Erickson, Bradley J.

PurposeEarly identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In noncontrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyperacute phase (< 8 h from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry‐based analysis of texture features of ischemic lesions in noncontrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase.MethodsOne hundred thirty‐nine (139) ncCT scans of hyperacute ischemic stroke with follow‐up magnetic resonance diffusion‐weighted (MR‐DW) images were collected. The regions of stroke were identified in the MR‐DW images, which were spatially aligned to corresponding ncCT images. A state‐of‐the‐art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR‐DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co‐occurrence and run‐length texture‐based image features in the regions of ischemic stroke and their contralateral regions.ResultsThe classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions.ConclusionsThe results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in noncontrast CT. This work is an initial step toward development of an automated decision support system for detection of hyperacute ischemic stroke lesions on noncontrast CT of the brain.

Abstract As anticancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. Although traditional, anatomic CT, and MRI examinations are useful in many settings, increasing evidence suggests that these methods cannot answer the fundamental biologic and physiologic questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients and to provide a more efficient path for the development of improved targeted therapies. Clin Cancer Res; 22(2); 284–90. ©2016 AACR.

Objective The objective of this study was to determine whether dynamic magnetic resonance imaging (dMRI) enhancement parameters could predict dopamine agonist (DA) resistance in prolactinomas. Methods We retrospectively identified patients with prolactinomas who were treated with DA and underwent dMRI from 2001 through 2012 at Mayo Clinic (Rochester, MN). Intensities of the adenoma and pituitary gland were measured by drawing regions of interest on the images. Enhancement ratio, enhancement peak, prepeak slope (PPS), and enhancement time were compared between DA-resistant and DA-responsive groups, between DA-treated and DA-naive groups, and between the first and follow-up dMRIs. Results We identified 49 patients with prolactinomas, with 6 (12.2%) showing DA resistance. Thirty-seven patients (75.5%) underwent dMRI while receiving treatment, 12 (25.5%) underwent dMRI before starting therapy, and 10 (20.4%) had follow-up dMRI after DA therapy. The PPS of the tumor was higher in the treatment-resistant group versus the responsive group (mean [SD], 4.42 [3.19] vs 2.65 [1.59]; P = 0.03), whereas no difference was noted in the pituitary gland (5.79 [2.21] vs 4.06 [2.48]; P = 0.11). Logistic regression analysis indicated that tumor PPS was associated with DA resistance (odds ratio, 1.71; 95% confidence interval, 1.07–3.27; P = 0.02). Conclusions Dynamic MRI with PPS analysis potentially can be used early in the treatment course to evaluate DA resistance in pituitary prolactinomas.

Abstract Background Renal imaging examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) patients. TKV has become the gold-standard image biomarker for ADPKD progression at early stages of the disease and is used in clinical trials to characterize treatment efficacy. Automated methods to segment the kidneys and measure TKV are desirable because of the long time requirement for manual approaches such as stereology or planimetry tracings. However, ADPKD kidney segmentation is complicated by a number of factors, including irregular kidney shapes and variable tissue signal at the kidney borders. Methods We describe an image processing approach that overcomes these problems by using a baseline segmentation initialization to provide automatic segmentation of follow-up scans obtained years apart. We validated our approach using 20 patients with complete baseline and follow-up T1-weighted magnetic resonance images. Both manual tracing and stereology were used to calculate TKV, with two observers performing manual tracings and one observer performing repeat tracings. Linear correlation and Bland–Altman analysis were performed to compare the different approaches. Results Our automated approach measured TKV at a level of accuracy (mean difference ± standard error = 0.99 ± 0.79%) on par with both intraobserver (0.77 ± 0.46%) and interobserver variability (1.34 ± 0.70%) of manual tracings. All approaches had excellent agreement and compared favorably with ground-truth manual tracing with interobserver, stereological and automated approaches having 95% confidence intervals ∼±100 mL. Conclusions Our method enables fast, cost-effective and reproducible quantification of ADPKD progression that will facilitate and lower the costs of clinical trials in ADPKD and other disorders requiring accurate, longitudinal kidney quantification. In addition, it will hasten the routine use of TKV as a prognostic biomarker in ADPKD.

PurposeNoninvasive imaging techniques that quantify renal tissue composition are needed to more accurately ascertain prognosis and monitor disease progression in polycystic kidney disease (PKD). Given the success of magnetization transfer (MT) imaging to characterize various tissue remodeling pathologies, it was tested on a murine model of autosomal dominant PKD.MethodsC57Bl/6 Pkd1 R3277C mice at 9, 12, and 15 months were imaged with a 16.4T MR imaging system. Images were acquired without and with RF saturation in order to calculate MT ratio (MTR) maps. Following imaging, the mice were euthanized and kidney sections were analyzed for cystic and fibrotic indices, which were compared with statistical parameters of the MTR maps.ResultsThe MTR‐derived mean, median, 25th percentile, skewness, and kurtosis were all closely related to indices of renal pathology, including kidney weight/body weight, cystic index, and percent of remaining parenchyma. The correlation between MTR and histology‐derived cystic and fibrotic changes was R2 = 0.84 and R2 = 0.70, respectively.ConclusionMT imaging provides a new, noninvasive means of measuring tissue remodeling PKD changes and may be better suited for characterizing renal impairment compared with conventional MR techniques. Magn Reson Med 75:1466–1473, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

ABSTRACTBACKGROUND AND PURPOSERequests for after‐hours emergent spine MR imaging seem to be increasing. We sought to review the trend in after hours spine MRI utilization at our institution and to determine how these results impacted therapeutic intervention.METHODSFollowing Institutional Review Board approval, reports from 179 after hours spinal MRI's performed over the past 13 years were obtained and the relevant electronic medical records were reviewed.RESULTSEmergent after hours spine MRI utilization increased from 7 per year to 23 over 13 years. Fifty‐eight percent (104/179) had significant findings. Twenty‐nine percent (52/179) of all patients imaged underwent surgery to treat pathologies identified on MR. Surgery was performed in only 2% (4/179) of these patients within 3 hours and 6% (10/179) within 6 hours of MRI completion. Five percent (8/179) had findings that were treated with radiation therapy and in 78% of these it was performed within 6–12 hours. Of those in whom steroids or antibiotics were initiated, 41% and 50% were treated within 3 hours of MR scanning, respectively.DISCUSSIONClinical use of emergent after hours spine MRI is steadily increasing at our institution. While MR imaging often discerned significant pathologies, performing these emergent studies rarely resulted in immediate surgical or radiotherapeutic intervention.

SummaryPurposeThe distinction between corticotropin (ACTH)‐producing pituitary adenomas and occult ectopic ACTH neoplasms is a challenge and frequently complicated by the detection of nonfunctioning pituitary adenomas on dynamic magnetic resonance imaging (DMRI). Herein, we explored quantitative differences in DMRI measures from ACTH‐producing pituitary adenomas.Materials and methodsFifty‐five patients with pathology confirmed ACTH‐producing and 44 with nonfunctioning pituitary adenomas were analysed in this retrospective pilot study. The intensities of adenomas and of adjacent pituitary tissue were measured by drawing a region of interest. Time–intensity curves were then constructed, and quantitative analysis included: enhancement time, enhancement peak and prepeak slope (PPS). Multivariable logistic analysis and receiver operating characteristic curves (ROC) were used to evaluate the parameters.ResultsQuantitative analysis showed that tumour PPS in ACTH‐producing adenomas was markedly lower than that in nonfunctioning adenomas (P = 0·0042) and that the PPS of the adjacent pituitary gland was not different (P = 0·2441). The PPS was >3·0 in 59·1% of nonfunctioning adenomas and ≤3·0 in 69·1% of ACTH‐producing adenomas (P = 0·0049). Logistic analysis revealed lower tumour PPS levels (P = 0·0123, OR 1·24, 95% CI: 1·05–1·52) were associated with ACTH‐producing adenomas. The optimal PPS cut‐off determined by ROC analysis was 2·89, with a sensitivity of 69% and specificity of 70%. No significant difference was found between the two groups in enhancement time or enhancement peak.ConclusionEnhancement parameters in DMRI can help distinguishing ACTH producing from nonfunctioning pituitary adenomas, which could be useful in the differential diagnosis between ACTH‐producing pituitary adenomas and ectopic ACTH syndrome complicated with nonfunctioning pituitary adenoma.