Patterns of enhancement on breast MR images: interpretation and imaging pitfalls

Improving the diagnostic performance of contrast-enhanced mammography through lesion conspicuity and enhancement quantification

OBJECTIVES

To analyze qualitative and quantitative enhancement of breast lesions on CEM and their impact on specificity and overall diagnostic performance in predicting malignancy. A secondary objective was to compare lesion enhancement patterns between CEM and contrast-enhanced (CE)-MRI.

METHODS

The cohort included screening and symptomatic cases from CEM research studies (December 2016-March 2023) with an identifiable lesion. Three breast radiologists independently assessed lesion conspicuity as low, moderate, or high, based on the BI-RADS CEM lexicon. Lesion enhancement was quantified by drawing two regions of interest representing lesion and background parenchyma, to calculate contrast enhancement from the early (CEearly) and late (CElate) views. Area-under-the-curve (AUC) was used to assess diagnostic performance, with thresholds determined using the maximum Youden index. Cohen's κ was used to measure agreement between CEM and DCE-MRI enhancement patterns. p-values < 0.05 were deemed statistically significant.

RESULTS

From 503 CEM studies, 143 BI-RADS 2-5 lesions were analyzed. Lesion conspicuity was significantly associated with lesion histology (p < 0.001), contrast enhancement metrics (CEearly, CElate), and enhancement patterns on CEM recombined images. CEearly performed better in differentiating malignant from benign lesions or background parenchymal enhancement (BPE), with AUC values of 0.83 and 0.88 and 90% specificity in distinguishing BPE from cancers. There was fair/moderate agreement between lesion enhancement patterns on CEM and DCE-MRI (Cohen's κ = 0.35, p < 0.001), with a higher agreement for lesions exhibiting a wash-out pattern (Cohen's κ = 0.5, p < 0.001).

CONCLUSION

Both conspicuity and quantification of lesion enhancement can improve CEM specificity in predicting malignancy, with CEearly offering the best diagnostic performance.

KEY POINTS

Question Quantifying lesion enhancement conspicuity on contrast-enhanced mammography (CEM) has demonstrated potential in differentiating malignancy from benign lesions and BPE. Finding Contrast from the early recombined view (CEearly) performed better in discriminating malignant from benign lesions and BPE, with 90% specificity for BPE vs cancers. Clinical relevance Conspicuity and quantification of lesion enhancement on CEM can improve the specificity and overall diagnostic performance of CEM in cancer detection. Implementation of conspicuity thresholds in routine CEM interpretation could potentially reduce unnecessary recalls and benign biopsies.

Role of breast MRI BI-RADS descriptors in discrimination of non-mass enhancement lesion: A systematic review & meta-analysis

OBJECTIVES

To evaluate the association of BI-RADS 5th edition distribution and type of enhancement descriptors with the malignancy of non-mass enhancement (NME) lesions.

METHODS

Medline via PubMed, Scopus, Web of Science, ProQuest, and Embase databases were systematically searched from January 2013 to July 2022 for original studies, written in English, reporting the positive predictive value (PPV) of individual BI-RADS 5th edition descriptors (distribution and type of enhancement) of NME lesions. Risk of bias and quality of included studies were assessed by QUADAS 2 appraisal tool. Odds ratio (OR) of pathologically confirmed malignant results in each distribution and internal enhancement were pooled in a meta-analysis.

RESULTS

Eight studies for a total of 1095 lesions were included. The pooled OR of malignancy for linear, focal, segmental, regional, multiple region, and diffuse distributions are 0.70 (95%CI: 0.44-1.14), 0.37 (95% CI: 0.26-0.54), 2.42 (95% CI: 1.62-3.62), 0.56 (95% CI: 0.11-2.79), 2.80 (95% CI: 0.96-8.21), and 3.35 (95% CI: 0.59-19.04), respectively. The pooled OR of malignancy for homogenous, heterogeneous, clustered ring enhancement, and clumped enhancement are 0.39 (95% CI: 0.23-0.67), 0.59 (95% CI: 0.40-0.85), 2.92 (95% CI: 1.86-4.57), and 1.49 (95% CI: 0.96-2.32), respectively.

CONCLUSION

Based on a meta-analysis of 8 studies and more than one thousand non-mass enhancing lesions, diffuse, multiple regions and clustered ring descriptors of enhancement have the highest pooled OR for malignancy.

Evaluation of High-risk (B3) Breast Lesions on MRI: The Role of Diffusion-weighted Imaging and Texture Analysis Features in Predicting Upgrade to Malignancy

OBJECTIVE

This study aimed to investigate the potential malignancy associated with high-risk breast lesions using breast magnetic resonance imaging (MRI) characteristics, apparent diffusion coefficient (ADC) measurements, and texture analysis parameters.

METHODS

This retrospective study included 40 patients with 41 lesions diagnosed as high-risk lesions after needle biopsy. All the patients underwent surgery. Based on the histopathologic results of the surgical excision, the patients were divided into 2 groups: those diagnosed with malignancy and those who were not. The MRI characteristics of the lesions were recorded. The ADC values of the lesions were measured. Textural analysis of the lesions was also performed.

RESULTS

Fourteen lesions (34.1%) were upgraded to malignancy. The median ADCmean values ​​in the malignant group were 1.114 × 10-3 versus 1.383×10-3 mm2/s in the nonmalignant group, which was statistically significant (P < 0.001). The cutoff value for the mean ADC was 1.163 ×10-3 mm2/s. The sensitivity and specificity were 71.4% and 85.2%, respectively. Among the texture analysis parameters, kurtosis values obtained from images on the ADC map and the first subtracted dynamic contrast-enhanced (DCE) series and contrast values obtained from images on the second subtracted DCE series were found to be statistically significant (P = 0.016, P = 0.019, and P = 0.045, respectively) between the malignant and nonmalignant groups.

CONCLUSIONS

ADC measurements and texture analysis parameters provide useful diagnostic information for determining which high-risk breast lesions will progress to malignancy.

Imaging Findings of Lupus Mastitis: A Systematic Review of Case Studies

Lupus mastitis (LM) is a rare manifestation of systemic lupus erythematosus, resulting in an inflammatory process within the breast tissue. This condition presents diagnostic challenges due to its similarity to other breast pathologies, including infections and malignancies. Clinically, patients may exhibit symptoms such as breast pain, swelling, and erythema, often leading to further investigation through imaging and histopathological analysis. Given the absence of established clinical guidelines for LM, identifying the most effective imaging and laboratory techniques is essential for accurate diagnosis and treatment. In this paper, 32 patient case reports were analyzed, with 20 patients having previous diagnoses of lupus and 12 not previously diagnosed. The study population comprised 90.6% (n=29) women, with a mean age of 44 years and an average disease duration of 10 years. The majority of LM was unilateral, with 50% (n=16) of patients experiencing LM in the right breast, 31.2% (n=10) in the left breast, and 18.8% (n=6) bilaterally. The location varied to almost all quadrants of the breast. The rarity of LM contributes to clinician unawareness, often resulting in misdiagnosis and confusion. As there are currently no guidelines, in this study we examined the imaging findings of LM through a variety of modalities and reported a suggested clinical guideline to follow for the diagnoses of LM.

Quantification of breast biopsy clip marker artifact on routine breast MRI sequences: a phantom study

BACKGROUND

To investigate the artifact sizes of four common breast clip-markers on a standard breast magnetic resonance imaging (MRI) protocol in an in vitro phantom model.

METHODS

Using 1.5-T and 3-T whole-body scanners with an 18-channel breast coil, artifact dimensions of four breast biopsy markers in an agarose-gel phantom were measured by two readers on images obtained with the following sequences: T2-weighted fast spin-echo short inversion time fat-suppressed inversion-recovery with magnitude reconstruction (T2-TIRM); T1-weighted spoiled gradient-echo with fat suppression (T1_FL3D), routinely used for dynamic contrast-enhanced imaging; diffusion-weighted imaging (DWI), including a readout segmented echo-planar imaging (RESOLVE-DWI) and echo-planar imaging sequence (EPI-DWI). After outlining the artifacts by freehand regions of interest, sagittal and lateral diameters in axial images were measured.

RESULTS

Interreader agreement for artifact size quantification was high, depending on the sequence (80.4-94.8%). Overall, the size, shape, and appearance of artifacts depended on clip type and MRI sequence. The artifact size ranged from 5.7 × 8.5 mm2 to 13.4 × 17.7 mm2 at 1.5 T and from 6.6 × 8.2 mm2 to 17.7 × 20.7 mm2 at 3 T. Clip artifacts were largest on EPI-DWI and RESOLVE-DWI (p ≤ 0.016). In three out of four clips, T2-TIRM showed the smallest artifact (p ≤ 0.002), while in one clip the artifact was smallest on T1_FL3D (p = 0.026). With the exception of one clip in the RESOLVE sequence, all clips showed a decrease in the artifact area from DWI to ADC images (p ≤ 0.037).

CONCLUSION

Breast clip-marker MRI artifact appearances depend on clip type, field strength, and sequence and may reach a significant size, potentially obscuring smaller lesions and hindering accurate assessment of breast tumors.

RELEVANCE STATEMENT

Considerable variations in artifact size and characteristics across different breast clips, MRI sequences, and field strengths exist. Awareness of these artifacts and their characteristics is essential to ensure accurate interpretation of scans and appropriate treatment planning.

KEY POINTS

Awareness of breast clip artifacts is essential for accurate interpretation of MRI. The appearance of artifacts depends on breast clip type, field strength, and sequence. Clip-related artifacts might hinder the visibility of small lesions.

Enhancing the Understanding of Breast Vascularity Through Insights From Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Comprehensive Review

Breast vascularity plays a crucial role in both physiological and pathological processes, particularly in the development and progression of breast cancer. Understanding vascular changes within breast tissue is essential for accurate diagnosis, treatment planning, and monitoring therapeutic response. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has emerged as a valuable tool for evaluating breast vascularity due to its ability to provide detailed functional and morphological insights. DCE-MRI utilizes contrast agents to highlight blood flow and vessel permeability, making it especially useful in differentiating between benign and malignant lesions. This review explores the significance of DCE-MRI in breast vascularity assessment, highlighting its principles, clinical applications, and role in detecting malignancy through vascular changes. We also examine its utility in monitoring treatment response and quantitative analysis of perfusion metrics such as Ktrans and extracellular-extravascular volume (Ve). While DCE-MRI offers remarkable diagnostic accuracy, challenges remain regarding its cost, accessibility, and potential overlap of enhancement patterns between benign and malignant conditions. The review further discusses emerging technologies and future directions for DCE-MRI, including advanced imaging techniques and machine learning-based quantification. Overall, DCE-MRI stands out as a powerful tool in the comprehensive evaluation of breast vascularity, with significant potential to improve patient outcomes in breast cancer management.

Diagnostic value of qualitative and quantitative enhancement parameters on contrast-enhanced mammography

PURPOSE

To determine whether qualitative and quantitative enhancement parameters obtained from contrast-enhanced mammography (CEM) can be used in predicting malignancy.

METHODS

After review board approval, consecutive 136 suspicious lesions with definite diagnosis were retrospectively analyzed on CEM. Acquisition was routinely started with craniocaudal view and ended with mediolateral oblique view of the affected breast. Lesion conspicuity (low, moderate, high), internal enhancement pattern (homogeneous, heterogeneous, rim), contrast-to-noise ratio (CNR), percentage of signal difference (PSD) and relative enhancement from early to late view were analyzed. PSD and relative enhancements were used to determine patterns of descending, steady or ascending enhancements. Receiver operating characteristic analysis, Cohen's kappa statistics and Spearman correlation tests were used.

RESULTS

There were 29 benign and 107 malignant lesions. 64% of the malignant lesions exhibited high conspicuity compared to 14% of the benign lesions (P < 0.001). CNR values were higher in malignant lesions compared to benign ones (P ≤ 0.004). CNR from early view yielded 82% sensitivity, 72% specificity and PSD yielded 79% sensitivity, 65% specificity. Descending pattern and rim enhancement observed in 44% and 21% of breast cancers, respectively, and both provided 96% positive predictive value for malignancy.

CONCLUSION

Diagnostic accuracy of quantitative parameters was higher than that of qualitative parameters. High CNR, rim enhancement, and descending pattern were features commonly seen in malignant lesions, while low CNR, homogeneous enhancement, and ascending pattern were commonly seen in benign lesions.

Evaluation of orbital lesions with DCE-MRI: a literature review

PURPOSE

To provide a major review on the applications of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in evaluating orbital lesions. This review also outlines selected scenarios where DCE-MRI may be helpful.

METHODS

A comprehensive retrospective literature review of all English language publications on PubMed, EMBASE, and Google Scholar between 1994 and 2022. This literature review examined the specific applications and clinical scenarios surrounding the utility of DCE-MRI in orbital lesions and various findings that have been presented in the current literature.

RESULTS

DCE-MRI provides information on tissue physiology and permeability, beyond the anatomical features displayed on static imaging. Various measured parameters (qualitative, semi-quantitative, and quantitative) obtained by DCE-MRI have been used to differentiate between benign and malignant lesions, specific orbital lymphoproliferative diseases (OLPD), lacrimal gland lesions, and various rare orbital tumours. DCE-MRI has a limited role as an initial diagnostic imaging modality. However, DCE-MRI may prove to have benefit in predicting and monitoring treatment response in orbital lymphoma as a critical imaging study, but literature specific to orbital malignancies remains limited.

CONCLUSION

The value of DCE-MRI may be in situations of diagnostic uncertainty, where it may be an additional imaging aid following conventional imaging techniques. It may also act as a critical imaging modality for monitoring of orbital tumour treatment response, but the literature remains limited. Standardisation of imaging protocol, measured parameters, and statistical analysis remain limitations of this imaging technique.

Application value of MRI-guided wire localization to the non-palpable breast lesions only shown in Breast MRI

Introduction

Magnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women.

Methods

A total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed.

Results

All of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P<0.05). All the lesions were assessed according to BI-RADS category of MRI (C4A=77, C4B=40, C4C=12, C5=2). The malignancy rates were as follows: 16.88% for 4A lesions (13/77), 37.50% for 4B lesions (15/40), 75.00% for 4C lesions (9/12) and 100% for 5 lesions (2/2). There was a significant correlation between the BI-RADS category and the incidence of benign-to-malignant lesions (P<0.001).

Conclusion

MRI-guided wire localization can assist to remove suspected breast lesions early, safely and accurately. This technique makes up for the deficiency of X-ray and ultrasound, improves the accuracy of diagnosis and resection therapy in intraductal carcinoma and early invasive carcinoma, and helps to improve the the prognosis of breast cancer.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features

PURPOSE

Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors.

MATERIAL AND METHODS

A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority.

RESULTS

The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set.

CONCLUSION

This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

How to manage type 2 curve dilemma in dynamic contrast-enhanced magnetic resonance imaging of the breast: diffusion-weighted imaging or early phase enhancement kinetics?

BACKGROUND

Type 2 time-intensity curves can indicate both malignant and benign breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

PURPOSE

To investigate whether diffusion-weighted imaging (DWI) or early phase kinetics of DCE-MRI is practical to discriminate breast masses that depict type 2 curve in DCE-MRI.

MATERIAL AND METHODS

We retrospectively included 107 lesions in 97 patients with type 2 curves in DCE-MRI. Morphological characteristics, early phase dynamic parameters on DCE-MRI, and apparent diffusion coefficient (ADC) values on DWI were evaluated. Diagnostic thresholds of ADC and early phase maximum enhancement ratio (EPMER) to distinguish between benign and malignant masses were calculated. Strongest predictors of malignancy were determined to build the most effective diagnostic model.

RESULTS

DWI, EPMER, and all morphological features were found statistically significant to discriminate malignancy (P <0.05). The thresholds of ADC and EPMER were assigned as 1.0 ×10-3 mm2/s and 72%, respectively. The sensitivity and specificity were 80% and 97% for ADC, and 93% and 60% for EPMER, respectively. Two models were established. Model 1 comprised ADC and the lesion margin. Model 2 consisted of ADC, margin, and EPMER with a high specificity (99%) and positive predictive value (97%).

CONCLUSION

When combined with DWI, early phase wash-in data provide diagnostic improvement of breast masses presenting type 2 curve in the late phase of DCE-MRI, especially for specificity. Future studies are required to support our findings for the need of a cross-validation.

Clinical and Imaging Features of MRI Screen-Detected Breast Cancer

BACKGROUND

Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers.

PATIENTS AND METHODS

Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded.

RESULTS

During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam.

CONCLUSION

MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass.

Breast MRI: an illustration of benign findings

Despite its unparalleled sensitivity for aggressive breast cancer, breast MRI continually excites criticism for a specificity that lags behind that of modern mammographic techniques. Radiologists reporting breast MRI need to recognise the range of benign appearances on breast MRI to avoid unnecessary biopsy. This review summarises the reported diagnostic accuracy of breast MRI with particular attention to the technique's specificity, provides a referenced reporting strategy and discusses factors that compromise diagnostic confidence. We then present a pictorial review of benign findings on breast MRI. Enhancing radiological skills to discriminate malignant from benign findings will minimise false positive biopsies, enabling optimal use of multiparametric breast MRI for the benefit of screening clients and breast cancer patients.

MRI Features of Synchronous Masses in Known Breast Cancer Patients in Predicting Benign Versus Malignant Lesions: A Case Based Review at Tertiary Care Cancer Hospital

Sara RehmanObjectives  The purpose of this study was to determine the diagnostic accuracy of breast magnetic resonance imaging (MRI) in classifying incidental satellite masses in biopsy-proven breast cancer patients as benign or malignant masses and assessing its impact on surgical management of these patients. We also analyzed the incidence of MRI-detected lesions, which were thereafter assessed with second look ultrasound (US). Materials and Methods  A retrospective study was performed on breast cancer patients presenting from August 01, 2016 to July 31, 2019, with satellite masses seen on base line MRI. Satellite masses were classified as benign and malignant based on MRI features of shape, margin, T2-weighted imaging signals, internal enhancement pattern, enhancement kinetic curves, and diffusion restriction. This was compared with results of histopathological examination. The number of MRI-detected lesions, location of the satellite mass, and type of surgery were also documented. Results  Out of 400 breast cancer patients undergoing MRI breast, 115 patients had multiple masses. Histopathological diagnosis was available for 73 patients; and a total of 93 satellite masses were evaluated. There was evidence of additional masses on second look ultrasound in 21 patients. Of 72 masses classified as malignant on MRI, 58 showed malignant pathological outcome; while out of 21 masses characterized as benign on MRI, 18 turned out to be benign on histopathology. A statistically significant association was found between MRI features and pathological outcome of satellite masses ( p  = 0.001). The sensitivity, specificity, positive and negative predictive values, and accuracy were 95%, 56%, 80.56%, 85.7% and 81.7%, respectively. Based on these findings, modified radical mastectomy (MRM)/mastectomy was done for 42 patients, 5 patients underwent lumpectomy limited to a single tumor, extended resection done for 14 patients, 5 underwent bilateral breast conservation surgery (BCS), BCS for contralateral breast done for 4 patients undergoing ipsilateral MRM/mastectomy, and bilateral MRM/mastectomies were performed for 2 patients. One patient was lost to follow up. Conclusion  Breast MRI is the most sensitive modality for the assessment of breast cancer and plays an essential role in the detection of additional tumor foci. These findings can modify the surgical approach in these patients. However, considering the low specificity, biopsy of satellite masses is imperative to determine the most appropriate surgical plan.

Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network

Background

Architectural distortion (AD) is a common imaging manifestation of breast cancer, but is also seen in benign lesions. This study aimed to construct deep learning models using mask regional convolutional neural network (Mask-RCNN) for AD identification in full-field digital mammography (FFDM) and evaluate the performance of models for malignant AD diagnosis.

Methods

This retrospective diagnostic study was conducted at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2011 and December 2020. Patients with AD in the breast in FFDM were included. Machine learning models for AD identification were developed using the Mask RCNN method. Receiver operating characteristics (ROC) curves, their areas under the curve (AUCs), and recall/sensitivity were used to evaluate the models. Models with the highest AUCs were selected for malignant AD diagnosis.

Results

A total of 349 AD patients (190 with malignant AD) were enrolled. EfficientNetV2, EfficientNetV1, ResNext, and ResNet were developed for AD identification, with AUCs of 0.89, 0.87, 0.81 and 0.79. The AUC of EfficientNetV2 was significantly higher than EfficientNetV1 (0.89 vs. 0.78, P=0.001) for malignant AD diagnosis, and the recall/sensitivity of the EfficientNetV2 model was 0.93.

Conclusion

The Mask-RCNN-based EfficientNetV2 model has a good diagnostic value for malignant AD.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction

Objectives

The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology.

Methods

The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed.

Results

The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues.

Conclusions

Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection.

Advances in knowledge

This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.

Epidermal inclusion cyst in male breast: how to differentiate from other male breast lesions

Male breast lesions are relatively less common. The most encountered malignant lesion in the male breast is ductal adenocarcinoma; and benign lesions are gynecomastia, fibrocystic disease, intramammary lymph node, fibroadenoma, lipoma and epidermal inclusion cyst (EIC), respectively [5,6]. To date, there had been published only a few cases of EIC of the male breast in literature [3,5,6]. In this case, we aimed to present a new case of EIC with its clinical, radiological and pathological characteristics in the male breast. It had benign sonographic and magnetic resonance imaging findings but had also malignant imaging findings with diffusion restriction on diffusion-weighted imaging.

Type 2 dynamic curves: A diagnostic dilemma

BACKGROUND

Magnetic 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.

AIM

To investigate the pathological diagnosis of lesions with type 2 dynamic curves.

METHODS

We 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.

RESULTS

Twenty-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.

CONCLUSION

The 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.

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

Simple Summary

Neoadjuvant chemotherapy (NAC) followed with surgery is the standard strategy in the treatment of locally advanced breast cancer, but the individual efficacy varies. Early and accurate prediction of complete responders determines the NAC regimens and prognosis. Breast MRI has been recommended to monitor NAC response before, during, and after treatment. Radiomics has been heralded as a breakthrough in medicine and regarded to have changed the landscape of biomedical research in oncology. Delta-radiomics characterizing the change in feature values by applying radiomics to multiple time points, is a promising strategy for predicting response after NAC. In our study, the delta-radiomics model built with the change of radiomic features before and after one cycle NAC could effectively predict pathological complete response (pCR) in breast cancer. The model provides strong support for clinical decision-making at the earliest stage and helps patients benefit the most from NAC.

Abstract

Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

A nomogram for predicting three or more axillary lymph node involvement before breast cancer surgery

Based on the American College of Surgeons Oncology Group (ACOSOG)-Z0011, a useful nomogram has been constructed to identify patients who do not require intraoperative frozen sections to evaluate sentinel lymph nodes in the previous study. This study investigated the developed nomogram by ultrasonography (US) and positron emission tomography (PET)/computed tomography (CT) as a modality. In the training set, 89/1030 (8.6%) patients had three or more positive nodes. Larger tumor size, higher grade ultrasonographic ALN classification, and findings suspicious of positive ALN on PET/CT were associated in multivariate analysis. The areas under the receiver operating characteristic curve (AUC) of the nomogram were 0.856 [95% CI 0.815-0.897] in the training set. The AUC in the validation set was 0.866 [95% CI 0.799-0.934]. Application of the nomogram to 1067 patients who met the inclusion criteria of ACOSOG-Z0011 showed that 90 (8.4%) patients had scores above the cut-off and a false-negative result was 37 (3.8%) patients. And the specificity was 93.8%, and the negative predictive value was 96.4%. The upgraded nomogram improved the predictive accuracy, using only US and PET/CT. This nomogram is useful for identifying patients who do not require intraoperative analysis of sentinel lymph nodes and considering candidates for identifying neoadjuvant chemotherapy. The patients consisted of clinical T1-2 and node-negative invasive breast cancer. The training and validation set consisted of 1030 and 781 patients, respectively. A nomogram was constructed by analyzing factors related to three or more axillary lymph node metastases. The patients who matched the ACOSOG-Z0011 criteria were selected and applied to the new nomogram.

Raafat B, Alamri S, Alghamdi AJ, Osman H, Ahmed RM, Almahwasi A, S. Alamri A, Ansari M. Distinction of Breast Masses from Benign to Malignant using Magnetic Resonance Imaging and Dynamic Contrast-Enhanced in Tertiary Care Hospitals of Taif, Saudi Arabia: A Retrospective Study

Background: Breast cancer is the most frequent cancer among women throughout the world. A range of noninvasive techniques have been employed for early prevention. In health-care practice, however, quality and sensitivity remained critical. Objective: The aim of this study is to see how well Breast Magnetic Resonance Imaging (MRIs) and Dynamic Contrast-Enhanced MRI (DCE-MRI) techniques can detect breast cancer and distinguish between malignant and benign lesions. Methods: A retrospective study was conducted at the Taif Hospitals, Saudi Arabia. The Picture Archiving and Communication System was used to acquire medical records and data from 50 individuals with probable breast cancer, and breast MRI pictures were analyzed. Breast Imaging Reporting and Data System (BI-RADS) radiologist reports and DCE-MRI kinetic curves were evaluated. Excel was also used to test the sensitivity and specificity of breast MRI. Results: According to the BI-RADS results, biopsies, and breast MRI data, 52 percent of 50 patients were categorized as benign, 24 percent as malignant, and 24 percent had no abnormalities. Biopsy revealed that 61.5 percent of the malignant lesions were benign, whereas 38.5 percent were appropriately identified as cancerous. The majority of malignant tumors were discovered in patients over the age of 50. The washout curve correctly identified 60% of the malignant lesions and 40% of the benign lesions. Our data demonstrated the usefulness of MRI in detecting breast cancers by analyzing BI-RADS and utilizing DCE-MRI. False-positive, on the other hand, can lead to unnecessary biopsies. Conclusion: Breast cancer is more common among women of their fifties and beyond. Biopsies, breast MRIs, and kinetic curve analysis can all be utilized to differentiate between benign and malignant breast masses with high sensitivity and specificity.

Diagnostic role of dynamic contrast-enhanced magnetic resonance imaging in differentiating breast lesions

OBJECTIVE

This study aimed to assess the diagnostic role of perfusion weighted image (DCE-PWI) to differentiate benign from malignant breast lesions.

PATIENTS AND METHODS

The study comprised 32 women who had mammography and/or breast ultrasonography findings that were clinically questionable. All patients were fasting during the magnetic resonance imaging (MRI) test to avoid nausea or dynamic contrast-enhanced vomiting from the contrast medium.

RESULT

In this study, we observed the form of the dynamic curve (time and signal intensity curve) type I (persistent curve) was noted in 12 lesions (37.5%): 10 lesions were benign and two lesions were malignant; type II (plateau curve) was noted in eight lesions (25%): three lesions were benign and five lesions were malignant, and type III (washout curve) noted in 12 lesions (37.5%): one lesion was benign and 11 lesions were malignant.

CONCLUSIONS

The dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion technique plays an important role in differentiating benign and malignant tumors in breast cancer.

Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast

Purpose

Sclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA.

Methods

Sixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation.

Results

In the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03).

Conclusions

Radiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.

Can Contrast-Enhanced Spectral Mammography (CESM) Reduce Benign Breast Biopsy?

Objectives

To evaluate the potential of contrast-enhanced spectral mammography (CESM) in reducing benign breast biopsy rate, thereby improving resource utilization. To explore its potential as a value-adding modality in the management of BI-RADS 4/5 lesions.

Materials and Methods

This was a prospective study conducted between July 2016 and September 2018. Patients with BI-RADS 4/5 lesions detected on conventional imaging (mammogram, digital breast tomosynthesis, and ultrasound) were enrolled for adjunct CESM. Histopathologic correlation was done for all lesions. Additional suspicious lesions detected on CESM were all identified on second-look ultrasound and subsequently biopsied. Images were evaluated independently by two radiologists trained in breast imaging using BI-RADS classification. Presence of enhancement on CESM, BI-RADS score, and histopathology of each lesion were analyzed and tested with the chi-square/fisher-exact test for statistical significance.

Results

The study included 105 lesions in 63 participants—1 man and 62 women, an average age of 53.7 ± 10.8 years. On CESM, 22 (20.9%) of the lesions did not show enhancement. All 22 lesions had been classified as BI-RADS 4A and were subsequently proven to be benign. Of the remaining 83 enhancing lesions, 54 (65.1%) were malignant and 29 (34.9%) were benign (p < 0.05). CESM detected 6 additional lesions which were not identified on initial conventional imaging. Four of these were proven malignant and were in a different quadrant than the primary lesion investigated.

Conclusion

There is evidence that the absence of enhancement in CESM strongly favors benignity. It may provide the reporting radiologist with greater confidence in imaging assessment, especially in BI-RADS 4A cases, where a proportion of them are in actuality BI-RADS 3. Greater accuracy of BI-RADS grading can reduce nearly half of benign biopsies and allow better resource allocation. CESM also increases the detection rate of potentially malignant lesions, thereby changing the treatment strategies.

Multiparametric Magnetic Resonance Imaging in Evaluation of Benign and Malignant Breast Masses with Pathological Correlation

Background

Dynamic contrast-enhanced (DCE) MRI sequences plays a vital role in diagnosing breast masses with high sensitivity and specificity as compared to other diagnostic modalities. The addition of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values significantly improves diagnostic accuracy. This study aimed to study the breast masses on DCE-MRI, restricted diffusion on DWI, ADC values, and choline peak on spectroscopy in breast cancer diagnosis.

Material and methods

This study was a prospective observational study which involved subjects with breast lumps. Baseline data was collected from the patients along with pertinent clinical history and relevant laboratory investigations. MR mammography (MRM) was performed on a 1.5 Tesla MR Scanner (MAGNETOM® Avanto, Siemens AG, Munich Germany) using a dedicated double breast coil.

Results

Forty-one subjects were included with a total of 54 breast masses in them. The mean age of the study population was 47.1±14.7 years. From the MRI final diagnosis, the majority (53.70%) were diagnosed as malignant lesions and 46.30% as benign. Out of 20 lesions diagnosed as benign on histopathology, only five percent had ADC value <1.3 ×10⁻³mm²/s, and the majority (95%) had ADC value >1.3 ×10⁻³mm²/s. All 20 lesions were circumscribed, ovoid, or round in shape showing no restricted diffusion on DWI, with corresponding ADC value of >1.3×10⁻³mm²/s, homogeneous post-contrast enhancement, or with dark internal septations, type I kinetic enhancement curve, and they showed no choline peak on spectroscopy. Out of 34 malignant lesions diagnosed on histopathology, the majority (85.29%) displayed restricted diffusion on DWI and had an ADC value of <1.3×10⁻³mm²/s, most of them had spiculated margins, type II/ III kinetic curve with choline peak on spectroscopy.

Conclusion

Multiparametric MR mammography, which included DCE-MRM, DWI, ADC values, and spectroscopy, correlated well with the histopathological diagnosis of benign and malignant breast masses.

Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis

Background

The Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions is categorized into 4A, 4B, and 4C, which reflect an increasing malignancy potential from low (2–10%) moderate (10–50%) and high (50–95%). Determining the benign and malignant of BI-RADS category 4 breast lesions is very important for accurate diagnosis and follow-up treatment. This study aimed to explore the value of breast magnetic resonance imaging (MRI) omics features and clinical characteristics in the assessment of BI-RADS category 4 breast lesions.

Methods

This retrospective study analyzed 96 lesions (39 benign and 57 malignant) from 92 patients diagnosed with MRI BI-RADS category 4 lesions in the Second Affiliated Hospital of Dalian Medical University between May 2017 and December 2019. The lesions were sub-categorized as BI-RADS 4A, 4B, or 4C based on the MRI findings. An imaging omics analysis model was applied to extract the MRI features. The positive predictive value (PPV) of each subcategory was calculated, and the area under the curve (AUC) was used to describe the efficiency for different diagnoses. Moreover, we analyzed 17 clinical indicators to assess their diagnostic value for BI-RADS category 4 breast lesions.

Results

The PPVs of BI-RADS 4A, 4B, and 4C were 7.1% (2/28), 41.2% (7/17), and 94.1% (48/51), respectively. The AUC, sensitivity, and specificity were 0.919, 84.2%, and 92.3%, respectively. The combination of T₁-weighted images (T₁WI) with dynamic contrast-enhanced (DCE) MRI yielded the best diagnostic results among all dual sequences. Two clinical indicators [progesterone receptor (PR) and Ki-67 expression] achieved an AUC almost equal to 1.0. The radiomics and redundancy reduction methods reduced the clinical data features from 1,233 to 14.

Conclusions

High diagnostic performance can be achieved in distinguishing malignant breast BI-RADS category 4 lesions using the combination of T₁WI and DCE in MRI. Combining the PR and Ki-67 expression variables can further improve MRI accuracy for breast BI-RADS category 4 lesions.

Therapeutic Options in the Management of Aromatase Inhibitor-Associated Bone Loss

BACKGROUND

Breast cancer is the most commonly occurring cancer in women worldwide. Early breast cancer is a kind of invasive neoplasm that has not proliferated beyond the breast or the axillary lymph nodes. Current therapeutic strategies for breast cancer mainly include local therapies such as surgery or radiotherapy and systemic therapies like chemotherapy, endocrine, and targeted therapy.Nowadays, the adjuvant treatment for hormone receptor-positive early breast cancer in postmenopausal women remains the main effective systemic therapy which can improve disease-free survival and overall survival; it involves several endocrine treatment regimens including selective estrogen receptor modulators (SERMs), aromatase inhibitors (AIs), or a combination of them. AIs have been shown to be more effective in preventing recurrence in postmenopausal women with early breast cancer when compared with tamoxifen, thus representing the standard of care for adjuvant endocrine therapy. Although AIs are usually well-tolerated, they can have some side effects. Apart from the appearance of arthralgias or myalgias and cardiovascular events, AI therapies, reducing already low endogenous postmenopausal estradiol levels, cause increased bone loss and increase fracture risk in postmenopausal women.

OBJECTIVES

To evaluate the therapeutic options in the management of aromatase inhibitor-associated bone loss (AIBL).

METHODS

We reviewed the current literature dealing with different therapeutic options in the treatment of AIBL.

RESULTS

Clinical practice guidelines recommend a careful evaluation of skeletal health in all women with breast cancer before AI therapy initiation. Adequate calcium and vitamin D intake have also been suggested. Pharmacological attempts to minimize AI-related bone loss have focused on the use of antiresorptive agents, such as bisphosphonates and denosumab, to protect bone integrity and reduce the risk of fractures. Furthermore, clinical trials have shown that by making the bone microenvironment less susceptible to breast cancer metastasis, these drugs are able to increase disease-free survival.

CONCLUSIONS

AI, thatare the pillar of the systemic treatment for patients with hormone receptor-positive breast cancer, are associated with different side effects, and in particular osteoporosis and fractures. Both bisphosphonates and denosumab are able to prevent this negative effect.

Incidental Breast Findings on Computed Tomography and MR Imaging

Computed tomography (CT) and magnetic resonance (MR) imaging may demonstrate a wide variety of incidental findings in the breast, including primary breast carcinoma, the second most common cancer in women. It important to recognize the spectrum of pathologic conditions in order to properly assess the need for further workup. Some findings may be diagnosed as benign on the basis of CT/ MR imaging and clinical history alone, whereas others will require evaluation with dedicated breast imaging and possibly biopsy. This article serves to guide radiologists' management of the wide spectrum of incidental breast findings encountered on cross-sectional imaging.

Unveiling the histopathologic spectrum of MRI-guided breast biopsies: an institutional pathological-radiological correlation

PURPOSE

Breast magnetic resonance imaging (MRI) has high sensitivity but suffers from low specificity, resulting in many benign breast biopsies for MRI-detected lesions. We sought to compare histologic findings between patients who underwent MRI-guided breast biopsy versus biopsy via other imaging modalities as well as to examine features associated with malignancy in the MRI cohort to help inform MRI-biopsy practice.

METHODS

A 2-year (2018-2019) retrospective review of breast biopsies at our enterprise was conducted. Biopsies were categorized as stereotactic, ultrasound, MRI, or palpation guided. Pathology was categorized as benign (further divided into nine categories), atypical, or malignant (subdivided into in situ and invasive carcinoma). Pathology was compared between biopsy groups. Clinical, pathologic, and imaging features were compared between pathology groups within the MRI cohort.

RESULTS

5828 biopsies from 4154 patients were reviewed, including 548 MRI-guided biopsies with stratification of MRI-biopsy pathology as follows: 69% benign, 13.8% atypical, and 17.2% malignant. Among benign MRI biopsies, there was higher frequency of "clustered cysts with papillary apocrine metaplasia" (56/548; 10.2%) and lower rate of fibroadenoma/fibroadenomatous change (55/548; 10%) compared to other modalities (158 or 3% and 1144 or 21.7% of 5280 biopsies, respectively). Multivariate analysis revealed indication of breast cancer (p < .0001), ipsilateral cancer (p < .0001) and rapid initial phase kinetics (p = .017) to remain significantly associated with malignant MRI-biopsy pathology.

CONCLUSIONS

A concurrent or recent breast cancer diagnosis was most predictive of malignancy on MRI-guided breast biopsy. Combined MRI feature evaluation and radiologic-pathologic concordance activities may allow for prognostic refinement and improved risk stratification.

MRI Virtual Biopsy of T2 Hyperintense Breast Lesions

A wide variety of benign and malignant breast processes may generate hyperintense signal at T2-weighted magnetic resonance imaging (MRI). MRI has been traditionally used in the pre-treatment planning of breast cancer, in assessing treatment response and detecting recurrence. In this comprehensive review, we describe and illustrate the MRI features of a few common and uncommon T2 hyperintense breast lesions, with an emphasis on MRI features that help to characterize lesions based on morphological features, specific appearances on T1-and T2-weighted imaging, and enhancement characteristics on the dynamic post-contrast phase that are either diagnostic or aid in narrowing the differential diagnosis.

Breast MRI: False-Negative Results and Missed Opportunities

Breast MRI is the most sensitive modality for the detection of breast cancer. However, false-negative cases may occur, in which the cancer is not visualized at MRI and is instead diagnosed with another imaging modality. The authors describe the causes of false-negative breast MRI results, which can be categorized broadly as secondary to perceptual errors or cognitive errors, or nonvisualization secondary to nonenhancement of the tumor. Tips and strategies to avoid these errors are discussed. Perceptual errors occur when an abnormality is not prospectively identified, yet the examination is technically adequate. Careful development of thorough search patterns is critical to avoid these errors. Cognitive errors occur when an abnormality is identified but misinterpreted or mischaracterized as benign. The radiologist may avoid these errors by utilizing all available prior examinations for comparison, viewing images in all planes to better assess the margins and shapes of abnormalities, and appropriately integrating all available information from the contrast-enhanced, T2-weighted, and T1-weighted images as well as the clinical history. Despite this, false-negative cases are inevitable, as certain subtypes of breast cancer, including ductal carcinoma in situ, invasive lobular carcinoma, and certain well-differentiated invasive cancers, may demonstrate little to no enhancement at MRI, owing to differences in angiogenesis and neovascularity. MRI is a valuable diagnostic tool in breast imaging. However, MRI should continue to be used as a complementary modality, with mammography and US, in the detection of breast cancer. ^©RSNA, 2021.

Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer

BACKGROUND

Lymphovascular invasion (LVI) is an important risk factor for prognosis of breast cancer and an unfavorable prognostic factor in node-negative invasive breast cancer patients. The purpose of this study was to evaluate the association between LVI and pre-operative features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in node-negative invasive breast cancer.

METHODS

Data were collected retrospectively from 132 cases who had undergone pre-operative MRI and had invasive breast carcinoma confirmed on the last surgical pathology report. MRI and DWI data were analyzed for the size of tumor, mass shape, margin, internal enhancement pattern, kinetic enhancement curve, high intratumoral T2-weighted signal intensity, peritumoral edema, DWI rim sign, and apparent diffusion coefficient (ADC) values. We calculated the relationship between presence of LVI and various prognostic factors and MRI features.

RESULTS

Pathologic tumor size, mass margin, internal enhancement pattern, kinetic enhancement curve, DWI rim sign, and the difference between maximum and minimum ADC were significantly correlated with LVI (p < 0.05).

CONCLUSIONS

We suggest that DCE-MRI with DWI would assist in predicting LVI status in node-negative invasive breast cancer patients.

Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer

BACKGROUND

Oncotype DX is a multigene assay used in breast cancer, and the result provided as a 'recurrence score (RS)' corresponds to the risk of a cancer recurrence and the chemotherapeutic benefit in estrogen receptor (ER)-positive human epidermal growth factor receptor (HER)2-negative invasive breast cancer. However, its accessibility is limited.

PURPOSE

To evaluate whether magnetic resonance imaging (MRI) could be used to predict Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

MATERIAL AND METHODS

We enrolled 473 patients with ER-positive HER2-negative invasive breast cancer who underwent a preoperative MRI and Oncotype DX assay between January 2015 and December 2018. The MRI was reviewed and associations between Oncotype DX RS values were evaluated. Logistic regression analysis was used to identify independent predictors of high and low RS.

RESULTS

Of the 485 cancers, 288 (59.4%) had low (<18), 155 (31.9%) had intermediate (18-30), and 42 (8.7%) had high (≥31) RS. Multiple logistic regression analysis revealed that a round shape (odds ratio [OR] = 2.554, P = 0.089) and low proportion of washout component (OR = 1.011, P = 0.014) were associated with low RS and that heterogeneously dense (OR = 3.205, P = 0.007) or scattered fibroglandular (OR = 3.776, P = 0.005) breast tissue, a non-spiculated margin (OR = 5.435, P = 0.007), and low proportion of persistent component (OR = 1.012, P = 0.036) were associated with high RS.

CONCLUSION

MRI features showed the potential for the discrimination of Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

Five-dimensional quantitative low-dose Multitasking dynamic contrast- enhanced MRI: Preliminary study on breast cancer

PURPOSE

To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T₁ mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm³ , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg).

METHODS

Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T₁ recovery dimension for dynamic T₁ quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T₁ maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standard-dose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups.

RESULTS

The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively).

CONCLUSION

The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated kinetic parameters were capable of differentiating between normal breast tissue and benign and malignant tumors.

Dynamic contrast-enhanced MRI in oncology: how we do it

Magnetic resonance imaging (MRI) is particularly attractive for clinical application in perfusion imaging thanks to the absence of ionizing radiation and limited volumes of contrast agent (CA) necessary. Dynamic contrast-enhanced MRI (DCE-MRI) involves sequentially acquiring T1-weighted images through an organ of interest during the passage of a bolus administration of CA. It is a particularly flexible approach to perfusion imaging as the signal intensity time course allows not only rapid qualitative assessment, but also quantitative measures of intrinsic perfusion and permeability parameters. We examine aspects of the T1-weighted image series acquisition, CA administration, post-processing that constitute a DCE-MRI study in clinical practice, before considering some heuristics that may aid in interpreting the resulting contrast enhancement time series. While qualitative DCE-MRI has a well-established role in the diagnostic assessment of a range of tumours, and a central role in MR mammography, clinical use of quantitative DCE-MRI remains limited outside of clinical trials. The recent publication of proposals for standardized acquisition and analysis protocols for DCE-MRI by the Quantitative Imaging Biomarker Alliance may be an opportunity to consolidate and advance clinical practice.

Architecture distortion score (ADS) in malignancy risk stratification of architecture distortion on contrast-enhanced digital mammography

OBJECTIVE

To develop a risk predictor model in evaluation of tomosynthesis-detected architectural distortion (AD) based on characteristics of contrast-enhanced digital mammography (CEDM).

METHODS

Ninety-four AD lesions on CEDM in combination with tomosynthesis were retrospectively reviewed from 92 consecutive women (mean age, 52.4 years ± 7.9) with abnormal diagnostic or screening mammography. CEDM results were correlated with histology of ADs using cross-tabulation for statistical analysis. Predictors for risk of malignancy from CEDM characteristics (background parenchyma enhancement, degree of AD enhancement, enhancing morphology, size of enhancement, and enhancing spiculations) and patient's age were evaluated using logistic regression. We propose a sum score, termed AD score (ADS), for risk stratification and corresponding suggested BI-RADS category.

RESULTS

Thirty-three of ninety-four (35.1%) of detected AD lesions were malignant. The sensitivity, specificity, PPV, and NPV of CEDM in evaluation of malignant AD are 100%, 42.6%, 48.5%, and 100%, respectively. Absence of AD enhancement on CEDM is highly indicative of no underlying malignancy. On multivariate analysis, the predictors on CEDM with statistical significance are (1) marked intensity of AD enhancement (OR, 22.6; 95%CI 3.1, 166.6; p = .002); and (2) presence of enhancing spiculations (OR, 9.1; 95%CI 2.2, 36.5; p = .002). A prediction model whose scores (ADS) given by ranking of OR of all predictors with AUC of 0.934 and Brier score of 0.0956 was developed.

CONCLUSION

ADS-based lesion characterization on CEDM enables risk assessment of tomosynthesis-detected AD lesions.

KEY POINTS

• Architecture distortions presenting with marked enhancement intensity and presence of enhancing spiculations are highly associated with risk of malignancy. • Absence of architecture distortion enhancement in minimal or mild background parenchyma enhancement on CEDM indicates low risk of breast malignancy (NPV = 100%).

Is biopsy necessary? Role of DCE-MRI in BIRADS-3 lesions

PURPOSE

We aimed to evaluate BIRADS-3 breast lesions with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and compare with histopathology, and to investigate the effectiveness of breast MRI for follow-up and management.

METHODS

A total of 84 BIRADS-3 lesions reported by US or mammography and evaluated by DCE-MRI between September 2014 and October 2015 were included in this study. All patients underwent percutaneous or surgical biopsy for histopathologic diagnosis. Morphologic and kinematic features on MRI were compared with histopathologic results.

RESULTS

Of the 84 BIRADS-3 breast lesions, 9 (10.7%) had malignant features on DCE-MRI and all were verified with histopathologic results. DCE-MRI had 96.7% sensitivity, 72% specificity, 92% positive predictive value, and 82.5% negative predictive value. MRI and histopathology results were correlated for the diagnosis of malignant lesions. The sensitivity and negative predictive value of MRI for diagnosis of malignant lesions were both 100%.

CONCLUSION

Differentiation of benign versus malignant lesions was accomplished with 100% accuracy with DCE-MRI. We suggest that DCE-MRI should be an additional diagnostic tool and problem-solving modality for BIRADS-3 lesions, particularly in patients with relative risk factors.

Kinetic patterns of benign and malignant breast lesions on contrast enhanced digital mammogram

Purpose

To evaluate the kinetic patterns of benign and malignant breast lesions using contrast-enhanced digital mammogram (CEDM).

Methods

Women with suspicious breast lesions on mammography or ultrasound were enrolled. Single-view mediolateral oblique (MLO) CEDM of an affected breast was acquired at 2, 3, 4, 7, and 10 min after injection of contrast agent. Three readers visually and semi-quantitatively analyzed the enhancement of suspicious lesions. The kinetic pattern of each lesion was classified as persistent, plateau, or washout over two time intervals, 2–4 min and 2–10 min, by comparing the signal intensity at the first time interval with that at the second.

Results

There were 73 malignant and 75 benign lesions in 148 patients (mean age: 52 years). Benign and malignant breast lesions showed the highest signal intensity at 3 min and 2 min, respectively. Average areas under receiver operating characteristic (ROC) curve for diagnostic accuracy based on lesion enhancement at different time points were 0.73 at 2 min, 0.72 at 3 min, 0.69 at 4 min, 0.67 at 7 min, and 0.64 at 10 min. Diagnostic performance was significantly better at 2, 3, and 4 min than at 7 and 10 min (all p < 0.05). A washout kinetic pattern was significantly associated with malignant lesions at 2–4 min and 2–10 min frames according to two of the three readers’ interpretations (all p ≤ 0.001).

Conclusion

Applications of optimal time intervals and kinetic patterns show promise in differentiation of benign and malignant breast lesions on CEDM.

Granular cell tumor of the breast: correlations between imaging and pathology findings

Objective

To review the imaging features of granular cell tumors of the breast (on mammography, ultrasound, and magnetic resonance imaging), establishing a pathological correlation, in order to familiarize radiologists with this entity and make them aware of the differential diagnoses, other than malignancy, of lesions with spiculated margins.

Materials and Methods

We reviewed the medical records (from a clinical-pathology database and picture archiving and communication system) of five patients with a pathologically confirmed diagnosis of granular cell tumor of the breast, treated at the Portuguese Oncology Institute of Lisbon, in the city of Lisbon, Portugal, between January 2012 and December 2018.

Results

All five tumors exhibited imaging features highly suggestive of malignancy (BI-RADS 5 lesions), namely spiculated margins, significant depth, and posterior acoustic shadowing (on ultrasound). One tumor showed a kinetic curve indicative of washout on magnetic resonance imaging, two were adherent to the pectoralis muscle, and one was accompanied by skin retraction. Pathology provided the definitive diagnosis in all cases.

Conclusion

Granular cell tumors of the breast pose a diagnostic challenge because they can present with clinical and imaging features mimicking malignancy, and the diagnosis is therefore provided by pathology. Radiologists should be familiarized with this entity, so they can be aware of the fact that breast lesions with spiculated margins can be indicative of diagnoses other than malignancy.

Correlation Between Enhancement Intensity in Contrast Enhancement Spectral Mammography and Types of Kinetic Curves in Magnetic Resonance Imaging

Background

Due to the decreased sensitivity of mammography in glandular breasts, new diagnostic modalities, like contrast-enhanced spectral mammography (CESM) and digital breast tomosynthesis (DBT) have been developed. The aim of this study was to compare qualitative enhancement levels on CESM with type of kinetic enhancement curves on MRI examination.

Material/Methods

Patients qualified for the CESM examination presented some diagnostic doubts – suspected multifocality, multicentricity, or having dense glandular breast tissue. The enhancement level on CESM was described as weak, medium, or strong. Enhancement on achieved MR images was assessed on the basis of enhancement kinetic curves. The level of enhancement on CESM was associated with enhancement curves type on MRI. All lesions detected on CESM and MRI were histopathologically verified.

Results

The study involved 107 lesions diagnosed in 94 patients: 71 lesions (66%) appeared to be infiltrating on histopathological examination, 9 lesions (8%) were non-infiltrating cancers, and 27 lesions (25%) were benign. Data analysis revealed that lesions with wash-out curve on MRI most often presented strong enhancement on CESM, while in lesions with progressive enhancement curve, strong enhancement on CESM was the rarest. The relationship between enhancement level on CESM and curve type on contrast-enhanced MRI depends on the nature of the lesion. The type of MRI curve was found to be associated with enhancement level on CESM.

Conclusions

We compared subjective assessments of contrast enhancement on CESM with enhancement kinetic curves on MRI. The results showed that the level of enhancement on CESM and type of kinetic curves on MRI depends on the lesion type.

Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration

We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.

Radiation treatment monitoring with DCE-US in CWR22 prostate tumor xenografts

BACKGROUND

Longitudinal monitoring of potential radiotherapy treatment effects can be determined by dynamic contrast-enhanced ultrasound (DCE-US).

PURPOSE

To assess functional parameters by means of DCE-US in a murine subcutaneous model of human prostate cancer, and their relationship to dose deposition and time-frame after treatment. A special focus has been placed to evaluate the vascular heterogeneity of the tumor and on the most suitable data analysis approach that reflects this heterogeneity.

MATERIAL AND METHODS

In vivo DCE-US was acquired 24 h and 48 h after radiation treatment with a single dose of 7.5 Gy and 10 Gy, respectively. Tumor vasculature was characterized pixelwise using the Brix pharmacokinetic analysis of the time-intensity curves.

RESULTS

Longitudinal changes were detected ( P < 0.001) at 24 h and 48 h after treatment. At 48 h, the eliminating rate constant of the contrast agent from the plasma, k_el, was correlated ( P ≤ 0.05) positively with microvessel density (MVD; r_τ = 0.7) and negatively with necrosis (r_τ = -0.6) for the treated group. Furthermore, Ak_ep, a parameter related to transcapillary transport properties, was also correlated to MVD (r_τ = 0.6, P ≤ 0.05).

CONCLUSION

DCE-US has been shown to detect vascular changes at a very early stage after radiotherapy, which is a great advantage since DCE-US is non-invasive, available at most hospitals, and is low in cost compared to other techniques used in clinical practice.

Solitary, Well-Circumscribed, T2 Hyperintense Masses on MRI Have Very Low Malignancy Rates

OBJECTIVE

The purpose of this study was to determine the malignancy rate of solitary MRI masses with benign BI-RADS descriptors.

METHODS

A retrospective review was conducted of all breast MRI reports that described a mass with a final BI-RADS assessment of 3, 4, or 5, from February 1, 2005, through February 28, 2014 (n = 1510). Studies were excluded if the mass was not solitary, did not meet formal criteria for a mass, or had classically suspicious BI-RADS features (e.g., washout kinetics, and spiculated margin). The masses were reviewed by 2 fellowship-trained breast radiologists who reported consensus BI-RADS mass margin, shape, internal-enhancement, and kinetics descriptors. The T2 signal was reported as hyperintense if equal to or greater than the signal intensity of the axillary lymph nodes. Pathology results or 2 years of imaging follow-up were recorded. Comparisons were made between mass descriptors and clinical outcomes.

RESULTS

There were 127 women with 127 masses available for analysis. There were 76 (60%) masses that underwent biopsy for an overall malignancy rate of 4% (5/127): 2 ductal carcinoma in situ (DCIS) and 3 invasive ductal carcinoma. The malignancy rate was 2% (1/59) for T2 hyperintense solitary masses. The malignancy rate was greater than 2% for all of the following BI-RADS descriptors: oval (3%, 3/88), round (5%, 2/39), circumscribed (4%, 5/127), homogeneous (4%, 3/74), and dark internal septations (4%, 2/44).

CONCLUSION

T2 hyperintense solitary masses without associated suspicious features have a low malignancy rate, and they could be considered for a BI-RADS 3 final assessment.

Role of DCE-MR in predicting breast cancer subtypes

OBJECTIVE

The purpose of this retrospective study is to find a correlation between dynamic contrast-enhanced MR features with histological, immunohistochemical and loco-regional characteristics of breast cancer.

MATERIALS AND METHODS

A total of 149 patients with histopathologically confirmed invasive breast carcinoma underwent MR imaging. Histological analysis included: histological features (histological type, necrosis, vascular invasion and Mib-1), immunohistochemical characterization (immunophenotype, receptor status, HER2-neu and grading) and loco-regional characteristics (T and N). The kinetic MR features analyzed were: curve type, maximum enhancement, time to peak, wash-in and wash-out rate, brevity of enhancement and area under curve.

RESULTS

MRI kinetic parameters and immunohistological features were compared using chi square test, two-tailed student t test and Anova test, with p = 0.05 level of significance. Vascular invasion was shown to be significantly related to time to peak (p = 0.02). The immunohistotype was shown to be significantly related with maximum enhancement (p = 0.05), time to peak (p = 0.04) and wash-in rate (p = 0.01). ER status correlates with maximum and relative enhancement (p = 0.004 and p = 0.028), wash-in rate (p = 0.0018) and area under curve (p = 0.006). PR status was significantly related to time to peak (p = 0.048) and wash-in rate (p = 0.05).

CONCLUSION

Maximum enhancement absolute and relative, time to peak, wash-in rate and area under the curve significantly correlate with several prognostic factors, like ER status, immune profile and tumoral vascular invasion, and may predict the aggressiveness of the tumor.

Clinical role of breast MRI now and going forward

Magnetic resonance imaging (MRI) is a well-established method in breast imaging, with manifold clinical applications, including the non-invasive differentiation between benign and malignant breast lesions, preoperative staging, detection of scar versus recurrence, implant assessment, and the evaluation of high-risk patients. At present, dynamic contrast-enhanced MRI is the most sensitive imaging technique for breast cancer diagnosis, and provides excellent morphological and to some extent also functional information. To compensate for the limited functional information, and to increase the specificity of MRI while preserving its sensitivity, additional functional parameters such as diffusion-weighted imaging and apparent diffusion coefficient mapping, and MR spectroscopic imaging have been investigated and implemented into the clinical routine. Several additional MRI parameters to capture breast cancer biology are still under investigation. MRI at high and ultra-high field strength and advances in hard- and software may also further improve this imaging technique. This article will review the current clinical role of breast MRI, including multiparametric MRI and abbreviated protocols, and provide an outlook on the future of this technique. In addition, the predictive and prognostic value of MRI as well as the evolving field of radiogenomics will be discussed.

T1-Weighted Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to Distinguish Between Concurrent Cholesterol Granuloma and Invasive Ductal Carcinoma of the Breast: A Case Report

Patient: Female, 52

Final Diagnosis: Cholesterol granuloma of the breast

Symptoms: Follow up

Medication: —

Clinical Procedure: Surgical excision

Specialty: Radiology