MRI breast screening revisited

Heller, Samantha L.; Moy, Linda

doi:10.1002/jmri.26547

Cited by 32 publications

(21 citation statements)

References 47 publications

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“…This advantage of CE-BMRI over XRM in detecting malignant breast lesions is in line with findings of other groups, although the clinical trial settings are not always exactly comparable with respect to study population (patients with elevated breast cancer risk (38,39), dense breasts, proven breast cancer (40)) and imaging purpose (screening (39,41), preoperative staging (40), recurrence detection).…”

Section: Discussionsupporting

confidence: 79%

Diagnostic efficacy of contrast-enhanced breast MRI versus X-ray mammography in women with different degrees of breast density

et al. 2020

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Background Detection of breast cancer in women with high breast densities is a clinical challenge. Purpose To study the influence of different degrees of breast density on the sensitivity of contrast-enhanced breast magnetic resonance imaging (CE-BMRI) versus X-ray mammography (XRM). Material and Methods We performed an additional analysis of two large Phase III clinical trials (G1; G2) which included women with histologically proven breast cancers, called “index cancers.” Additional cancers were detected during image reading. We compared the sensitivity of CE-BMRI and XRM in women with different breast densities (ACR A→D; Version 5). For each study, six blinded readers evaluated the images. Results are given as the “Median Reader.” Results A total of 774 patients were included, 169 had additional cancers. While sensitivity of CE-BMRI for detecting all index cancers was independent of breast density (ACR A→D) (G1: 83%→83%; G2: 91%→91%) the sensitivity of XRM declined (ACR A→D) (G1: 79%→62%; G2: 82%→64%). Thus, the sensitivity difference between both imaging modalities in ACR A breasts of 3% (G1) and 9% (G2) increased to 21% (G1) and 26% (G2) in ACR D breasts. Sensitivity of CE-BMRI for detecting at least one additional cancer increased with increasing breast density (ACR A→D) (G1: 50%→73%, G2: 57%→81%). XRM’s sensitivity decreased (G1: 34%→20%) or remained stable (G2: 24%→25%). Conclusion CE-BMRI showed significantly higher sensitivity compared to XRM.

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Section: Discussionsupporting

confidence: 79%

Diagnostic efficacy of contrast-enhanced breast MRI versus X-ray mammography in women with different degrees of breast density

et al. 2020

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“…Previous studies 3,4 found that increased spatial resolution significantly improves diagnostic confidence and accuracy on breast DCE MRI. However, this improvement occurs at the expense of temporal resolution.…”

Section: Discussionmentioning

confidence: 96%

“…Then the intensity of the cropped images was normalized to [0, 1] with min-max normalization. The final preprocessed image dimension was 320 × 128 × 83, with voxel size 0.9 × 0.9 × 1.5 mm 3 .…”

Section: Data Preprocessingmentioning

confidence: 99%

“…BREAST MAGNETIC RESONANCE IMAGING (MRI) is recognized as the most sensitive imaging technique for breast cancer detection 1,2 . T 1 ‐weighted dynamic contrast‐enhanced (DCE) MRI, which provides both morphological and hemodynamic information, has emerged as an adjunct method to mammography and ultrasound 3,4 …”

mentioning

confidence: 99%

“…To generate images with relatively high‐resolution (HR) with in‐plane spatial resolution (≤1 mm pixel sizes), which present more detailed morphological features, the scan time for each dynamic is generally 60–90 seconds. Often, such a long scan acquisition time must be reduced, 3,4 which may lead to loss of kinetic information for certain malignant tumors. Furthermore, the overall breast MRI acquisition time is typically 20–30 minutes, and breast MRI is commonly performed in the prone position with the breasts pendant into specialized breast coil arrays, 5 leading to patient discomfort and anxiety 6,7 .…”

mentioning

confidence: 99%

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High‐Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network

Kim

Pan

et al. 2020

Magnetic Resonance Imaging

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Background: A generative adversarial network could be used for high-resolution (HR) medical image synthesis with reduced scan time. Purpose: To evaluate the potential of using a deep convolutional generative adversarial network (DCGAN) for generating HR pre and HR post images based on their corresponding low-resolution (LR) images (LR pre and LR post). Study Type: This was a retrospective analysis of a prospectively acquired cohort. Population: In all, 224 subjects were randomly divided into 200 training subjects and an independent 24 subjects testing set. Field Strength/Sequence: Dynamic contrast-enhanced (DCE) MRI with a 1.5T scanner. Assessment: Three breast radiologists independently ranked the image datasets, using the DCE images as the ground truth, and reviewed the image quality of both the original LR images and the generated HR images. The BI-RADS category and conspicuity of lesions were also ranked. The inter/intracorrelation coefficients (ICCs) of mean image quality scores, lesion conspicuity scores, and Breast Imaging Reporting and Data System (BI-RADS) categories were calculated between the three readers. Statistical Test: Wilcoxon signed-rank tests evaluated differences among the multireader ranking scores. Results: The mean overall image quality scores of the generated HR pre and HR post were significantly higher than those of the original LR pre and LR post (4.77 ± 0.41 vs. 3.27 ± 0.43 and 4.72 ± 0.44 vs. 3.23 ± 0.43, P < 0.0001, respectively, in the multireader study). The mean lesion conspicuity scores of the generated HR pre and HR post were significantly higher than those of the original LR pre and LR post (4.18 ± 0.70 vs. 3.49 ± 0.58 and 4.35 ± 0.59 vs. 3.48 ± 0.61, P < 0.001, respectively, in the multireader study). The ICCs of the image quality scores, lesion conspicuity scores, and BI-RADS categories had good agreements among the three readers (all ICCs >0.75). Data Conclusion: DCGAN was capable of generating HR of the breast from fast pre-and postcontrast LR and achieved superior quantitative and qualitative performance in a multireader study. Level of Evidence: 3 Technical Efficacy Stage: 2

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Intelligence Sparse Sensor Network for Automatic Early Evaluation of General Movements in Infants

Bao,

Zhang,

et al. 2024

Advanced Science

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General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants’ general movements can be captured digitally, but the lack of quantitative assessment and well‐trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low‐resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy‐to‐use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full‐body motion data. The proof‐of‐the‐concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence‐based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders.

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MRI breast screening revisited

Cited by 32 publications

References 47 publications

Diagnostic efficacy of contrast-enhanced breast MRI versus X-ray mammography in women with different degrees of breast density

Diagnostic efficacy of contrast-enhanced breast MRI versus X-ray mammography in women with different degrees of breast density

High‐Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network

Intelligence Sparse Sensor Network for Automatic Early Evaluation of General Movements in Infants

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