Abbreviated Screening MRI for Women with a History of Breast Cancer:                     Comparison with Full-Protocol Breast MRI

Kim, Soo-Yeon; Cho, Nariya; Hong, Hyunsook; Lee, Youkyoung; Yoen, Heera; Kim, Yeon Soo; Park, Ah Reum; Ha, Su Min; Lee, Su Hyun; Chang, Jung Min; Moon, Woo Kyung

doi:10.1148/radiol.213310

Cited by 25 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Cheng et al discovered that ZnGa 2 O 4 :Cr 3+ (ZGCs) could be activated repeatedly by 18 F-FDG and exhibited long-lasting luminescence after excitation, allowing for in vivo imaging with high sensitivity and long decay time. 478 (Ba 0.55 Y 0.3 F 2 :Eu 3+ ) that could be excited by 18 F-FDG for in vivo imaging. 539 The use of Cherenkov radiation for effective energy transfer between fluorophores and radionuclides requires close proximity due to the strong absorption of Cherenkov radiation by biological tissues.…”

Section: Cerenkov Luminescence Imagingmentioning

confidence: 99%

“…538 18 Ffluorodeoxyglucose ( 18 F-FDG) is a clinically approved radiopharmaceutical used for tumor imaging, which has a short halflife of approximately 110 min. Cherenkov luminescence imaging using 18 F-FDG as the light source enables deep-tissue in vivo imaging. For example, Cheng et al discovered that ZnGa 2 O 4 :Cr 3+ (ZGCs) could be activated repeatedly by 18 F-FDG and exhibited long-lasting luminescence after excitation, allowing for in vivo imaging with high sensitivity and long decay time.…”

Section: Cerenkov Luminescence Imagingmentioning

confidence: 99%

“…10,11 CT, MRI, US, PET, and SPECT can typically achieve tissue penetration depth of several centimeters within the living organisms, exhibiting varying degrees of quantitative capabilities. 12−14 Furthermore, PET, MRI, and US exhibit exceptional sensitivity (∼10 −11 −10 −12 M), 15−17 spatial resolution (∼20−100 μm), 18,19 and temporal resolution (seconds), 20 respectively. However, every coin has two sides.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nanocrystals for Deep-Tissue In Vivo Luminescence Imaging in the Near-Infrared Region

Yang,

Jiang,

Zhang

2023

Chem. Rev.

View full text Add to dashboard Cite

In vivo imaging technologies have emerged as a powerful tool for both fundamental research and clinical practice. In particular, luminescence imaging in the tissuetransparent near-infrared (NIR, 700−1700 nm) region offers tremendous potential for visualizing biological architectures and pathophysiological events in living subjects with deep tissue penetration and high imaging contrast owing to the reduced light−tissue interactions of absorption, scattering, and autofluorescence. The distinctive quantum effects of nanocrystals have been harnessed to achieve exceptional photophysical properties, establishing them as a promising category of luminescent probes. In this comprehensive review, the interactions between light and biological tissues, as well as the advantages of NIR light for in vivo luminescence imaging, are initially elaborated. Subsequently, we focus on achieving deep tissue penetration and improved imaging contrast by optimizing the performance of nanocrystal fluorophores. The ingenious design strategies of NIR nanocrystal probes are discussed, along with their respective biomedical applications in versatile in vivo luminescence imaging modalities. Finally, thought-provoking reflections on the challenges and prospects for future clinical translation of nanocrystal-based in vivo luminescence imaging in the NIR region are wisely provided.

show abstract

Section: Cerenkov Luminescence Imagingmentioning

confidence: 99%

Section: Cerenkov Luminescence Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nanocrystals for Deep-Tissue In Vivo Luminescence Imaging in the Near-Infrared Region

Yang,

Jiang,

Zhang

2023

Chem. Rev.

View full text Add to dashboard Cite

show abstract

“…Currently, MRI-based breast cancer screening is highly valued by clinicians and researchers [10,11,12,13,14]. Typically, only T1-weighted pre-and post-contrast MRI and DWI or T2 are included in the screening protocols, and while some abbreviated MRI protocols have also explored obtaining more MRI parameters, this is time-consuming and costly [3,15,16,17,18]. Unfortunately, this may lead to missing characteristic information of unscanned MRI sequences, such as T2-weighted MRI, that may sometimes be valuable for breast lesion characterization [19,3,20].…”

Section: Introductionmentioning

confidence: 99%

IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data

Zhang¹,

Tan²,

Luyi³

et al. 2023

Preprint

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of lesions. Such multi-parameter MRI information has been shown to improve radiologist performance in lesion classification, as well as improving the performance of artificial intelligence models in various tasks. However, obtaining multi-parameter MRI makes the examination costly in both financial and time perspectives, and there may be safety concerns for special populations, thus making acquisition of the full spectrum of MRI sequences less durable. In this study, different than naive input fusion or feature concatenation from existing MRI parameters, a novel Integrated MRI Multi-Parameter reinfOrcement fusion generatoR wiTh AtteNTion Network (IMPORTANT-Net) is developed to generate missing parameters. First, the parameter reconstruction module is used to encode and restore the existing MRI parameters to obtain the corresponding latent representation information at any scale level. Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information. Finally, a reinforcement fusion scheme embedded in a V − -shape generation module is used to combine the hierarchical representations to generate the missing MRI parameter. Results showed that our IMPORTANT-Net is capable of generating missing MRI parameters and outperforms comparable state-of-the-art networks. Our code is available at https://github.com/Netherlands-Cancer-Institute/MRI_IMPORTANT_NET.

show abstract

“…[1] For women with high risk of breast cancer, magnetic resonance imaging (MRI) has been proven to be an important tool for the examination of suspicious breast lesions, [2] as MRI provides multi-directional and multi-sequence imaging along with high soft tissue discrimination ability and can define the nature of the lesions through the blood supply situation around the mass. [3] More importantly, as a functional method of imaging, MRI can reflect the diffusion of water molecules in living tissues by the apparent diffusion coefficient (ADC). Diffusion-weighted imaging (DWI) has been widely used for diagnosis, prognosis, efficacy monitoring and evaluation of recurrence and metastasis.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

Wang

Jin

et al. 2022

Medicine

View full text Add to dashboard Cite

Background: This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to monoexponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer.Methods: A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer.Results: Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed.Conclusions: Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.Abbreviations: 95% CI = 95% confidence interval, ADC = apparent diffusion coefficient, AUC = areas under the receiver operating characteristic curve, DKI = diffusion kurtosis imaging, DWI = diffusion weighted imaging, MD = mean diffusivity, MK = mean kurtosis.

show abstract

Abbreviated Screening MRI for Women with a History of Breast Cancer: Comparison with Full-Protocol Breast MRI

Cited by 25 publications

References 34 publications

Nanocrystals for Deep-Tissue In Vivo Luminescence Imaging in the Near-Infrared Region

Nanocrystals for Deep-Tissue In Vivo Luminescence Imaging in the Near-Infrared Region

IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

Contact Info

Product

Resources

About