Purpose: The ability to accurately detect and characterize intramammary micro- and macrocalcifications without ionized radiation has significant clinical implications for early breast cancer assessment. The aim of this prospective study was to investigate the feasibility of detecting intramammary calcifications using 3D multi-echo gradient echo (ME-GRE) magnitude and true susceptibility-weighted images (tSWI) compared to digital mammography (DM) in patients with different breast sizes and densities of breast parenchyma at 1.5T.

Methods: Two board-certified radiologists evaluated digital mammograms of 70 patients and compared them to the corresponding SW-based images concerning detection of intramammary calcifications and determination of their sizes. The clinical performance of both SW-based techniques in accurately detecting and assessing intramammary calcifications was determined. In addition an interobserver agreement was performed.

Results: Compared to DM, visualization of calcification lesions using both SW-based techniques is influenced by: 1) Morphology (shape and homogeneity) of calcifications; 2) Spatial arrangement of calcifications. Closely adjacent calcifications may not be distinguishable in the SW-based images as individual lesions. Overlapping susceptibility effects resulted in a single, larger lesion; 3) The (in-phase or out-of-phase cycle) echo time value selected in the imaging protocol plays a crucial role in visualizing the lesions; and 4) Lesion size was overestimated by up to 2.0 mm when comparing SW-based techniques to DM. An almost perfect interobserver agreement was found for the analysis. Microcalcifications could not be visualized. A sensitivity of 13.0% and specificity of 99.7% for the diagnosis of macrocalcifications could be provided with both SW-based techniques.

Conclusion: Our findings highlight the potentials and limitations of SW-based techniques at 1.5 T for characterizing intramammary micro- and macrocalcifications. The high specificity of SW-based techniques in combination with higher magnetic field strengths could revolutionize breast cancer screening and management.

Purpose: This study aims to evaluate the predictive value of MRI features for pathologic complete response (pCR) and survival outcomes in patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NAC).

Methods: A retrospective analysis was conducted on 168 BC patients treated with NAC between 2018 and 2022. Pre-NAC breast MRI scans were evaluated for enhancement patterns, time-intensity curve (TIC), peritumoral edema, and background enhancement. Both pre- and post-NAC MRIs were assessed for Epeak %, mean apparent diffusion coefficient (ADC) value, and ADC ratio (mean ADC of lesion/contralateral normal breast parenchyma). Survival outcomes were analyzed using Kaplan−Meier and Cox regression models.

Results: pCR was achieved in 34% of patients. MRI demonstrated a sensitivity of 74% and a specificity of 86% in predicting pCR, with an overall accuracy of 82%. The post-NAC percentage of initial peak enhancement (Epeak) was significantly lower in the pCR group (P < 0.001). Multivariate analysis identified a pre-NAC Epeak ≤ 96 (hazard ratio [HR]: 6.26, P < 0.001) and a post-NAC Epeak > 188 (HR: 18.40, P < 0.001) as independent risk factors for disease-free survival. Additionally, a lower pre-NAC ADC ratio (≤0.65) was associated with poorer overall survival (HR: 2.8, P: 0.041). Pre-NAC peritumoral edema, background enhancement, and TIC were not significant predictors of survival outcomes.

Conclusion: MRI features, including Epeak % and ADC ratio, are important predictors of pCR and survival outcomes in BC patients undergoing NAC. Incorporating these biomarkers into clinical practice may improve treatment planning and optimize patient outcomes.

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.

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.

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 (P = 0.004 and P < 0.001), whereas motion artifacts were present in images from site B with CS-VIBE (P = 0.04). Lesion morphology assessments (P < 0.001) and vessel delineation (P < 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 (P < 0.001). The area under the receiver operating characteristics curve was 0.55 for site A and 0.69 for site B (P = 0.39).

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.

This review focuses on the management of women at high risk for breast cancer, particularly those with pathogenic variants in BRCA1 and BRCA2, who face elevated lifetime risks. We explore preventive strategies such as risk-reducing surgeries and enhanced screening methods, including MRI. MRI has proved to be an effective tool in early cancer detection, particularly in high-risk populations, and represents a shift toward more personalized and precise screening protocols. Considering the rise in awareness and progress in genetic testing, there is a growing demand for tailored screening methods that balance efficacy, accessibility, and sustainability. Technological advances have resulted in the availability of a range of screening options, and this review highlights the potential for continued innovation in clinical practice and the adoption of strategies that take individual risk factors into account to improve long-term outcomes in breast cancer management.

Purpose: High b-value acquisition and diffusion-weighted imaging with background suppression (DWIBS) are desirable in high-specificity breast cancer diagnosis on non-contrast-enhanced magnetic resonance imaging; however, this inherently results in a lower signal-to-noise ratio (SNR). Compressed sensitivity encoding (C-SENSE), which combines SENSE with compressed sensing, improves the SNR by reducing noise. Recent technological improvements allow us to incorporate this acceleration technique into echo-planar imaging, called echo-planar imaging with C-SENSE (EPICS). This study aimed to compare image quality and reliability of the apparent diffusion coefficient (ADC) between DWIBS obtained using SENSE and EPICS in patients with small breast cancers.

Methods: Thirty-seven patients with pathologically confirmed breast cancer underwent DWIBS, and images were reconstructed using both conventional SENSE (SENSE-DWIBS) and EPICS (EPICS-DWIBS). Two board-certified radiologists independently evaluated lesion conspicuity (LC) and noise using a 5-point grading scale. The same 2 radiologists independently measured SNR, contrast-to-noise ratio (CNR), and the mean cancer ADC. The Pearson coefficient and Bland–Altman plot were applied to assess the accuracy of ADCs.

Results: LC scores were higher with EPICS than with SENSE, reaching significance for one reviewer but not the other reviewer. Noise ratings on visual evaluation were significantly lower with EPICS than with SENSE (P < 0.001 for both reviewers). SNR was significantly higher with EPICS than with SENSE (P < 0.005 for both reviewers). CNR was significantly higher with EPICS than with SENSE (P < 0.001 for both reviewers). Bland–Altman plots of cancer ADCs using EPICS-DWIBS and SENSE-DWIBS showed excellent concordance, with a bias of 0.026 × 10⁻³ mm²/s and limits of agreement ranging 0.054 × 10⁻³ mm²/s; the Pearson’s correlation coefficient was 0.997 (P < 0.0001).

Conclusion: EPICS enhances breast DWIBS image quality, with improved SNR and CNR and reduced noise levels. The ADCs of breast cancers obtained using EPICS were almost perfectly correlated with those obtained using conventional SENSE.

Purpose: The aim of our study was to investigate the technical accuracy of susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM) created to detect intramammary-like calcifications depending on different TEs, volume, and type of calcification samples at 1.5T.

Methods: Jello-embedded particles of blackboard chalk and ostrich eggshell ranging in size from 4 to 25 mm² were used to simulate intramammary calcifications after testing different base substances and calcifications for their suitability to be used in breast phantoms. Breast phantoms were systematically examined using CT and an optimized 3D multi-echo gradient echo pulse sequence with following parameters: TR/TE, 22/1.88–15.52 ms in 1.24 ms increments; reconstructed voxel, 0.5 × 0.5 × 1.1 mm³; receiver bandwidth, 1120 Hz/Px; flip angle, 15°; integrated parallel imaging technique with a GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) factor of 2/24; and a total acquisition time of 3:00 min. A qualitative evaluation of the dependence of the visualization of calcification samples on volume and TE value was followed by a calculation of the SNR, the contrast-to-noise ratio (CNR) and the creation of SWI and QSM in the sense of a (semi)-quantitative analysis of the images.

Results: Jello proved to be a suitable base substance for preparing breast phantoms for SW MRI. Blackboard chalk and ostrich eggshell proved to be suitable for mimicking intramammary-like calcifications. The decrease in the median SNR of the blackboard chalk samples was significantly higher than the corresponding value of the ostrich eggshell samples over the entire TE range (47.5 to 17.0 vs. 16.0 to 6.56, P < 0.0001). The increase in the median CNR of the blackboard chalk samples was significantly higher than the corresponding value of the ostrich eggshell samples over the entire TE range (2.46 to 35.0 vs. 20.2 to 36.8, P = 0.007). With increasing TE value, the signal void volume of the calcification particle increases in the magnitude images as well as in SWI and QSM. Due to the blooming effect, the median gradients of the TE-based changes in signal void volumes were higher in SWI than in magnitude images and in QSM, regardless of the type of calcification particle examined. The maximum magnetic susceptibility of ostrich eggshell samples varied in a TE range of 1.88 to 15.52 ms from −7.2 to −2.51 ppm and that of blackboard chalk from −2.0 to −1.7 ppm. Compared to the manually measured volumes of the calcification particles, both MR-based measurements and CT examinations overestimated the actual sample size. The (non)-significant overestimation in the MRI-data is dependent on the set TE. The CT-based hyperdense volumes were overestimated compared to the corresponding manually measured sample volumes in a range of 109.8%–315.2% for ostrich eggshell samples (P = 0.016) and in a range of 39.9%–156.4% for blackboard chalk samples (P = 0.69).

Conclusion: Our systematic in-vitro investigation of magnitude images, SWI, and QSM revealed that various set TE values, different volumes, and compositions of calcifications have a significant impact on visualizing intramammary(-like) calcifications.

The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses. DL’s predictive capabilities for patient-specific outcomes also suggest potential for more personalized treatment strategies. The advancements in DL are pioneering a new era in breast cancer diagnostics, promising more personalized and effective healthcare solutions. Nonetheless, the integration of this technology into clinical practice faces challenges, necessitating further research, validation, and development of legal and ethical frameworks to fully leverage its potential.

Purpose: This study investigated the breast lesion conspicuity and apparent diffusion coefficient (ADC) reliability for three different diffusion-weighted imaging (DWI) protocols: spatiotemporal encoding (SPEN), single-shot echo-planar imaging (SS-EPI), and readout segmentation of long variable echo-trains (RESOLVE).

Methods: Sixty-five women suspected of having breast tumors were included in this study, with 44 lesions (36 malignant, 8 benign) analyzed further. Breast MRI was performed on a 3 Tesla (3T) system (MAGNETOM Prisma, Siemens) equipped with a dedicated 18-channel breast array coil for a phantom and patients. Three DWI protocols—SPEN, SS-EPI, and RESOLVE—were used. SS-EPI was acquired with an in-plane resolution of 2 × 2 mm², a slice thickness of 3 mm, and b-values of 0 and 1000 s/mm². SPEN had a higher in-plane resolution of 1 × 1 mm², a slice thickness of 1.5 mm, and b-values of 0, 850, and 1500 s/mm². RESOLVE was acquired with an in-plane resolution of 1 × 1 mm², a slice thickness of 1.5 mm, and b-values of 0 and 850 s/mm². Lesion conspicuity and ADC values were evaluated.

Results: The average lesion conspicuity scores were significantly higher for RESOLVE (3.54 ± 0.65) than for SPEN (3.07 ± 0.91) or SS-EPI (2.48 ± 0.78) (P < 0.01). The SPEN score was significantly higher than the SS-EPI score (P < 0.01). Phantom measurements indicated marginally lower ADC values for SPEN compared to SS-EPI and RESOLVE across all concentrations. The results revealed that SPEN (b = 0, 850, 1500 sec/mm²) yielded significantly lower ADC values compared to SPEN (b = 0, 850 sec/mm²) in malignant lesions (P < 0.01), with no significant difference observed between SPEN (b = 0, 850 sec/mm²), SS-EPI, and RESOLVE. For benign lesions, no significant difference in ADC values was found between SPEN (b = 0, 850 sec/mm²), SPEN (b = 0, 850, 1500 sec/mm²), SS-EPI, and RESOLVE.

Conclusion: RESOLVE provided the highest lesion conspicuity, and ADC values in breast lesions were not significantly different among sequences ranging b values 850–1000 sec/mm². SPEN with higher b-values (0, 850, 1500 vs. 0, 850 sec/mm²) yielded significantly lower ADC values in malignant lesions, highlighting the importance of b-value selection in ADC quantification.

Purpose: This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images.

Methods: Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV). Qualitative evaluation of anatomical structures, noise, and artifacts in 2D after dDLR and 3D was performed by two radiologists using a five-point Likert scale. All were analyzed statistically. Gwet’s agreement coefficients were also calculated.

Results: The CV of 2D after dDLR was significantly lower than that before dDLR (P < 0.05). Both radiologists rated 2D higher than 3D for all anatomical structures and noise (P < 0.05), except for artifacts. Both Gwet’s agreement coefficients of anatomical structures, noise, and artifacts in 2D and 3D produced nearly perfect agreement between the two radiologists. The evaluation of 2D tended to be more reproducible than 3D.

Conclusion: 2D with parallel imaging, partial Fourier technique, and dDLR was proved to be superior to 3D for depicting shoulder joint structures with lower noise.

Purpose: Gadolinium-based contrast media (GBCM) may affect apparent diffusion coefficient measurements on diffusion-weighted imaging. We aimed at investigating the effect of GBCM and inter-reader variation on intravoxel incoherent motion (IVIM) parameters in breast lesions.

Methods: A total of 89 patients referred to 3T breast MRI with at least one histologically verified lesion were included. IVIM data were acquired using a single-shot echo planar imaging sequence before and after GBCM administration. D (true diffusion coefficient), D* (pseudo-diffusion coefficient) and f (perfusion fraction) were calculated and measured by two readers (R1, R2). Inter-reader and intra-reader agreements were assessed by intraclass correlation coefficients (ICCs) and Bland–Altman plots.

Results: D was comparable before and after GBCM administration and between readers. D* and f decreased after GBCM administration and showed a lower agreement between readers. Intra-reader agreement before and after GBCM administration was almost perfect for D for both R1 and R2 (ICC 0.955 and 0.887). The intra-reader agreement was substantial to moderate for D* (ICC R1 0.708, R2 0.583) and moderate for f (ICC R1 0.529 and R2 0.425). Inter-reader agreement before GBCM administration was almost perfect for D (ICC 0.905), substantial for D* (ICC 0.733), and moderate for f (ICC 0.404); after contrast media administration, it was almost perfect for D (ICC 0.876) and substantial for D* (ICC 0.654) and f (ICC 0.606). Bland–Altman plots revealed no significant bias.

Conclusion: Administration of GBCM seems to have a stronger effect on D* and f values than on D values. This should be considered when applying IVIM in clinical practice.

Purpose: To investigate the predictive performance of radiomic features extracted from breast MRI for upgrade of ductal carcinoma in situ (DCIS) to invasive carcinoma.

Methods: This retrospective study included 71 women with DCIS lesions diagnosed preoperatively by biopsy. All women underwent breast dynamic contrast-enhanced (DCE) MRI of the breast, which included pre-contrast and five post-contrast phases continuously with a time resolution of 60s. Lesion segmentation was performed manually, and 144 radiomic features of the lesions were extracted from T2-weighted images (T2WI), pre-contrast T1-weighted images (T1WI), and post-contrast 1st, 2nd, and 5th phase subtraction images on DCE-MRI. Qualitative features of mammography, ultrasound, and MRI were also assessed. Clinicopathological features were evaluated using medical records. The least absolute shrinkage and selection operator (LASSO) algorithm was applied for features selection and model building. The predictive performance of postoperative upgrade to invasive carcinoma was assessed using the area under the receiver operating characteristic curve.

Results: Surgical specimens revealed 13 lesions (18.3%) that were upgraded to invasive carcinoma. Among clinicopathological and qualitative features, age was the only significant predictive variable. No significant radiomic features were observed on T2WI and post-contrast 2nd phase subtraction images on DCE-MRI. The area under the curves (AUCs) of the LASSO radiomics model integrated with age were 0.915 for pre-contrast T1WI, 0.862 for post-contrast 1st phase subtraction images, and 0.833 for post-contrast 5th phase subtraction images. The AUCs of the 200-times bootstrap internal validations were 0.885, 0.832, and 0.775.

Conclusion: A radiomics approach using breast MRI may be a promising method for predicting the postoperative upgrade of DCIS. The present study showed that the radiomic features extracted from pre-contrast T1WI and post-contrast subtraction images in the very early phase of DCE-MRI were more predictable.

The demand for breast imaging services continues to grow, driven by expanding indications in breast cancer diagnosis and treatment. This increasing demand underscores the potential role of artificial intelligence (AI) to enhance workflow efficiency as well as to further unlock the abundant imaging data to achieve improvements along the breast cancer pathway. Although AI has made significant advancements in mammography and digital breast tomosynthesis, with commercially available computer-aided detection (CAD systems) widely used for breast cancer screening and detection, its adoption in breast MRI has been slower. This lag is primarily attributed to the inherent complexity of breast MRI examinations and also hence the more limited availability of large, well-annotated publicly available breast MRI datasets. Despite these challenges, interest in AI implementation in breast MRI remains strong, fueled by the expanding use and indications for breast MRI. This article explores the implementation of AI in breast MRI across the breast cancer care pathway, highlighting its potential to revolutionize the way we detect and manage breast cancer. By addressing current challenges and examining emerging AI applications, we aim to provide a comprehensive overview of how AI is reshaping breast MRI and improving outcomes for patients.

Breast diffusion weighted MR imaging (DWI) is increasingly used, because it is fast and easy to be added in clinical protocol without contrast agent and provides information of cellularity or tissue microstructure. This review article explores the principles of breast DWI, the standardization of acquisition techniques, and its current clinical applications. We emphasize its role in differentiating benign from malignant lesions, reducing unnecessary biopsies, and discuss the evidence supporting DWI as a potential standalone screening tool. Prognostic indicators derived from DWI parameters and its utility in monitoring treatment responses are discussed. Finally, we look to the future, discussing emerging techniques. This review provides a comprehensive overview of breast DWI’s current status and future potential.

The early detection and treatment of breast cancer is extremely important for extending patients’ outcomes. Breast MRI has high sensitivity for the detection of breast cancer and plays an important role in breast cancer diagnosis and treatment, but conventional dynamic contrast-enhanced (DCE) MRI may be too time-consuming for breast cancer screening purposes. Abbreviated MRI is a technique that can be applied within a short time, as usually only the pre-contrast and first post-contrast images from the dynamic study or additional T2-weighted imaging are used. Abbreviated MRI may thus be suitable for breast cancer screening. In addition, its diagnostic performance for differentiating benign and malignant breast lesions is superior to that of breast tomosynthesis and comparable to that of conventional DCE MRI. The usefulness of abbreviated MRI for patients with a history of breast cancer and in clinical settings has been described, but the specificity of abbreviated DCE MRI is slightly lower than that of conventional DCE MRI. Ultrafast DCE MRI is a technique that obtains kinetic information by capturing multiple time phases in a short time scan in the very early phase after the injection of contrast material. Various parameters, including the maximum slope and time to enhancement can be used to evaluate kinetic information. Based on this kinetic information, ultrafast DCE MRI can differentiate between benign and malignant breast lesions. Since background parenchymal enhancement (BPE) is weak in the very early phase after a contrast media injection, ultrafast DCE MRI is also useful for identifying lesions in patients with marked BPE on conventional DCE MRI. In addition, ultrafast DCE MRI is useful for predicting the prognostic marker status of breast cancer, assessing the effectiveness of neoadjuvant therapy, examining MRI-detected lesions before surgery, and morphological assessments.

Purpose: To investigate the characteristics of suspicious MRI-only visible lesions and to explore the validity of subcategorizing these lesions into the following two groups: lesions that would require immediate biopsy (4Bi) and lesions for which careful clinical follow-up could be recommended (4Fo).

Methods: A retrospective review of 108 MRI-only visible lesions in 106 patients who were diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 between June 2018 and June 2022 at our institution was performed by two radiologists. The breast MR images were evaluated according to BI-RADS and additional MRI descriptors (linear ductal, branching, and apparent diffusion coefficient values). The lesions were categorized by previously reported classification systems, and the positive predictive values (PPVs) for the different categories were determined and compared. Subsequently, a new classification system was developed in this study.

Results: The total malignancy rate was 31% (34/108). No significant differences between benign and malignant lesions were identified for focus and mass lesions. For non-mass lesions, linear ductal and heterogeneous internal enhancement suggested a benign lesion (P = 0.0013 and P = 0.023, respectively), and branching internal enhancement suggested malignancy (P = 0.0066). Segmental distribution suggested malignancy (P = 0.0097). However, the PPV of segmental distribution with heterogeneous enhancement was significantly lower than that of category 4 segmental lesions with other enhancement patterns (11% vs. 59%; P = 0.0198).

As a new classification, the distribution of focal, linear, and segmental was given a score of 0, 1, or 2, and the internal enhancement of heterogeneous, linear-ductal, clumped, branching, and clustered-ring enhancement was given a score of 0, 1, 2, 3, and 4, respectively. When categorized using a scoring system, a statistically significant difference in PPV was observed between 4Fo (n = 27) and 4Bi (n = 33) (7% vs. 61%, P = 0.000029).

Conclusion: The new classification system was found to be highly capable of subcategorizing BI-RADS category 4 MRI-only visible non-mass lesions into 4Fo and 4Bi.

Purpose: To evaluate the significant findings of hip periprosthetic joint infection (PJI) using metal-artifact-reduction (MAR) MRI and to compare the MRI results to other clinical markers.

Methods: The results of MRI, including two-dimensional fast-spin echo sequences with increased bandwidth and multi-acquisition variable-resonance image combination selective for hips with orthopedic implants at 1.5T (from April 2014 to November 2021), were retrospectively assessed for imaging findings and diagnostic impressions by two radiologists. Clinical data and courses were also investigated. Univariate and multivariate analyses were performed to identify the significant MRI findings in patients with hip PJI and those who underwent surgical intervention. The MRI impressions were compared with other clinical markers in diagnosing hip PJI.

Results: Thirty-seven hip joints in 24 Asian patients (age = 73.9 ± 10.8 years; 18 females) were included. Twelve hip joints (32%) had PJI; seven underwent a surgical intervention. The significant findings for hip PJI included periosteal edema of the acetabulum, intermuscular edema, intramuscular fluid collection, and lymphadenopathy (P < 0.05). In the cases with surgical intervention, the significant findings included capsular distension, capsular thickening, an osteolysis-like pattern of the femur, subcutaneous fluid collection, and lymphadenopathy (P < 0.05). The MRI impressions had high diagnostic significance for both hip PJI cases and those with surgical intervention (P < 0.001). The MRI impression was more significant for hip PJI than the other clinical markers (P < 0.05), while the other clinical markers were more significant in the cases with surgical intervention (P < 0.05).

Conclusion: The significant findings in the hip PJI cases included acetabular periosteal edema, intermuscular edema, intramuscular fluid collection, and lymphadenopathy. The significant findings in the cases with surgical intervention included capsular distention, capsular thickening, a femoral osteolysis-like pattern, subcutaneous fluid collection, and lymphadenopathy. The utilization of MAR MRI demonstrated great diagnostic significance for hip PJI.

Purpose: Adipocytes around aggressive breast cancer (BC) are less lipid different from naive adipocytes (cancer-associated adipocytes, CAAs), and peritumoral edema caused by the release of cytokines from CAAs can conduce to decrease the peritumoral fat proportion. The purpose of this study was to correlate peritumoral fat content identified by using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with lymph node metastasis (LNM) and recurrence-free survival (RFS) in BC patients and to compare with T2-weighted (T2WI) and diffusion-weighted images (DWI) analyses.

Methods: This retrospective study consisted of 85 patients who were diagnosed with invasive carcinoma of breast and underwent breast MRI, including IDEAL before surgery. The scan time of fat fraction (FF) map imaging using IDEAL was 33s. Four regions of interest (ROIs), which are 5 mm from the tumor edge, and one ROI in the mammary fat of the healthy side were set on the FF map. Then average peritumoral FF values (TFF), average FF values on the healthy side (HFF), and peritumoral fat ratio (PTFR, which is defined as TFF/HFF) were calculated. Tumor apparent diffusion coefficient (ADC) values were measured on ADC map obtained by DWI. Peritumoral edema was classified into three grades based on the degree of signal intensity around the tumor on T2WI (T2 edema).

Results: The results of stepwise logistic regression analysis for four variables (TFF, PTFR, T2 edema, and ADC value) indicated that TFF and T2 edema were significant factors of LNM (P < 0.01). RFS was significantly associated with TFF (P = 0.016), and 47 of 49 (95.9%) patients with TFF more than 85.5% were alive without recurrence.

Conclusion: Peritumoral fat content identified by using IDEAL is associated with LNM and RFS and may therefore be a useful prognostic biomarker for BC.

Purpose: We aimed to investigate the diagnostic feasibility of an adjusted diffusion-weighted imaging (DWI) lexicon using multiple b values to assess breast lesions according to DWI-based breast imaging reporting and data system (BI-RADS).

Methods: This Institutional Review Board (IRB)-approved prospective study included 127 patients with suspected breast cancer. Breast MRI was performed using a 3T scanner. Breast DW images were acquired using five b-values of 0, 200, 800, 1000, and 1500 s/mm² (5b-value DWI) on 3T MRI. Two readers independently assessed lesion characteristics and normal breast tissue using DWI alone (5b-value DWI and 2b-value DWI with b = 0 and 800 s/mm²) according to DWI-based BI-RADS and in combination with the standard dynamic contrast-enhanced images (combined MRI). Interobserver and intermethod agreements were assessed using kappa statistics. The specificity and sensitivity of lesion classification were evaluated.

Results: Ninety-five breast lesions (39 malignant and 56 benign) were evaluated. Interobserver agreement for lesion assessment on 5b-value DWI was very good (k ≥ 0.82) for DWI-based BI-RADS categories, lesion type, and mass characteristics; good (k = 0.75) in breast composition; and moderate (k ≥ 0.44) in background parenchymal signal (BPS) and non-mass distribution. Intermethod agreement between assessments performed using either 5b-value DWI or combined MRI was good-to-moderate (k = 0.52–0.67) for lesion type; moderate (k = 0.49–0.59) for DWI-based BI-RADS category and mass characteristics; and fair (k = 0.25–0.40) for mass shape, BPS, and breast composition. The sensitivity and positive predictive values (PPVs) for 5b-value DWI were 79.5%, 84.6% and 60.8%, 61.1% for each reader, respectively; 74.4%, 74.4% and 63.0%, 61.7% for 2b-value DWI; and 97.4%, 97.4% and 73.1%, 76.0% for combined MRI. The specificity and negative predictive values (NPVs) were 64.3%, 62.5% and 81.8%, 85.4% for 5b-value DWI; 69.6%, 67.9% and 79.6%, 79.2% for 2b-value DWI; and 75.0%, 78.6% and 97.7%, 97.8% for combined MRI.

Conclusion: Good observer agreement was observed in the 5b-value DWI. The 5b-value DWI based on multiple b-values might have the potential to complement the 2b-value DWI; however, their diagnostic performance tended to be inferior to that of combined MRI for the characterization of breast tumors.