Fast MRI breast cancer screening – Ready for prime time

Partovi, Sasan; Sin, David; Lu, Ziang; Sieck, Leah; Marshall, Holly; Pham, Ramya; Plecha, Donna

doi:10.1016/j.clinimag.2019.10.013

Cited by 40 publications

(25 citation statements)

References 54 publications

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“…This is in contrast to the reported CDR of an fpMRI in women with dense breasts of 16.5/1000 [ 30 ]. Although quite a number of studies have demonstrated the benefits of abMRI [ 72 , 73 ], a universal abMRI protocol has not yet been developed. It is hoped that once a protocol has been standardized, women with dense breasts and those at intermediate risk for breast cancer will have access to MRI as the most sensitive screening tool, rather than only patients at the highest risk [ 30 , 72 , 74 ].…”

Section: Abbreviated Breast Mrimentioning

confidence: 99%

Current and Future Directions of Breast MRI

Houser

Barreto

Mehta

et al. 2021

JCM

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Magnetic resonance imaging (MRI) is the most sensitive exam for detecting breast cancer. The American College of Radiology recommends women with 20% or greater lifetime risk of developing breast cancer be screened annually with MRI. However, other high-risk populations would also benefit. Hartmann et al. reported women with atypical hyperplasia have nearly a 30% incidence of breast cancer at 25-year follow-up. Women with dense breast tissue have up to a 4-fold increased risk of breast cancer when compared to average-risk women; their cancers are more likely to be mammographically occult. Because multiple cohorts of women are at high risk for developing breast cancer, there has been a movement to develop an abbreviated MRI (abMRI) protocol to expand the availability of MRI screening. Studies on abMRI effectiveness have been promising, with Weinstein et al. demonstrating a cancer detection rate of 27.4/1000 in women with dense breasts after a negative digital breast tomosynthesis. Breast MRI is also used to evaluate the extent of disease as part of preoperative assessment in women with newly diagnosed breast cancer, and to assess a patient’s response to neoadjuvant chemotherapy. This paper aims to explore the current uses of MRI and propose future indications and directions.

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Section: Abbreviated Breast Mrimentioning

confidence: 99%

Current and Future Directions of Breast MRI

Houser

Barreto

Mehta

et al. 2021

JCM

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“…At present, the imaging examinations used for the diagnosis of lung cancer mainly include X-ray examination, magnetic resonance (MRI), and CT. X-rays can detect abnormal signs of the lungs at an early stage, but X-rays have many hidden areas for detection and are prone to missed diagnosis [16]. MRI has good resolution and can clearly distinguish the mediastinum, hilar vessels, masses, and lymph nodes, but the lungs are high in air content and are prone to artifacts [17]. Due to its high resolution, CT has the characteristics of fast scanning time and clear images and is not easily affected by lung gas.…”

Section: Analysis Of Consistency Between Ct and Postoperativementioning

confidence: 99%

Correlation between Imaging Features and Pathological Stages of Primary Lung Tumors Based on Nanocontrast Agents

Li¹,

Qian²,

Mao

et al. 2021

Computational and Mathematical Methods in Medicine

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As one of the conventional methods of lung cancer detection, computed tomography (CT) usually requires the use of contrast agents to enhance the imaging effect. Conventional iodine contrast agents have poor signal-to-noise ratio and are prone to adverse reactions. It is necessary to find more effective and safe contrast agents for CT scans. The gold nanoparticles with secondary electron effect and photoelectric absorption effect can prolong the display time of the patient’s blood circulation after being injected into the patient’s body, which makes the nanocontrast agent a research hotspot in the field of CT imaging. In this study, ultrasmall gold nanoclusters with a diameter of about 5 nm were used as the contrast agent in CT scans. It was found that CT scans based on nanocontrast agents can obtain high-quality lung cancer imaging images, and the patient has no obvious adverse reactions. When observing the CT image, it was found that the stage of lung cancer patients can be clearly distinguished through the CT scan image. When analyzing the consistency of CT imaging and pathological classification, the Kappa value was 0.810, indicating that the two have a high degree of consistency. Therefore, this study believes that the imaging characteristics of primary lung tumors based on nanocontrast agents are highly correlated with their pathological types.

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“…In equation (7), TP is expressed as the true positive of the test result, FN is false negative, FP is false positive, TN is true negative, and the confusion map shown in Figure 2 is obtained.…”

Section: Observation Indicatorsmentioning

confidence: 99%

“…is imaging is effective for the detection of substantive and cystic masses in the breast and can reflect the situation of axillary lymph nodes and surrounding tissues. Moreover, with the popularity of Doppler ultrasound imaging technology, its imaging performance is greatly improved [7]. However, due to the defect of unclear images in ultrasound, the detection probability of small breast lesions such as intraductal carcinoma is low [8].…”

Section: Introductionmentioning

confidence: 99%

Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

Liu

Tang

Ruan

et al. 2021

Scientific Programming

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This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM ( P < 0.05 ). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants ( P < 0.05 ). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups ( P < 0.05 ). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.

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Fast MRI breast cancer screening – Ready for prime time

Cited by 40 publications

References 54 publications

Current and Future Directions of Breast MRI

Current and Future Directions of Breast MRI

Correlation between Imaging Features and Pathological Stages of Primary Lung Tumors Based on Nanocontrast Agents

Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

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