial. The role of DWI in the breast has focused on evaluation of response to neoadjuvant chemotherapy and has been limited by overlap in benign and malignant lesion appearance.