Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts

Contains fulltext : 191364.pdf (Publisher’s version ) (Closed access)RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived measurements. MATERIALS AND METHODS: An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study was performed to calculate percent breast density (PBD) by quantifying fibroglandular volume and total breast volume derived from magnetic resonance imaging (MRI) segmentation and mammograms using two commercially available software programs (Volpara and Quantra). Consecutive screening MRI exams from a 6-month period with negative or benign findings were used. The most recent mammogram within 9 months was used to derive mean density values from "for processing" images at the per breast level. Bland-Altman statistical analyses were performed to determine the mean discrepancy and the limits of agreement. RESULTS: A total of 110 women with 220 breasts met the study criteria. Overall, PBD was not different between MRI (mean 10%, range 1%-41%) and Volpara (mean 10%, range 3%-29%); a small but significant difference was present in the discrepancy between MRI and Quantra (4.0%, 95% CI: 2.9 to 5.0, P < 0.001). Discrepancy was highest at higher breast densities, with Volpara slightly underestimating and Quantra slightly overestimating PBD compared to MRI. The mean discrepancy for both Volpara and Quantra for total breast volume was not significantly different from MRI (p = 0.89, 0.35, respectively). Volpara tended to underestimate, whereas Quantra tended to overestimate fibroglandular volume, with the highest discrepancy at higher breast volumes. CONCLUSIONS: Both Volpara and Quantra tend to underestimate PBD, which is most pronounced at higher densities. PBD can be accurately measured using automated volumetric software programs, but values should not be used interchangeably between vendors

Adaptive statistical iterative reconstruction reduces patient radiation dose in neuroradiology CT studies.

INTRODUCTION: Adaptive statistical iterative reconstruction (ASIR) can decrease image noise, thereby generating CT images of comparable diagnostic quality with less radiation. The purpose of this study is to quantify the effect of systematic use of ASIR versus filtered back projection (FBP) for neuroradiology CT protocols on patients' radiation dose and image quality. METHODS: We evaluated the effect of ASIR on six types of neuroradiologic CT studies: adult and pediatric unenhanced head CT, adult cervical spine CT, adult cervical and intracranial CT angiography, adult soft tissue neck CT with contrast, and adult lumbar spine CT. For each type of CT study, two groups of 100 consecutive studies were retrospectively reviewed: 100 studies performed with FBP and 100 studies performed with ASIR/FBP blending factor of 40 %/60 % with appropriate noise indices. The weighted volume CT dose index (CTDIvol), dose-length product (DLP) and noise were recorded. Each study was also reviewed for image quality by two reviewers. Continuous and categorical variables were compared by t test and free permutation test, respectively. RESULTS: For adult unenhanced brain CT, CT cervical myelography, cervical and intracranial CT angiography and lumbar spine CT both CTDIvol and DLP were lowered by up to 10.9 % (p &lt; 0.001), 17.9 % (p = 0.005), 20.9 % (p &lt; 0.001), and 21.7 % (p = 0.001), respectively, by using ASIR compared with FBP alone. Image quality and noise were similar for both FBP and ASIR. CONCLUSION: We recommend routine use of iterative reconstruction for neuroradiology CT examinations because this approach affords a significant dose reduction while preserving image quality

Prediction of Early Arterial Recanalization and Tissue Fate in the Selection of Patients With the Greatest Potential to Benefit From Intravenous Tissue-Type Plasminogen Activator.

BACKGROUND AND PURPOSE: Our objective is to determine the performance of the combination of likelihood of arterial recanalization and tissue fate to predict functional clinical outcome in patients with acute stroke. METHODS: Clinical, imaging, and outcome data were collected in 173 patients with acute ischemic stroke who presented within 4.5 hours from symptom onset, in the time window eligible for intravenous tissue-type plasminogen activator. Imaging data included Alberta Score Program Early Computed Tomographic Score (ASPECTS), site of occlusion, volume of ischemic core and penumbra, and recanalization. Outcome data consisted of modified Rankin Scale score at 90 days. We classified patients based on their baseline imaging characteristics and treatment with intravenous tissue-type plasminogen activator (yes/no) according to 5 different hypothetical prognostic algorithms: (1) based on whether patients received intravenous tissue-type plasminogen activator, (2) based on ASPECTS, (3) based on the site of occlusion, (4) based on volume of ischemic core and penumbra, and (5) based on a matrix of predicted recanalization and volume of ischemic core and penumbra. We compared the performance of such algorithms to predict good clinical outcome, defined as modified Rankin Scale score of ≤2 at 90 days. RESULTS: One hundred and twenty-four patients received intravenous tissue-type plasminogen activator; 49 did not. In the group that was treated, 46 (37%) had good outcome as opposed to 38.7% in the nontreated. The algorithm that combined the prediction of recanalization with the volume of ischemic core and penumbra showed the highest accuracy to predict good outcome (77.7%) as opposed to others (range, 43.9%-57.2%) CONCLUSIONS: The combination of predicted recanalization and tissue fate proved superior to prognosticate good clinical outcome when compared with other usual predictors

Contributions of gender and systemic estradiol and testosterone concentrations to maximal secretagogue drive of burst-like growth hormone secretion in healthy middle-aged and older adults

To test whether concentrations of estradiol and testosterone predict GH responses to mechanistically distinct secretagogues in healthy older adults, we studied 16 volunteers (n = 10 men, n = 6 women, age 49-72 yr) in each of six randomly ordered sessions as follows: 1) saline; 2) L-arginine; 3) aerobic exercise; 4) GHRH; 5) GH-releasing peptide (GHRP)-2; and 6) somatostatin-induced rebound. Statistical comparisons disclosed that stimulus type (P < 0.001) and the interaction between gender and stimulus type (P = 0.023) determine GH secretion. In women, each secretagogue, except exercise and somatostatin-induced rebound, stimulated GH secretion by 2.6- to 6.4-fold over saline/rest (P < 0.023). In men, somatostatin-induced rebound drove GH secretion by 4.6-fold (P = 0.004), exercise by 16-fold (P < 0.001), and other secretagogues by 18- to 109-fold over saline/rest (each P < 0.001). Gender comparisons disclosed greater GH secretion in men than women after somatostatin-induced rebound (P = 0.008) and GHRP-2 injection (P < 0.001) and conversely greater GH secretion in women than men after saline (P = 0.013). Regression analysis showed that individual concentrations of estradiol (r = 0.80, P = 0.002) and testosterone (r = 0.63, P = 0.008) and their combination (r = 0.86, P < 0.001) strongly predict responses to GHRP-2 only. We conclude that among healthy middle-aged and older adults, the action of GHRP is uniquely determined by gender and physiological concentrations of testosterone and estradiol

Prediction of recanalization in acute stroke patients receiving intravenous and endovascular revascularization therapy.

BACKGROUND AND PURPOSE: The study aims to assess the recanalization rate in acute ischemic stroke patients who received no revascularization therapy, intravenous thrombolysis, and endovascular treatment, respectively, and to identify best clinical and imaging predictors of recanalization in each treatment group. METHODS: Clinical and imaging data were collected in 103 patients with acute ischemic stroke caused by anterior circulation arterial occlusion. We recorded demographics and vascular risk factors. We reviewed the noncontrast head computed tomographies to assess for hyperdense middle cerebral artery and its computed tomography density. We reviewed the computed tomography angiograms and the raw images to determine the site and degree of arterial occlusion, collateral score, clot burden score, and the density of the clot. Recanalization status was assessed on recanalization imaging using Thrombolysis in Myocardial Ischemia. Multivariate logistic regressions were utilized to determine the best predictors of outcome in each treatment group. RESULTS: Among the 103 study patients, 43 (42%) received intravenous thrombolysis, 34 (33%) received endovascular thrombolysis, and 26 (25%) did not receive any revascularization therapy. In the patients with intravenous thrombolysis or no revascularization therapy, recanalization of the vessel was more likely with intravenous thrombolysis (P = 0·046) and when M1/A1 was occluded (P = 0·001). In this subgroup of patients, clot burden score, cervical degree of stenosis (North American Symptomatic Carotid Endarterectomy Trial), and hyperlipidemia status added information to the aforementioned likelihood of recanalization at the patient level (P &lt; 0·001). In patients with endovascular thrombolysis, recanalization of the vessel was more likely in the case of a higher computed tomography angiogram clot density (P = 0·012), and in this subgroup of patients gender added information to the likelihood of recanalization at the patient level (P = 0·044). CONCLUSION: The overall likelihood of recanalization was the highest in the endovascular group, and higher for intravenous thrombolysis compared with no revascularization therapy. However, our statistical models of recanalization for each individual patient indicate significant variability between treatment options, suggesting the need to include this prediction in the personalized treatment selection

Prediction of Recanalization Trumps Prediction of Tissue Fate: The Penumbra: A Dual-edged Sword.

BACKGROUND AND PURPOSE: To determine whether infarct core or penumbra is the more significant predictor of outcome in acute ischemic stroke, and whether the results are affected by the statistical method used. METHODS: Clinical and imaging data were collected in 165 patients with acute ischemic stroke. We reviewed the noncontrast head computed tomography (CT) to determine the Alberta Score Program Early CT score and assess for hyperdense middle cerebral artery. We reviewed CT-angiogram for site of occlusion and collateral flow score. From perfusion-CT, we calculated the volumes of infarct core and ischemic penumbra. Recanalization status was assessed on early follow-up imaging. Clinical data included age, several time points, National Institutes of Health Stroke Scale at admission, treatment type, and modified Rankin score at 90 days. Two multivariate regression analyses were conducted to determine which variables predicted outcome best. In the first analysis, we did not include recanalization status among the potential predicting variables. In the second, we included recanalization status and its interaction between perfusion-CT variables. RESULTS: Among the 165 study patients, 76 had a good outcome (modified Rankin score ≤2) and 89 had a poor outcome (modified Rankin score &gt;2). In our first analysis, the most important predictors were age (P&lt;0.001) and National Institutes of Health Stroke Scale at admission (P=0.001). The imaging variables were not important predictors of outcome (P&gt;0.05). In the second analysis, when the recanalization status and its interaction with perfusion-CT variables were included, recanalization status and perfusion-CT penumbra volume became the significant predictors (P&lt;0.001). CONCLUSIONS: Imaging prediction of tissue fate, more specifically imaging of the ischemic penumbra, matters only if recanalization can also be predicted