Language：English   Publisher：Japanese Journal of Radiology  

Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost. Diffusion-weighted imaging (DWI), a non-contrast technique, has shown promise in differentiating malignant from benign lesions. It offers shorter scanning times and eliminates contrast agent risks. Apparent diffusion coefficient (ADC) values provide quantitative measures for lesion characterization, potentially reducing unnecessary biopsies. Studies have revealed some correlations between ADC values and hormone receptor status in breast cancers, although substantial variability exists among studies. However, standardization remains challenging. Initiatives such as European Society of Breast Imaging (EUSOBI), Diffusion-Weighted Imaging Screening Trial (DWIST), Quantitative Imaging Biomarkers Alliance (QIBA) have proposed guidelines to ensure consistency in imaging protocols and equipment specifications, addressing variability in ADC measurements across different sites and vendors. Advanced techniques like Intravoxel incoherent motion (IVIM) and non-Gaussian DWI offer insights into tissue microvasculature and microstructure. Despite ongoing challenges, the integration of these advanced MRI techniques shows great promise for improving breast cancer diagnosis, characterization, and treatment planning. Continued research and standardization efforts are crucial for maximizing the potential of breast DWI in enhancing patient care and outcomes.

DOI： 10.1007/s11604-024-01696-z

Open Access

Web of Science

Scopus

PubMed

Language：English  

DOI： 10.3389/fonc.2025.1524634

Web of Science

PubMed

Language：English   Publisher：European Radiology  

Objectives: Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches. Methods and results: We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue components with the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in two clinical examples. Conclusion: Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination. Clinical relevance statement: The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI. Key Points: Fat-signal suppression is essential for breast DWI; the SPAIR method is recommended. Short-tau inversion recovery (STIR) is not specific to fat; as a result, SNR is decreased and ADC values may be over- or underestimated. The STIR fat-suppression method must not be used after the injection of gadolinium-based contrast agents.

DOI： 10.1007/s00330-024-10973-4

Open Access

Web of Science

Scopus

PubMed

Language：English   Publisher：Radiology: Artificial Intelligence  

Purpose: To evaluate and compare the performance of different artificial intelligence (AI) models in differentiating between benign and malignant breast tumors at diffusion-weighted imaging (DWI), including comparison with radiologist assessments. Materials and Methods: In this retrospective study, patients with breast lesions underwent 3-T breast MRI from May 2019 to March 2022. In addition to T1-weighted imaging, T2-weighted imaging, and contrast-enhanced imaging, DWI was performed with five b values (0, 200, 800, 1000, and 1500 sec/ mm2). DWI data split into training and tuning and test sets were used for the development and assessment of AI models, including a small two-dimensional (2D) convolutional neural network (CNN), ResNet-18, EfficientNet-B0, and a three-dimensional (3D) CNN. Performance of the DWI-based models in differentiating between benign and malignant breast tumors was compared with that of radiologists assessing standard breast MR images, with diagnostic performance assessed using receiver operating characteristic analysis. The study also examined data augmentation effects (augmentation A: random elastic deformation, augmentation B: random affine transformation and random noise, and augmentation C: mixup) on model performance. Results: A total of 334 breast lesions in 293 patients (mean age, 54.9 years ± 14.3 [SD]; all female) were analyzed. The 2D CNN models outperformed the 3D CNN on the test dataset (area under the receiver operating characteristic curve [AUC] with different data augmentation methods: range, 0.83–0.88 vs 0.75–0.76). There was no evidence of a difference in performance between the small 2D CNN with augmentations A and B (AUC: 0.88) and the radiologists (AUC: 0.86) on the test dataset (P =.64). When comparing the small 2D CNN to radiologists, there was no evidence of a difference in specificity (81.4% vs 72.1%, P =.64) or sensitivity (85.9% vs 98.8%, P =.64). Conclusion: AI models, particularly a small 2D CNN, showed good performance in differentiating between malignant and benign breast tumors using DWI, without needing manual segmentation.

DOI： 10.1148/ryai.240206

Web of Science

Scopus

PubMed

Language：English   Publisher：Japanese Society for Magnetic Resonance in Medicine  

<p>Purpose: To assess the institutional variability in ultrafast dynamic contrast-enhanced (UF-DCE) breast MRI using time-resolved angiography with stochastic trajectories (TWIST)-volumetric interpolated breath-hold examination (VIBE) and compressed sensing (CS)-VIBE sequences acquired at 2 different institutions with different patient populations and contrast injection protocols.</p><p>Methods: UF-DCE MR images of 18 patients from site A acquired using a TWIST-VIBE sequence, and UF-DCE MR images of 18 patients from site B acquired with a CS-VIBE sequence, were retrospectively evaluated and compared. The 2-site patient cohort was matched for patient age, background parenchymal enhancement, malignancy or benignity, and lesion size. Qualitative assessments included noise, blurring, poor fat suppression, aliasing artifact, motion artifact, lesion conspicuity, lesion morphology, time-intensity-curve smoothness, and vessel delineation. For quantitative assessment, the bolus arrival time was evaluated for each lesion, and its diagnostic performance in discriminating between benign and malignant lesions was examined using receiver operating characteristics analysis.</p><p>Results: Thirteen malignant and five benign lesions were included from each site. Qualitative evaluation revealed that poor fat suppression and aliasing artifacts were visible in images from site A with TWIST-VIBE (<i>P</i> = 0.004 and <i>P</i> < 0.001), whereas motion artifacts were present in images from site B with CS-VIBE (<i>P</i> = 0.04). Lesion morphology assessments (<i>P </i>< 0.001) and vessel delineation (<i>P</i> < 0.001) were superior for images from site B with CS-VIBE. Bolus arrival time was significantly longer with TWIST-VIBE than with CS-VIBE, for both benign and malignant lesions (<i>P</i> < 0.001). The area under the receiver operating characteristics curve was 0.55 for site A and 0.69 for site B (<i>P</i> = 0.39).</p><p>Conclusion: Both acquisitions allowed evaluation of breast lesions with good lesion conspicuity and time-intensity-curve smoothness, whereas CS-VIBE was superior to TWIST-VIBE for morphological evaluation of breast lesions and depiction of blood vessels in the breast. Injection rate appears to have a significant impact on semi-quantitative parameters derived from UF-DCE MRI.</p>

DOI： 10.2463/mrms.mp.2024-0152

Web of Science

PubMed

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