Consistency of breast density measured from the same women in four different MR scanners.
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ABSTRACT:
SUBMITTER: Chen JH
PROVIDER: S-EPMC3411588 | biostudies-other | 2012 Aug
REPOSITORIES: biostudies-other
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Project description: Not available
| S-EPMC3221515 | biostudies-literature
Project description:PurposeTo investigate the fluctuation of fibroglandular tissue volume (FV) and percentage of breast density (PD) during the menstrual cycle and compare with postmenopausal women by using three-dimensional magnetic resonance (MR)-based segmentation methods.Materials and methodsThis study was approved by the Institutional Review Board and was HIPAA compliant. Written informed consent was obtained. Thirty healthy female subjects, 24 premenopausal and six postmenopausal, were recruited. All subjects underwent MR imaging examination each week for 4 consecutive weeks. The breast volume (BV), FV, and PD were measured by two operators to evaluate interoperator variation. The fluctuation of each parameter measured over the course of the four examinations was evaluated on the basis of the coefficient of variation (CV).ResultsThe results from two operators showed a high Pearson correlation for BV (R(2) = 0.99), FV (R(2) = 0.98), and PD (R(2) = 0.96). The interoperator variation was 3% for BV and around 5%-6% for FV and PD. In the respective premenopausal and postmenopausal groups, the mean CV was 5.0% and 5.6% for BV, 7.6% and 4.2% for FV, and 7.1% and 6.0% for PD. The difference between premenopausal and postmenopausal groups was not significant (all P values > .05).ConclusionThe fluctuation of breast density measured at MR imaging during a menstrual cycle was around 7%. The results may help the design and interpretation of future studies by using the change of breast density as a surrogate marker to evaluate the efficacy of hormone-modifying drugs for cancer treatment or cancer prevention.
| S-EPMC3219914 | biostudies-literature
Project description:PURPOSE:The accuracy of flow velocity and three-directional velocity components are important for the precise visualization of hemodynamics by 3D cine phase-contrast MRI (3D cine PC MRI, also referred to as 4D-flow). The aim of this study was to verify the accuracy of these measurements of prototype or commercially available 3D cine PC MRI obtained by three different manufactures' MR scanners. METHODS:The verification of the accuracy of flow velocity in 3D cine PC MRI was performed by circulating blood mimicking fluid through a straight-tube phantom in a slanting position, such that the three-directional velocity components were simultaneously measurable, using three 3T MR scanners from different manufacturers. The data obtained were processed by phase correction, and the velocity and three-directional velocity components in the center of the tube on the central cross section of a slab were calculated. The velocity profile in each three directions and the composite velocity profiles were compared with the calculated reference values, using the Hagen-Poiseuille equation. In addition, velocity profiles and the spatially time-averaged velocity perpendicular to the tube were compared with the theoretical values and measured values by a flowmeter, respectively. RESULTS:An underestimation of the maximum velocity in the center of the tube and an overestimation of the velocity near the tube wall due to partial volume effects were observed in all three scanners. A roughening and flattening of profiles in the center of the tube were observed in one scanner, due, presumably, to the low signal-to-noise ratio. However, the spatially time-averaged velocities corresponded well with the measured values by the flowmeter in all three scanners. CONCLUSION:In this study, we have demonstrated that the accuracy of flow velocity and three-directional velocity components in 3D cine PC MRI was satisfactory in all three MR scanners.
| S-EPMC6883082 | biostudies-literature
Project description:To investigate the difference of MR percent breast density measured from fat-suppressed versus nonfat-suppressed imaging sequences.Breast magnetic resonance imaging (MRI) with and without fat suppression was acquired from 38 subjects. Breasts were divided into subgroups of different morphological patterns ("central" and "intermingled" types). Breast volume, fibroglandular tissue volume, and percent density were measured. The results were compared using nonparametric statistical tests and regarded as significant at p < 0.05.Breast volume, fibroglandular volume, and percent density between fat-suppressed and nonfat-suppressed sequences were highly correlated. Breast volumes measured on these two sequences were almost identical. Fibroglandular tissue volume and percent density, however, had small (<5%) yet significant differences between the two sequences-they were both higher on the fat-suppressed sequence. Intraobserver variability was within 4% for both sequences and different morphological types. The fibroglandular tissue volume measured on downsampled images showed a small (<5%) yet significant difference.The measurement of breast density made on MRI acquired using fat-suppressed and nonfat-suppressed T1W images was about 5% difference, only slightly higher than the intraobserver variability of 3%-4%. When the density data from multiple centers were to be combined, evaluating the degree of difference is needed to take this difference into account.
| S-EPMC3215688 | biostudies-other
Project description:MR Radiomics based on cervical lesions from one single scanner has achieved promising results. However, it is a challenge to achieve clinical translation. Considering multi-scanners and non-uniform scanning parameters from different centers in a real-world medical scenario, we should first identify the influence of such conditions on the robustness of MR radiomics features (RFs) based on the female cervix. In this study, 9 healthy female volunteers were enrolled and 3 kiwis were selected as references. Each of them underwent T2 weighted imaging in three different 3.0-T MR scanners with uniform acquisition parameters, and in one MR scanner with various scanning parameters. A total of 396 RFs were extracted from their images with and without decile intensity normalization. The RFs' reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Representative features were selected using the hierarchical cluster analysis and their discrimination abilities were estimated by ROC analysis through retrospective comparison with the junctional zone and the outer muscular layer of healthy cervix in patients (n = 58) with leiomyoma. This study showed that only a few RFs were robust across different MR scanners and acquisition parameters based on females' cervix, which might be improved by decile intensity normalization method.
| S-EPMC7684312 | biostudies-literature
Project description:PurposeTo quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy.Materials and methodsThis retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n = 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation. Paired Wilcoxon signed rank tests were performed.ResultsThe segmentation CNNs exhibited a significant performance drop when applied to datasets from different manufacturers (Dice reduced from 89.7% ± 2.3 [standard deviation] to 68.7% ± 10.8, P < .05, from 90.6% ± 2.1 to 59.5% ± 13.3, P < .05, from 89.2% ± 2.3 to 64.1% ± 12.0, P < .05, for manufacturer 1, 2, and 3, respectively). After manufacturer adaptation, the segmentation performance was significantly improved (from 68.7% ± 10.8 to 84.3% ± 6.2, P < .05, from 72.4% ± 10.2 to 85.7% ± 6.5, P < .05, for manufacturer 2 and 3, respectively). Quantitative LV function parameters were also significantly improved. For LVEF, the manufacturer adaptation increased the Pearson correlation from 0.005 to 0.89 for manufacturer 2 and from 0.77 to 0.94 for manufacturer 3.ConclusionA segmentation CNN well trained on datasets from one MRI manufacturer may not generalize well to datasets from other manufacturers. The proposed manufacturer adaptation can largely improve the generalizability of a deep learning segmentation tool without additional annotation.Supplemental material is available for this article.© RSNA, 2020.
| S-EPMC8082399 | biostudies-literature
Project description:ObjectiveTo compare the reliability of different methods for measuring fat content of pancreas by MR modified Dixon(mDixon) Sequence and accurately evaluate pancreatic fat in as simple a way as possible.MethodsThis is a retrospective study, 64 patients were included in this study who underwent abdominal MR scan that contained the mDixon sequence from June 2019 to May 2020(Included 7 patients with type 2 diabetes and 4 patients with impaired glucose tolerance (IGT), they were admitted to hospital through the obesity clinic set up by endocrine department, all of them were initially diagnosed and untreated). All of the 64 patients were scanned in 3.0T MR (Philips Ingenia II) due to their condition, 10-34 slice pancreas images were obtained, which were different from each other. Three different methods of measurement were employed by two observers using Philips Intellispace Portal software: (1) All images (whole-pancreas) measurement, the whole-pancreatic fat fraction (wPFF) was calculated by software. (2) Interval slices measurement, that is half-pancreatic slices fat fraction (hPFF) measured in the same way, fat fraction obtained by the interlayer assay was calculated. (3) As usual, the fat content of pancreatic head, body and tail fat was measured respectively, and in order to improve credibility, we also measured head、 body and tail in every layer, and its average value was taken. The elapsed time of the above different measurement methods was recorded. Intra-group correlation coefficient (ICC) was used to analyze the consistency of the measured data within and between observers. T-tests and Friedman tests were applied to compare the difference of measured values among groups.ResultsNo matter in normal person or diabetic or IGT, hPFF has shown good stability (ICChPFF = 0.988), and there was no significant difference compared with wPFF. But the average fat percentage composition of head, body and tail were significantly different from wPFF and hPFF (P < 0.01). At the same time, compared with normal person, pancreatic fat content in IGT and diabetic patients showed progressive significance(P<0.05).ConclusionThe distribution of pancreatic fat is not uniform, the method of measuring half pancreas by interlayer data collection can reflect the fat content of the entire pancreas, this suggests that measuring 50% of the pancreas is sufficient, this method effectively saves time and effort without affecting the results, which may have a better clinical application prospect.
| S-EPMC8608312 | biostudies-literature
Project description:PurposeTo compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection.MethodsTwo methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density.ResultsImages are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue.ConclusionsEpidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient.
| S-EPMC5697622 | biostudies-literature
Project description:BACKGROUND:Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants. METHODS:Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry. RESULTS:After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing. CONCLUSIONS:This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs.
| S-EPMC5097387 | biostudies-literature
Project description:BackgroundMost studies on female sexual dysfunction are performed in population inventories and under specific clinical conditions. These approaches are performed using validated psychometric scales. Different scales to assess sexual function use different numbers of questions to characterize their domains. They also may or may not include domains of interaction between sexual partners. The objective of this study was to compare the precision between scales to be able to analyze their accuracy for better diagnosis of sexual dysfunction.MethodsFifty (50) healthy young women were enrolled in this study. Three questionnaires (FSFI, SQ-F, and GRISS) were applied to assess sexual function (n = 44). The accuracy measured by the area under the ROC curve (AUC) for individual domains and to cross-validated pairwise comparison of the three analyzed instruments was used. Kruskall-Wallis test to analyze individual domains of the scales was also used.The P-value was established as 0.05.ResultsThe results showed that all domains and total FSFI and GRISS scores were significantly different between normal and dysfunctional women, but not for SQ-F domains. Indeed, AUC accuracy varied from excellent-good domain discrimination for FSFI and GRISS, but fair-poor for SQ-F. For the paired comparison between the three questionnaires a fair accuracy was detected. The specificity percentage was around 84% whereas that for sensibility was low, around 30%.ConclusionsThe best agreement was between FSFI and SQ-F, probably being related to high similar shared questions when compared to GRISS. The agreement between SQ-F and GRISS was low possible due to low number of questions in SQ-F to characterize similar domains. This study evidenced high agreement between scales to sensitivity and low agreement for specificity, thereby conferring fair accuracy between them. Thus, the limited grade for discriminatory capacity (AUC) for sexual response should be considered when comparing results from these three different questionnaires and also when comparing with other different scales. In addition, despite the diversity of scales, the high reliability and fit for their desire domain suggest that the FSFI scale has good accuracy for the current clinical assessment of women's sexual health.Clinical trial registrationNCT03241524 . Retrospectively registered on 08/02/2017.
| S-EPMC6302409 | biostudies-literature