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This study aims to comprehensively evaluate the clinical utility of five diffusion models, including conventional mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential (SEM), and continuous-time random-walk (CTRW), for preoperatively predicting of breast lesion pathology, prognostic biomarkers, and molecular subtypes. We retrospectively analyzed 132 patients with pathologically verified breast lesions (41 benign and 91 malignant) who underwent a full protocol preoperative breast MRI protocol, including a diffusion-weighted imaging (DWI) sequence with nine b values (0 to 2000 s/mm 2 ) on a 3.0T MR scanner. The diffusion parameters from each model-Mono (ADC), IVIM (D, D*, f), DKI (MD, MK), SEM (DDC, α) and CTRW (D m , α, β)-were quantitatively calculated and compared between benign and malignant breast lesions, as well as across different prognostic biomarker statuses in breast cancer, using Mann-Whitney U-tests. For molecular subtypes comparisons, we employed the Kruskal-Wallis test followed by Bonferroni. All parameters, except IVIM-D*, significantly differentiated benign from malignant lesions. Notably, IVIM-D and DKI-MK values were significantly different between estrogen receptor (ER)-positive and ER-negative tumors. Progesterone receptor (PR)-positive cancers exhibited lower Mono-ADC, IVIM-D, DKI-MD, SEM-DDC, CTRW-D m , and CTRW-α values, alongside higher DKI-MK value compared to PR-negative cancers (p < 0.05). Significant differences in IVIM-D, IVIM-D*, and DKI-MK values were observed between human epidermal growth factor receptor 2 (HER2)-negative and HER2-positive tumors. Furthermore, higher SEM-α and CTRW-β values, along with lower DKI-MD and SEM-DDC values, were noted in the high Ki-67 expression group compared to the low Ki-67 group (p < 0.05). All five diffusion models proved valuable for breast cancer diagnosis, with the CTRW model exhibiting the highest diagnostic performance, although the difference was not statistically significant. The diffusion parameters derived from these models can effectively assist in distinguishing prognostic factors and molecular subtypes of breast cancer., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective: To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis., Methods: This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve., Results: The D FROC -median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter α CTRW -median generated a statistically higher AUC compared to the spatial heterogeneity parameter β CTRW -median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172)., Conclusions: The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters., Competing Interests: Declaration of competing interest All authors have approved the manuscript for submission and without any potential competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue., Methods: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C 1 , C 2 , C 3 , C 1 C 2 , F 1 , F 2 , F 3 , F 1 F 2 ) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model., Results: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C 2 ) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C 1 , C 2 , C 1 C 2 , F 1 , F 2 , F 3 ) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F 1 , C 1 C 2 , and C 2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively)., Conclusions: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions., (© 2024. The Author(s).)

Background: Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable., Purpose: To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images., Study Type: Retrospective., Subjects: A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results., Field Strength/sequence: A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm 2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence., Assessment: In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort., Statistical Tests: Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant., Results: For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635., Data Conclusion: Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions., Evidence Level: 4., Technical Efficacy: Stage 2., (© 2023 International Society for Magnetic Resonance in Medicine.)

Background: The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported., Purpose: To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer., Study Type: Retrospective., Population: One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer., Field Strength/sequence: Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T., Assessment: The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed., Statistical Tests: Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant., Results: The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of α mean and β mean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes., Data Conclusion: Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer., Evidence Level: 4 TECHNICAL EFFICACY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer., Purpose: To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer., Study Type: Retrospective., Population: Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions., Field Strength/sequence: 3-T; RSI-based DWI sequence with echo-planar imaging technique., Assessment: The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f 1 , hindered diffusion f 2 , free diffusion f 3 , and signal fractions f 1 f 2 ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer., Statistical Tests: Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant., Results: Malignant breast lesions showed significantly lower ADC and f 3 values, and significantly higher f 1 and f 1 f 2 values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f 3 value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f 1 , f 3 , and f 1 f 2 values were significantly different progesterone receptor (PR)-positive and PR-negative status., Data Conclusion: The RSI-derived parameters (f 1 , f 3 , and f 1 f 2 ) may facilitate the differential diagnosis between benign and malignant breast lesions., Level of Evidence: 4 TECHNICAL EFFICACY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Purpose: To build an automatic computer-aided diagnosis (CAD) pipeline based on multiparametric magnetic resonance imaging (mpMRI) and explore the role of different imaging features in the classification of breast cancer., Materials and Methods: A total of 222 histopathology-confirmed breast lesions, together with their BI-RADS scores, were included in the analysis. The cohort was randomly split into training (159) and test (63) cohorts, and another 50 lesions were collected as an external cohort. An nnUNet-based lesion segmentation model was trained to automatically segment lesion ROI, from which radiomics features were extracted for diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and contrast-enhanced (DCE) pharmacokinetic parametric maps. Models based on combinations of sequences were built using support vector machine (SVM) and logistic regression (LR). Also, the performance of these sequence combinations and BI-RADS scores were compared. The Dice coefficient and AUC were calculated  to evaluate the segmentation and classification results. Decision curve analysis (DCA) was used to assess clinical utility., Results: The segmentation model achieved a Dice coefficient of 0.831 in the test cohort. The radiomics model used only three features from diffusion coefficient (ADC) images, T2WI, and DCE-derived kinetic mapping, and achieved an AUC of 0.946 [0.883-0.990], AUC of 0.842 [0.6856-0.998] in the external cohort, which was higher than the BI-RADS score with an AUC of 0.872 [0.752-0.975]. The joint model using both radiomics score and BI-RADS score achieved the highest test AUC of 0.975 [0.935-1.000], with a sensitivity of 0.920 and a specificity of 0.923., Conclusion: Three radiomics features can be used to construct an automatic radiomics-based pipeline to improve the diagnosis of breast lesions and reduce unnecessary biopsies, especially when using jointly with BI-RADS scores., (© 2023. Italian Society of Medical Radiology.)

Purpose: Lysophosphatidic acid (LPA) is a bioactive molecule which participates in many physical and pathological processes. Although LPA receptor 6 (LPAR6), the last identified LPA receptor, has been reported to have diverse effects in multiple cancers, including breast cancer, its effects and functioning mechanisms are not fully known., Methods: Multiple public databases were used to investigate the mRNA expression of LPAR6, its prognostic value, and potential mechanisms in breast cancer. Western blotting was performed to validate the differential expression of LPAR6 in breast cancer tissues and their adjacent tissues. Furthermore, in vitro experiments were used to explore the effects of LPAR6 on breast cancer. Additionally, TargetScan and miRWalk were used to identify potential upstream regulating miRNAs and validated the relationship between miR-27a-3p and LPAR6 via real-time polymerase chain reaction and an in vitro rescue assay., Results: LPAR6 was significantly downregulated in breast cancer at transcriptional and translational levels. Decreased LPAR6 expression in breast cancer is significantly correlated with poor overall survival, disease-free survival, and distal metastasis-free survival, particularly for hormone receptor-positive patients, regardless of lymph node metastatic status. In vitro gain and loss-of-function assays indicated that LPAR6 attenuated breast cancer cell proliferation. The analyses of TCGA and METABRIC datasets revealed that LPAR6 may regulate the cell cycle signal pathway. Furthermore, the expression of LPAR6 could be positively regulated by miR-27a-3p. The knockdown of miR-27a-3p increased cell proliferation, and ectopic expression of LPAR6 could partly rescue this phenotype., Conclusion: LPAR6 acts as a tumor suppressor in breast cancer and is positively regulated by miR-27a-3p., (© 2021. The Author(s).)

Rationale and Objectives: Intra-peritumoural textural transition (Ipris) is a new radiomics method, which includes a series of quantitative measurements of the image features that represent the differences between the inside and outside of the tumour. This study aimed to explore the feasibility of Ipris analysis for the preoperative prediction of axillary lymph node (ALN) status in patients with breast cancer based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Materials and Methods: This study was approved by the Institutional Review Board (IRB) of our hospital. One hundred sixty-six patients with clinicopathologically confirmed invasive breast cancer and ALN status were enrolled. All patients underwent preoperative breast DCE-MRI examinations. The primary breast lesion was manually segmented using the ITK-SNAP software for each patient. Two sets of image features were extracted, including Ipris features and conventional intratumoural features. Feature selection was conducted using Spearman correlation analysis and support vector machine with recursive feature elimination (SVM-RFE). Next, three models were established in training dataset: Model 1 was established by Ipris features; Model 2 was established by intratumoural features; Model 3 was established by combining Ipris features and intratumoural features. The performances of the three models were evaluated for the prediction of ALN status in testing datasets., Results: Model 1 with four Ipris features achieved an AUC of 0.816 (95% CI, 0.733-0.883) and 0.829 (95% CI, 0.695-0.922) in the training and testing datasets, respectively. Model 2 with six intratumoural features achieved an AUC of 0.801 (95% CI, 0.716-0.870) and 0.824 (95% CI, 0.689-0.918) in the training and testing datasets, respectively. By incorporating the Ipris and intratumoural features, the AUC of Model 3 increased to 0.968 (95% CI, 0.916-0.992) and 0.855 (95% CI, 0.724-0.939) in the training and testing datasets, respectively., Conclusion: Ipris features based on DCE-MRI can be used to predict ALN status in patients with breast cancer. The model combining intratumoural and Ipris features achieved higher prediction performance., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Abstract: To evaluate diagnostic performance of perfusion-weighted imaging in differentiating benign from malignant breast lesions, and the correlation between the prognostic factors/subtypes of breast cancers and the perfusion parameters.A total of 76 patients (59 cases with breast cancer) were included in our study. The Wilcoxon rank-sum test or the Kruskal-Wallis test were adopted for comparisons according to the dichotomous histopathologic prognostic factors or immunohistochemical subtypes. Receiver operating characteristic curves were used to determine the area under the curve (AUC) values for perfusion parameters to assess discrimination ability.Confirming by pathology after operation, the percentage of benign lesions is 22.37% (17/76), malignant lesions (breast cancer) is 77.63% (59/76). According to puncture and pathological findings after operation, the standard of the molecular subtypes of breast cancer, triple negative account for 13.6% (8/59), non-triple negative account for 86.4% (51/59). The value of mean Ktrans and Kep were lower in benign than malignant lesions (P ≤ .001). The AUC of the 3 indicators are significantly improved after adjusting for age (AUC = 0.858 for Ktrans, AUC = 0.926 for Kep, and AUC = 0.827 for Ve). Moreover, the Ve index showed better discrimination performance than other indicators in identifying patients with triple-negative subtypes. Similarly, the identification ability came to the highest when combing Kep and Ve.Perfusion parameters on dynamic enhanced magnetic resonance imaging are statistically significant in distinguishing benign from malignant breast lesion, and may potentially be used as biomarkers in discriminating patients with triple-negative molecular subtypes of breast cancer., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: To investigate the additional value of DKI in discriminating suspicious breast lesions on DCE-MRI, as compared with conventional DWI; and to explore connection between DKI-parameters and prognostic factors of breast cancers., Methods: The institutional review board approved this retrospective study and written informed consent was waived. Totally, 300 women (mean age, 43.2 ± 10.4 years) with suspicious breast lesions on DCE-MRI were enrolled from November 2014 to September 2019. With pathology as reference, performance of ADC, K app and D app in discriminating suspicious breast lesions were analyzed by receiver operating characteristic (ROC) analysis with area under ROC curve (AUC). The specificities of parameters were compared by Chi-square test. The ADC, K app and D app of breast cancers with different receptor status were compared using Student's t or Mann-Whitney U or Kruskal-Wallis test., Results: There were 344 suspicious breast lesions (220 malignant, 124 benign) in 300 women. No significant differences were found for AUCs of ADC and DKI-parameters in discriminating suspicious breast lesions (0.882 vs. 0.888, p = 0.480). The specificities were significantly higher with ADC and D app than that with DCE-MRI (p = 0.003 and 0.005). The ADC, K app and D app were correlated with HER2 expression and lymph node status, and ADC and K app differed between ER-positive and negative tumors (all p < 0.05). Except K app , DKI/DWI-parameters showed relation with Ki-67 expression. None of the DKI/DWI-parameters showed relation with lesion grade (all p > 0.05)., Conclusion: The more complicated and time-consuming DKI is not superior to conventional DWI in differentiating suspicious breast lesions and reflecting prognostic information of breast cancer., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Hepatocyte growth factor (HGF) signaling plays a plethora of roles in tumorigenesis and progression in many cancer types. As HGF activator inhibitors, serine protease inhibitor, Kunitz types 1 and 2 (SPINT1 and SPINT2) have been reported to be differentially expressed in breast cancer, but their prognostic significance and functioning mechanism remain unclear., Methods: In our study, multiple databases and bioinformatics tools were used to investigate SPINT1/2 expression profiles, prognostic significance, genetic alteration, methylation, and regulatory network in breast carcinoma., Results: SPINT1/2 expression was upregulated in breast cancer, and was relatively higher in human epidermal growth factor receptor 2 (HER2) and node positive patients. Elevated SPINT1/2 expression was significantly correlated with a poorer prognosis. Genetic alterations and SPINT1/2 hypomethylation were observed. In breast carcinoma, SPINT1/2 were reciprocally correlated and shared common co-expressed genes. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that their common co-expressed genes were primarily involved in regulating cell attachment and migration., Conclusions: Our study identified the expression profiles, prognostic significance and potential roles of SPINT1/2 in breast carcinoma. These study results showed that the SPINT1/2 were potential prognostic biomarker for patients with breast cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wu, Yin, Luo, Zhang, Ai, Tian, Jin, Lei and Liu.)

Background: Understanding the main blood supply to the nipple-areola complex (NAC) is important for breast plastic surgery. However, previous reports have involved studies of cadavers and small sample sizes., Objectives: This study aimed to identify and classify the in vivo blood supply to the NAC based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Methods: DCE-MRI images of 393 breasts in 245 Asian women obtained from March 2012 to October 2019 were included retrospectively. Axial, coronal, and sagittal maximum-intensity projection images were evaluated to identify all vessels supplying the NAC. Blood supply to the NAC was classified into 9 anatomic zones: superomedial (Ia), medial (Ib), inferomedial (Ic), superolateral (IIa), lateral (IIb), inferolateral (IIc), central (III), inferior (IV), and superior (V)., Results: A total of 637 source vessels were identified in 393 breasts. Of the 393 breasts, 211 (53.7%) were supplied by a single zone, 132 (33.6%) by 2 zones, 38 (9.7%) by 3 zones, and 12 (3.1%) by 4 zones. Of the 637 vessels, 269 (42.2%) vessels were in zone Ia, 180 (28.3%) vessels were in zone IIa, and <10% of vessels were in the other zones. The number of NAC perfusion zones (P = 0.093) and the distribution of source vessels (P = 0.602) did not differ significantly between the left and right breasts., Conclusions: DCE-MRI provides a clear indication of the blood supply to the NAC. Blood vessels from the superomedial and superolateral zones were the predominant sources of blood supplying the NAC., (© 2021 The Aesthetic Society. Reprints and permission: journals.permissions@oup.com.)

Objectives: To investigate the feasibility and effectiveness of SMS rs-EPI for evaluating breast lesions., Methods: This prospective study was approved by IRB. Ninety-six patients had 102 histopathologically verified lesions (80 malignant and 22 benign) that were evaluated. Conventional rs-EPI and SMS rs-EPI data were acquired on a 3T scanner. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were quantitatively calculated for each lesion on both sequences. Images were qualitatively and quantitatively analyzed with respect to image sharpness, geometric distortion, lesion conspicuity, anatomic structure, overall image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Student's t test, Pearson correlation, receiver operating characteristic curve, Wilcoxon rank sum test, and paired-sample t tests were used for statistical analysis., Results: Compared to conventional rs-EPI, the acquisition time of SMS rs-EPI was markedly reduced (2:17 min vs 4:27 min). Pearson's correlations showed excellent linear relationships for each parameter between conventional rs-EPI and SMS rs-EPI (MK, r = 0.908; MD, r = 0.938; and ADC, r = 0.975; p < 0.01 for all). Furthermore, SMS rs-EPI had similar diagnostic performance compared with conventional rs-EPI. SMS rs-EPI had comparable visual image quality as conventional rs-EPI, with excellent inter-reader reliability (ICC = 0.851-0.940). No differences existed between conventional rs-EPI and SMS rs-EPI for either SNR or CNR (p > 0.05)., Conclusions: Applying the SMS technique can significantly reduce the acquisition time and produce similar diagnostic accuracy while generating comparable image quality as the conventional rs-EPI., Key Points: • SMS rs-EPI reduces scan time from 4:27 min to 2:17 min compared with conventional rs-EPI. • SMS rs-EPI has a comparable diagnostic performance to conventional rs-EPI in the differentiation between malignant and benign breast lesions. • SMS rs-EPI demonstrates comparable image quality to conventional rs-EPI with shorter scan time.

Objective: The study is to assess the accuracy and reliability of 3D simulated magnetic resonance imaging with SPACE sequence for estimating implant volume and reconstructing implant deformation, which may assist in the diagnosis of implant complications and making individualized surgical plans for these patients., Methods: MRI examinations of ten silicone implants were performed with T2, H 2 O-excitation SPACE sequence (T2-spc-H 2 O) and silicone-excitation SPACE sequence (T2-spc-Silicone) to find the most accurate method to estimate implant volume by ITK-SNAP. The effect of implant deformation and voxel size of silicone-excitation SPACE sequence on volume measurement was investigated. Thirteen normal patients and ten patients with implant complications (Wuhan Tongji Hospital from March 2017 to May 2019) were enrolled for testing the accuracy and reliability of 3D simulated MRI with silicone-excitation SPACE sequences for volume measurement and reconstructing implant deformation in patients., Results: The absolute volume differences of T2-spc-Silicone group were significantly less than T2-spc-H 2 O and T2 group (6.28 vs. 23.27 vs. 42.19 mL, P < 0.05) in vitro. No significant difference was found between the normality group and the deformation group for estimating the volume of implants. Besides, the voxel size of T2-spc-Silicone from 0.5 × 0.5 × 0.5 mm to 5.0 × 5.0 × 5.0 mm did not significantly affect the accuracy of volume measurement of the implants in deformation state. However, 3D images of the implant became blurred with the voxel size increased. With the voxel size larger than 1.5 × 1.5 × 1.5 mm, the scores of image quality decreased significantly. The number of folds could not be identified accurately with the voxel size larger than 2.0 × 2.0 × 2.0 mm. In normal patients, the measurement errors of T2-spc-Silicone were around 10 mL. In the patients with implant complications, there was no significant difference between measured volume and the actual volume of implants. Moreover, implant deformations were clearly presented by T2-spc-Silicone with the voxel size of 1.0 × 1.0 × 1.0 mm. The results showed excellent intraobserver reliability (ICC = 0.997 > 0.8), and internal consistency ranged from 0.986 to 0.997 (P < 0.001)., Conclusions: The method to measure implant volume by 3D simulated magnetic resonance imaging with T2-spc-Silicone sequence had possessed desirable accuracy and reliability. The deformation of the implant and the voxel size of the T2-spc-Silicone sequence didn't exhibit a significant effect on the accuracy of the measurement. T2-spc-Silicone with voxel size less than 2.0 × 2.0 × 2.0 mm could be used for 3D reconstruction of the implant deformation. The 1.0 × 1.0 × 1.0 mm was a suitable voxel size to reconstruct implant deformation clearly and quickly., Level of Evidence Iv: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Purpose: To investigate the application of whole-lesion histogram analysis of pharmacokinetic parameters for differentiating malignant from benign breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Materials and Methods: In all, 92 women with 97 breast lesions (26 benign and 71 malignant lesions) were enrolled in this study. Patients underwent dynamic breast MRI at 3T using a prototypical CAIPIRINHA-Dixon-TWIST-VIBE (CDT-VIBE) sequence and a subsequent surgery or biopsy. Inflow rate of the agent between plasma and interstitium (K trans ), outflow rate of agent between interstitium and plasma (K ep ), extravascular space volume per unit volume of tissue (v e ) including mean value, 25th/50th/75th/90th percentiles, skewness, and kurtosis were then calculated based on the whole lesion. A single-sample Kolmogorov-Smirnov test, paired t-test, and receiver operating characteristic curve (ROC) analysis were used for statistical analysis., Results: Malignant breast lesions had significantly higher K trans , K ep , and lower v e in mean values, 25th/50th/75th/90th percentiles, and significantly higher skewness of v e than benign breast lesions (all P < 0.05). There was no significant difference in kurtosis values between malignant and benign breast lesions (all P > 0.05). The 90th percentile of K trans , the 90th percentile of K ep , and the 50th percentile of v e showed the greatest areas under the ROC curve (AUC) for each pharmacokinetic parameter derived from DCE-MRI. The 90th percentile of K ep achieved the highest AUC value (0.927) among all histogram-derived values., Conclusion: The whole-lesion histogram analysis of pharmacokinetic parameters can improve the diagnostic accuracy of breast DCE-MRI with the CDT-VIBE technique. The 90th percentile of K ep may be the best indicator in differentiation between malignant and benign breast lesions., Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2018;47:91-96., (© 2017 International Society for Magnetic Resonance in Medicine.)