Breast Magnetic Resonance Imaging Examinations Research Articles

The Relationship between Breast Cancer Subtypes, Prognostic Factors, and Apparent Diffusion Coefficient Histogram Analysis.

Diffusion Magnetic Resonance Imaging (MRI) is a useful method to evaluate tumor biology and tumor microstructure. The apparent diffusion coefficient (ADC) value correlates negatively with the cellular density of the tumor. This study aimed to investigate the effectiveness of the ADC histogram analysis in showing the relationship between breast cancer prognostic factors and ADC parameters. This study is a retrospective observational descriptive study. ADC histogram parameters were evaluated in all tumor volumes of 67 breast cancer patients. Minimum, 5, 10, 25, 50, 75, 90, 95 percentiles, maximum, mean, median ADC values, kurtosis, and skewness were calculated. Breast MRI examinations were performed on a 3T MR scanner. We evaluated the fibroglandular tissue density of bilateral breasts, background enhancement, localization of masses, multifocality-multicentricity, shape, rim, internal contrast enhancement, and kinetic curve on breast MRI. BIRADS scoring was performed according to breast MRI. Pathologically, histologic type, histologic grade, HER 2, Ki 67, ER-, and PR status were evaluated. A significant correlation was found between tumor volume and ADC scores. There is a significant correlation between min ADC values (p< 0.031), max ADC (p< 0.001), and skewness (p< 0.019). A significant correlation was found between tumor kurtosis and lymph nodes (p< 0.029). There was a significant difference in ADC <sub<mean</sub<, ADC<sub<10%</sub<, ADC<sub<25%</sub<, ADC<sub<50%</sub<, ADC<sub<75%</sub<, ADC<sub<90%</sub<, ADC <sub<95%</sub< and ADC<sub<max</sub> values depending on ER-and PRstatus. (for ER p = 0.004, p = 0.018, p = 0.010, p = 0.008, p = 0.004, p = 0.004, p = 0.02, p = 0.02 and p = 0.038, for PR p &#60; 0.001, p = 0.028, p = 0.011, p = 0.001, p < 0.001, p =<0.001, p < 0.001, and p < 0.001, respectively; p < 0.05). These values were lower in ER-and PR-positive status than in ER-and PR-negative receptor status. According to HER2 status, there was a statistically significant difference in ADC<sub<5%</sub< and measurements of the lesions (p = 0.041; p < 0.05). Our study found no significant correlation between other prognostic factors, such as histological grade, Ki-67 indices, and ADC values. Our study found a significant difference between tumor volume, ER- and, PR status, HER2, and lymph node involvement, and some ADC values among prognostic factors for breast cancer. Furthermore, ADC histogram analysis can provide additional value in predicting some prognostic factors.

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI

The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future.

Additional occult cancers identified on staging breast MRI: imaging appearances and pathologic characteristics.

Breast magnetic resonance imaging (MRI) is increasingly being used for staging of patients with breast cancer due to its high sensitivity in detecting additional cancers (ACs). However, the clinical impact of diagnosing and treating these cancers remains unclear. A retrospective study was undertaken of patients with newly diagnosed breast cancer who underwent staging MRI at The American University of Beirut Medical Centre (AUBMC) between 2012 and 2020. Pathology reports and breast MRI examinations were reviewed. Eighteen breast cancer patients with 19 pathology-proven index cancers (ICs) and 19 pathology-proven MRI-detected ACs were included. Chi-square and Fisher's exact tests for categorical variables and Wilcoxon signed rank test for numerical variables were used to compare ICs to ACs. The ICs consisted of four ductal carcinoma in situ (DCIS), 13 invasive ductal carcinomas (IDC), of which five with associated DCIS, and two invasive lobular carcinomas, (ILC) of which one with associated DCIS. ACs comprised 12 DCIS, five IDC, two with associated DCIS and two ILC, one with associated DCIS. Interval cancers were more frequently invasive whereas ACs were more frequently in situ (P = 0.021). ACs were more frequently nuclear grade 2 (P = 0.009). There was no statistically significant difference between ICs and ACs in lesion type (P = 0.062), shape (P = 0.073), initial enhancement (P = 1), delayed enhancement (P = 0.732), hormonal receptor profile (P = 0.68) and Ki67 (P = 0.388). Among ACs, ten (53%) were larger than 10 mm of which five (26%) were invasive cancers, and five (26%) were larger than the ICs. ACs detected by breast MRI were more likely to be in situ and to show a nuclear grade 2. Although not reaching statistical significance, some ACs tend to be clinically significant by their type, size or nuclear grade. The impact on clinical management remains to be determined.

Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model.

The aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers. In this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists. In the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool. Our automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment.

The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI). To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs. Retrospective. Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days). 3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging. Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation. Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE). The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N=33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days). A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion. 3 TECHNICAL EFFICACY: Stage 2.

How to optimize breast MRI protocol? The value of combined analysis of ultrafast and diffusion-weighted MRI sequences

PurposeThe purpose of this retrospective study was to demonstrate the validity of early enhancement criteria on ultrafast magnetic resonance imaging (MRI) sequence to predict malignancy in a large population, and the benefit of diffusion-weighted imaging (DWI) to improve the performance of breast MRI. Material and methodsWomen who underwent breast MRI examination between April 2018 and September 2020 and further breast biopsy were retrospectively included. Two readers quoted the different conventional features and classified the lesion according to the BI-RADS classification based on the conventional protocol. Then, the readers checked for the presence of early enhancement (≤ 30 s) on ultrafast sequence and the presence of an apparent diffusion coefficient (ADC) ≥ 1.5 × 10−3 mm2/s to classify the lesions based on morphology and these two functional criteria only. ResultsTwo hundred fifty-seven women (median age: 51 years; range: 16–92 years) with 436 lesions (157 benign, 11 borderline and 268 malignant) were included. A MRI protocol plus two simple functional features, early enhancement (≤ 30 s) and an ADC value ≥ 1.5 × 10−3 mm2/s, had a greater accuracy than the conventional protocol to distinguish benign from malignant breast lesions with or without ADC value (P = 0.01 and P = 0.001, respectively) on MRI, mainly due to better classification of benign lesions (increased specificity) with increasing diagnostic confidence of 3.7% and 7.8% respectively. ConclusionBI-RADS analysis based on a simple short MRI protocol plus early enhancement on ultrafast sequence and ADC value has a greaterr diagnostic accuracy than a conventional protocol and may avoid unnecessary biopsy.

Type 2 dynamic curves: A diagnostic dilemma.

BACKGROUNDMagnetic resonance imaging (MRI) with multiparametric dynamic contrast plays a critical role in the assessment of breast lesions. Dynamic curves are a critical parameter in determining the benign or malignant nature of lesions. Dynamic curves of type 1 are known to represent benign masses, while dynamic curves of type 3 are known to identify malignant masses. Type 2 dynamic curves have a sensitivity of 42.6% and specificity of 75% for malignancy detection. AIMTo investigate the pathological diagnosis of lesions with type 2 dynamic curves.METHODSWe evaluated breast MRI examinations performed between 2020 and 2021 retrospectively and included lesions with type 2 dynamic curves. We included 38 lesions from 33 patients. The lesions were evaluated for their pathological diagnosis and morphological characteristics. RESULTSTwenty-six lesions were malignant, while twelve were benign. The most frequently encountered benign lesion (7/12, 58.3%) was sclerosing adenosis, while the most frequently encountered malignant diagnosis was invasive ductal cancer. The presence of a type 2 dynamic curve had a sensitivity of 40.2% and specificity of 73.4% for predicting malignancy. By combining type 2 curves and morphological features, the sensitivity and specificity were increased.CONCLUSIONThe high rates of malignancy detected histopathologically among patients with type 2 dynamic curves in our study are remarkable. Type 2 dynamic curves can be detected in benign breast masses, especially in sclerosing adenosis cases. Considering morphological features can increase the diagnostic accuracy in cases with type 2 dynamic curves.

Management of MRI-Detected Benign Internal Mammary Lymph Nodes.

Introduction In this retrospective study, we aimed to evaluate benign internal mammary lymph nodes (IMLNs) in terms of frequency, number, size, long axis/short axis (L/S) ratio, intercostal location, presence of fatty hilum, and stability using breast magnetic resonance imaging (MRI) and discuss the findings by reviewing existing literature. Methods This single-center study consisted of 130 women between the ages of 24 and 76 years, who had at least two breast MRI examinations in our institution, with the latest exam performed between January 1, 2019 and November 1, 2019, were eligible for the study. MRIs of the study group were independently reviewed by two radiologists. Results IMLN was detected in 31.1% of the 427 MRIs, with a total number of 256 nodes. The most common indication for breast MRI was high-risk screening (66.2%). The median number of nodes per patient was 1 (range: 1–6). The median follow-up time was 19.5 months (range: 6–141 months). None of these benign nodes showed significant interval growth. Mean L/R ratio of the nodes was 1.9. One hundred and four nodes ( n = 104, 40.6%) had a L/S ratio less than 2 and 43.2% ( n = 45) of the nodes with a L/S ratio less than 2, had a long axis measuring less than or equal to 3mm. IMLN of patients with breast implants had the largest mean long axis. The fatty hilum was identified in 34.3% ( n = 68) of the 256 nodes. The size of the lymph nodes where fatty hilum was visualized was significantly larger than the ones where fatty hilum was not visualized ( p < 0.001). Fatty hilum could be visualized in only 2.7% of the nodes with a long axis smaller than 3 mm. Conclusion IMLN is a frequent finding on breast MRI. We have shown that benign IMLNs might be large sized in specific cases like patients with breast implants. When small sized (≤3mm), they are more likely to be rounded (L/S ratio <2). The fatty hilum that is a feature of benignity might not be visualized in nodes less than or equal to3mm.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer.

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05). Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

Magnetic resonance imaging and shear wave elastography comparison in the evaluation of breast lesions

This study aims to compare the Magnetic Resonance Imaging (MRI) and Shear Wave Elastography (SWE) data and investigate their contribution to diagnosis when both are individually used and combined. This study included the consent of 46 diagnostic biopsy patients after their breast MRI examination was performed. Mass appearance patients in the sonographic evaluation were SWE examined before the biopsy. In MR examination, lesion localization, size, contrast curves, contrast enhancement features, intensity in the fat-suppressed T2A sequence, Emean value in SWE examination, and histopathological results were examined. Individual and combined use of MRI and SWE findings were evaluated with histopathological results. The diagnosis consistency was compared according to the histopathological results of the malignant-benign defined lesions. Higher sensitivity in MRI; higher specificity and accuracy in SWE were acquired when both methods were compared. The accuracy of MRI improved when MRI use is combined with SWE. The combined use of SWE with MRI increases the diagnostic accuracy in breast lesion characterization. We observed that lesions showing a type 3 enhancement curve and iso-hypointense on the T2W sequence in breast MRI could be predicted to show a stiff elasticity pattern on SWE due to higher elastography values.

Contrast enhanced mammography (CEM) versus magnetic resonance imaging (MRI) for staging of breast cancer: The pro CEM perspective

In women with newly diagnosed breast cancer, preoperative staging is required to assess disease extent, enabling us to decide on the most optimal treatment strategy. For locoregional staging, assessment of intramammary tumor extent and presence of axillary and perhaps also internal mammary lymph node metastases is required. Due to the similarity in the underlying principle, contrast-enhanced mammography is increasingly considered instead of breast MRI for this purpose. When considering the combination of CEM and US as a single appointment imaging strategy for preoperative staging of breast cancer, there is only limited room for an additional benefit of breast MRI. For tumor size measurements, equal performance of both CEM and MRI are observed. Sensitivity of both techniques for detecting breast cancer is comparable, meaning that both techniques are capable of detecting additional ipsilateral or contralateral tumor foci. However, specificity is in favor of CEM, meaning that there is a slightly lower chance of having false positive findings in preoperative staging of the breast. Axillary US can be performed during the same appointment as CEM, with equal performance and limitations as evaluation of the axilla on standard breast MRI examinations. Finally, there is no need to actively pursue the detection of IMLN metastases, meaning that additional MRI to do so is not required. This review provides a ‘pro-CEM’ perceptive on the arguments why breast MRI is hardly necessary when CEM in combination with US has been performed as a single appointment imaging strategy in breast cancer patients.

Trends in screening breast magnetic resonance imaging use among US women, 2006 to 2016.

Supplemental breast cancer screening with breast magnetic resonance imaging (MRI) is recommended for women at high risk of breast cancer. To the authors' knowledge, recent national trends in breast MRI use are unknown. The authors used claims data from a large national insurer to calculate screening breast MRI rates from 2006 to 2016 in a US cohort of 10 million women aged 20 to 64 years. Use was stratified by subgroups of women with a BRCA mutation, family history of breast cancer, and prior breast cancer history and stratified by age. Joinpoint regression evaluated annual changes in trends. The total sample included 37,447 screening breast MRI examinations in 25,617 women. Overall screening breast MRI rates were low and increased from 2.9 to 12.1 examinations per 10,000 women from 2006 to 2016. MRI use in women with a BRCA mutation increased by 21% on average annually from 210.8 per 10,000 women to 1562.0 per 10,000 women from 2006 to 2016. By 2016, women aged 50 to 64 years who had a BRCA mutation had the highest use of breast MRI (1669.6 MRI examinations per 10,000 women) compared with younger women (1198.4 MRI examinations per 10,000 women, 1519.1 MRI examinations per 10,000 women, and 1567.2 MRI examinations per 10,000 women, respectively, among women aged 20-29 years, 30-39 years, and 40-49 years). Women with a BRCA mutation comprised <1% of the current study population but received approximately 9% of screening breast MRI examinations. Breast MRI rates among women with a family history of breast cancer or prior breast cancer history initially increased from 2006 to 2008, but then stabilized or decreased. The increases in breast MRI use observed in the current study have indicated improvements in concordance with breast imaging guidelines. However, women with BRCA mutations remain underscreened, particularly younger women, thereby identifying a clear gap with which to enhance access.

Idiopathic Granulomatous Mastitis and its Mimics on Magnetic Resonance Imaging: A Pictorial Review of Cases from India.

Objectives:Idiopathic granulomatous mastitis (IGM) is a rare inflammatory disease of the breast, which is benign but potentially morbid. Mammographic and sonographic findings have been well characterized, but magnetic resonance imaging (MRI) findings have been less thoroughly documented. The objective of this study was to demonstrate characteristic findings for IGM and its mimics via a retrospective review.Material and Methods:Breast MRI examinations performed at Sir Ganga Ram Hospital in New Delhi, India between 2014 and 2019 were retrospectively reviewed to identify cases in which a pattern suggestive of granulomatous mastitis was seen. Cases of known malignancy were excluded. Any available breast pathology results were then obtained, and cases with presumptive or definitive diagnoses were compiled for analysis.Results:Overall, cases identified with characteristic imaging findings and confirmed diagnosis included seven cases of IGM, four cases of invasive ductal carcinoma, two cases of tuberculous mastitis, one case of non- tuberculous infectious mastitis, one case of foreign body mastitis, and one case of eosinophilc mastitis. One case of IGM with masses rather than of non-mass enhancement was also identified.Conclusion:In our review, cases with clustered ring enhancement were found to have inflammatory, idiopathic, infectious and malignant etiologies. While, these etiologies can only be reliably differentiated on pathology, familiarity with the pattern and an awareness of the differential may lead to decreased morbidity due to delays in diagnosis.

Cardiotoxicity evaluation using magnetic resonance imaging in breast Cancer patients (CareBest): study protocol for a prospective trial

BackgroundCardiovascular disease is second only to cancer recurrence as a determinant of lifespan in cancer survivors, and cancer therapy-related cardiac dysfunction is a clinically important risk factor. We aim to investigate the use of cardiac magnetic resonance imaging (MRI) to evaluate early tissue changes and perform functional assessment of chemo- and radiation-induced cardiotoxicity and to identify MRI prognostic indicators of cardiotoxicity in breast cancer patients.MethodsA 3-min cardiac imaging protocol will be added to the breast MRI examination to diagnose cardiotoxicity in breast cancer patients. Standardized MRI-based evaluation of breast cancer and the left ventricular myocardium will be performed at baseline and at 3, 6, and 12 months and 2 years or more after cancer treatment. We will analyze both ventricular volume and ejection fraction (EF), strain of left ventricle (LV), native T1, extracellular volume fraction (ECV), and T2 values acquired in the mid LV.DiscussionThe primary result of this study will be the comparison of the prognostic value of MRI parameters (native T1, ECV, both ventricular systolic function and LV strain) for cardiotoxicity. The endpoint is defined as the occurrence of a major adverse cardiac event (MACE). The secondary outcome will be an assessment of the temporal relationships between contractile dysfunction and microstructural injury over 4 years using MRI. This study will assess the usefulness of quantitative MRI to diagnose cardiotoxicity and will clarify the temporal relationships between contractile dysfunction and microstructural injury of the LV myocardium using MRI during breast cancer treatment.Trial registrationThe protocol was registered at clinicaltrials.gov (Clinical trial no. NCT03301389) on October 4, 2017.

Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

PurposeThe purpose of this study was to assess the clinical value of conventional magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) features for predicting the risk of axillary lymph node (ALN) metastasis in patients with breast cancer. MethodsThis retrospective study involved 265 patients with breast cancer who underwent 3.0 T breast magnetic resonance imaging examinations prior to surgery and other treatment. Of these, 119 underwent IVIM examination. The features of MRI and IVIM and postoperative pathologic results were collected. The association of MRI features of breast cancer with ALN metastasis were determined by univariate and multivariate analyses. Comparison of IVIM parameters between breast cancer patients with and without ALN metastasis was performed using the Mann–Whitney U test. ResultsAmong the 265 patients, 144 (54.3%) had ALN metastasis, and 121 (45.7%) did not. The size and shape of the tumours, T2WI signal, inhomogeneous enhancement, washout intensity–time curves and the values of slow ADC, fast ADC and fraction of fast ADC parameters were significantly associated with ALN metastasis. The AUC of conventional MRI for diagnosing axillary lymph node metastasis was 0.722. The AUC of MRI combined with slow ADC, fast ADC and fraction of fast ADC parameters that were used to diagnose breast cancer with ALN metastasis were 0.814, 0.803 and 0.900, respectively. ConclusionsThe features of IVIM parameters and conventional MRI can be used to predict the ALN metastasis in patients with breast cancer. MRI combined with fraction of fast ADC showed higher diagnostic efficiency for ALN metastasis in breast cancer than MRI did.

Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection.

To estimate the performance of diffusion-weighted imaging (DWI) for breast cancer detection. Consecutive breast magnetic resonance imaging examinations performed from January to September 2016 were retrospectively evaluated. Examinations performed before/after neoadjuvant therapy, lacking DWI sequences or reference standard were excluded; breasts after mastectomy were also excluded. Two experienced breast radiologists (R1, R2) independently evaluated only DWI. Final pathology or > 1-year follow-up served as reference standard. Mc Nemar, χ2, and κ statistics were applied. Of 1,131 examinations, 672 (59.4%) lacked DWI sequence, 41 (3.6%) had no reference standard, 30 (2.7%) were performed before/after neoadjuvant therapy, and 10 (0.9%) had undergone bilateral mastectomy. Thus, 378 women aged 49 ± 11years (mean ± standard deviation) were included, 51 (13%) with unilateral mastectomy, totaling 705 breasts. Per-breast cancer prevalence was 96/705 (13.6%). Per-breast sensitivity was 83/96 (87%, 95% confidence interval 78-93%) for both R1 and R2, 89/96 (93%, 86-97%) for double reading (DR) (p = 0.031); per-lesion DR sensitivity for cancers ≤ 10mm was 22/31 (71%, 52-86%). Per-breast specificity was 562/609 (93%, 90-94%) for R1, 538/609 (88%, 86-91%) for R2, and 526/609 (86%¸ 83-89%) for DR (p < 0.001). Inter-observer agreement was substantial (κ = 0.736). Acquisition time varied from 3:00 to 6:22min:s. Per-patient median interpretation time was 46s (R1) and 51s (R2). DR DWI showed a 93% sensitivity and 88% specificity, with 71% sensitivity for cancers ≤ 10mm, pointing out a potential for DWI as stand-alone screening method.

Comparison of the utility of clinical breast examination and MRI in the surveillance of women with a high risk of breast cancer

To review surveillance magnetic resonance imaging (MRI) and clinical breast examinations (CBE) performed for women at high risk of breast cancer in order to determine recall and cancer-detection rates. Data were collected on all surveillance MRI examinations performed at St James's Hospital in 2016 for women at high risk of developing breast cancer. Data collected included age, indication for MRI, MRI score, ultrasound indications and scores, and histology findings. Ultrasound scores were recorded from CBEs that received a score of ≥3. A total of 385 breast surveillance MRI examinations and CBEs were performed for women at high risk of breast cancer. A recall rate of 11.2% was documented for breast MRI examinations, whereas a recall rate of 6.2% was identified for CBEs. The biopsy rate was 6.2% for MRI and 0.2% for CBE. The cancer detection rate was 1.6% or 16 per 1,000 for MRI screening and 0% for CBE. The high cancer detection rate in the present study supports the unparalleled sensitivity of breast MRI surveillance. Furthermore, the present study did not identify any breast cancers through CBE, suggesting it is not a critical component of the surveillance programme of high-risk women. The current UK guidelines recommending a target recall rate of 7% were not met in the present study or by other studies in the literature, collectively suggesting the guidelines may not be reflective of what is attainable in clinical practice.

Patterns of Breast Imaging Use Among Women with a Personal History of Breast Cancer.

National patterns of breast imaging in women with a personal history of breast cancer (PHBC) are unknown making evaluation of annual surveillance recommendations a challenge. To describe variation in use of mammography and breast magnetic resonance imaging (MRI) examinations beginning 6months after diagnosis among women with PHBC in US community practice. We report on the breast imaging indication, imaging intervals, and time since breast cancer diagnosis by examination type. Longitudinal study using cross-sectional data. Breast Cancer Surveillance Consortium breast imaging facilities. 19,955 women diagnosed between 2005 and 2012 with AJCC stage 0-III incident breast cancer who had 69,386 mammograms and 3,553 breast MRI examinations from January 2005 to September 2013; median follow-up of 37.6months (interquartile range, 22.1-60.7). Breast imaging indication, imaging intervals, and time since breast cancer diagnosis by examination type. Among women with a PHBC who received breast imaging, 89.4% underwent mammography alone, 0.8% MRI alone, and 10.3% had both mammography and MRI. About half of mammograms and MRIs were indicated for surveillance vs. diagnostic, with an increase in the proportion of surveillance exams as time from diagnosis increased (mammograms, 45.7% at 1year to 72.2% after 5years; MRIs, 54.8% at 1year to 78.6% after 5years). In the first post-diagnosis period, 32.8% of women had > 2 breast imaging examinations and of these, 65.8% were less than 6months apart. During the first 5-year post-diagnosis, the frequency of examinations per year decreased and the interval between examinations shifted towards annual examinations. In women with a PHBC who received post-diagnosis imaging, a third underwent multiple breast imaging examinations per year during the first 2-year post-diagnosis despite recommendations for annual exams. As time since diagnosis increases, imaging indication shifts from diagnostic to surveillance.

Comparison of the diagnostic performance of abbreviated MRI and full diagnostic MRI using a computer-aided diagnosis (CAD) system in patients with a personal history of breast cancer: the effect of CAD-generated kinetic features on reader performance

To compare the diagnostic performance of abbreviated magnetic resonance imaging (MRI) and full diagnostic MRI with computer-aided diagnosis (CAD) system in patients with a personal history of breast cancer and to evaluate how the kinetic features affect the performance of two radiologists. Between 1 January 2014 and 31 December 2017, 3,834 breast MRI examinations in 2,310 patients with a history of breast cancer comprised the study population. MRI images were reviewed retrospectively by two radiologists. First, two radiologists independently reviewed T1-weighted images scanned at 90 seconds after the contrast medium injection and T2-weighted images. After 6 months, the two readers reviewed contrast-enhanced T1-weighted images with five consecutive delayed images using CAD. The diagnostic performance of the abbreviated-sequence and full-sequence MRI were compared. Fifty-one intramammary recurrences were detected with breast MRI in 47 patients. Of the 51 tumour recurrences, 36 (70.6%) lesions occurred >3 years after initial cancer surgery and seven (13.7%) lesions at <2 years after initial surgery. The sensitivity and specificity were 92.2-94.1% and 97.6-98.6% on the abbreviated sequence and 94.1-96.1% and 97.9-98.3% on the full diagnostic MRI. Of 51 malignant lesions, six showed delayed persistent pattern, of which three lesions were non-mass enhancement and three lesions were small enhancing masses <1 cm. Overall diagnostic performances of abbreviated MRI and full diagnostic MRI were similar in both readers. The CAD-generated kinetic features could affect reader performance and the sensitivity could be improved or the specificity improved according to the reader.

Kinetic Analysis of Lesions Identified on a Rapid Abridged Multiphase (RAMP) Breast MRI Protocol

We implemented a rapid abridged multiphase (RAMP) breast magnetic resonance imaging (MRI) protocol to reduce scan time and increase workflow efficiency. In this study, we compared delayed-phase kinetic analyses of benign and malignant lesions on the RAMP protocol versus a full dynamic contrast-enhanced (DCE) MRI protocol. Consecutive breast MRI examinations obtained from October 2015 to August 2016 with tissue diagnoses of suspicious MRI lesions were identified. RAMP MRI included one precontrast and two postcontrast phases. Full DCE MRI included one precontrast and at least three postcontrast phases. Lesion kinetic analyses including mean delayed-phase volume percentage of washout, predominant curve type, and worst curve type were assessed. Kinetic analyses assessed on RAMP and DCE MRI protocols were compared using Wilcoxon rank-sum test and Chi-Square test. Receiver operating characteristic analysis was performed to discriminate benign and malignant lesions based on delayed-phase parameters. The study included 177 consecutive breast lesions (50 benign, 127 malignant) in 162 women. RAMP MRI (23 benign, 61 malignant) and DCE MRI examinations (27 benign, 66 malignant) demonstrated 8.4% vs 9.3% washout (p = 0.36) for benign lesions and 18.5% vs 17% washout (p = 0.66) for malignancies, respectively. There was no difference in the predominant and worst curve types for malignant and benign lesions or in area under the receiver operating characteristic curves for delayed-phased parameters between the two protocols (p > 0.05). Lesion kinetic analyses from the RAMP MRI protocol can achieve the same discriminatory ability as the full DCE protocol. By reducing scan time, the RAMP MRI protocol improves patient comfort and enhances workflow efficiency and can be easily implemented in any clinical setting.