Non-mass-like enhancement on contrast-enhanced breast MR imaging: lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images

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.

Predictive factors for non-mass enhancement occult in conventional breast imaging: The "PAMAS" study

OBJECTIVES

To identify predictive factors for malignancy in non-mass enhancement occult on conventional imaging (NMEOCI) using MRI, focusing on morphological traits and clinical contexts to refine management strategies.

MATERIALS & METHODS

This retrospective single-center study reviewed all MRI-guided biopsies performed for NMEOCI between January 2015 and October 2021 at a European oncology reference facility. Exclusion criteria were unavailability of the MRI that led to the biopsy, inability to perform control clip MRI or surgery certifying correct targeting, and clustered ring enhancement. Clinical and radiological characteristics were analyzed, and a multivariate logistic regression model assessed associations with malignancy as confirmed by pathological analysis.

RESULTS

One hundred and twenty-eight patients (median age, 58.0 years (Q1-Q3: 45.5-68.0), 128 women) were evaluated. Increased risk of malignancy was associated with older age (p = 0.013), preoperative context (p = 0.050), presence of homolateral neoplasia (p = 0.031), or axillary adenomegaly (p = 0.034). In contrast, MRIs performed without indications (p = 0.044) or as part of screening for high-risk patients (p = 0.033) were protective. NMEOCI features such as larger size (p < 0.001), segmental distribution (p < 0.001), and micronodular character (p < 0.001) were correlated with malignancy risk, whereas homogeneous enhancement suggested benignity (p < 0.001). Five of these characteristics were independently associated with lesions at risk of malignancy: preoperative context, age of patient, micronodular enhancement, axillary adenomegaly, and segmental distribution.

CONCLUSION

Morphological characteristics and clinical contexts of NMEOCIs on MRI are associated with malignancy risk. The mnemonic acronym "PAMAS" ("not a mass" in French) is a useful guide for this type of lesion: Preoperative context, Age, Micronodular enhancement, axillary Adenomegaly, and Segmental distribution, are independently associated with lesions at risk of malignancy.

CLINICAL RELEVANCE STATEMENT

This study enhances the precision of MRI for the analysis of NMEOCI by identifying key morphological and clinical predictors of malignancy, some of which have never been studied before, potentially reducing unnecessary biopsies, and optimizing patient management.

Computerized Segmentation Method for Nonmasses on Breast DCE-MRI Images Using ResUNet++ with Slice Sequence Learning and Cross-Phase Convolution

The purpose of this study was to develop a computerized segmentation method for nonmasses using ResUNet++ with a slice sequence learning and cross-phase convolution to analyze temporal information in breast dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images. The dataset consisted of a series of DCE-MRI examinations from 54 patients, each containing three-phase images, which included one image that was acquired before contrast injection and two images that were acquired after contrast injection. In the proposed method, the region of interest (ROI) slice images are first extracted from each phase image. The slice images at the same position in each ROI are stacked to generate a three-dimensional (3D) tensor. A cross-phase convolution generates feature maps with the 3D tensor to incorporate the temporal information. Subsequently, the feature maps are used as the input layers for ResUNet++. New feature maps are extracted from the input data using the ResUNet++ encoders, following which the nonmass regions are segmented by a decoder. A convolutional long short-term memory layer is introduced into the decoder to analyze a sequence of slice images. When using the proposed method, the average detection accuracy of nonmasses, number of false positives, Jaccard coefficient, Dice similarity coefficient, positive predictive value, and sensitivity were 90.5%, 1.91, 0.563, 0.712, 0.714, and 0.727, respectively, larger than those obtained using 3D U-Net, V-Net, and nnFormer. The proposed method achieves high detection and shape accuracies and will be useful in differential diagnoses of nonmasses.

How Well Does Non-mass Enhancement Correlate With DCIS/Invasive Cancer?

INTRODUCTION

Contiguous non-mass enhancement (NME) often coexists with a solid tumor component on MRI, but it can be challenging to predict whether NME represents invasive breast cancer, ductal carcinoma in situ (DCIS), benign disease, or biopsy site reaction. The purpose of this study was to determine the association between the size/extent of NME and the presence of invasive cancer and/or DCIS on final pathology.

METHODS

This was a single institution retrospective analysis of a prospectively maintained breast cancer registry (2010-2020). Female patients who underwent surgical resection were included if they had a diagnosis of invasive breast cancer (with or without DCIS) and had an MRI showing both a solid mass and contiguous NME. The size of NME on MRI was compared with the size of invasive cancer and/or DCIS on the final pathology.

RESULTS

From a total of 3443 patients, 225 patients were included. 86.2% had invasive ductal carcinoma (IDC), and 12.0% had invasive lobular carcinoma 76.9% were ER+, 16.4% were HER2+, and 13.3% were triple negative breast cancer (TNBC). 18.7% received neoadjuvant chemotherapy (NCT) of whom 31% achieved a complete radiographic/pathologic response. Pearson correlation coefficients (r) between the size of NME and invasive cancer/DCIS showed a strong and positive correlation of MRI NME with DCIS on pathology in patients without NCT. Subgroup analysis showed the strongest correlations for NME and DCIS among non-white (r = .70) and HER2 + patients (r = .74) who did not receive NCT.

CONCLUSIONS

Strong correlations between NME and DCIS were found for HER2 + disease and non-white patients, but only modest correlations were found for other patient/disease characteristics. These correlations may impact decisions in surgical approach.

Comparison of the diagnostic value of contrast-enhanced ultrasound combined with conventional ultrasound versus magnetic resonance imaging in malignant non-mass breast lesions

OBJECTIVE

To compare the diagnostic value of contrast-enhanced ultrasound (CEUS)+conventional ultrasound vs MRI for malignant non-mass breast lesions (NMLs).

METHODS

A total of 109 NMLs detected by conventional ultrasound and examined by both CEUS and MRI were retrospectively analysed. The characteristics of NMLs in CEUS and MRI were noted, and agreement between the two modalities was analysed. Sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and area under the curve (AUC) of the two methods for diagnosing malignant NMLs were calculated in the overall sample and subgroups of different sizes(<10 mm, 10-20 mm, >20 mm).

RESULTS

A total of 66 NMLs detected by conventional ultrasound showed non-mass enhancement in MRI. Agreement between ultrasound and MRI was 60.6%. Probability of malignancy was higher when there was agreement between the two modalities. In the overall group, the sensitivity, specificity, PPV, and NPV of the two methods were 91.3%, 71.4%, 60%, 93.4% and 100%, 50.4%, 59.7%, 100%, respectively. The diagnostic performance of CEUS+conventional ultrasound was better than that of MRI (AUC: 0.825 vs 0.762, p = 0.043). The specificity of both methods decreased as lesion size increased, but sensitivity did not change. There was no significant difference between the AUCs of the two methods in the size subgroups (p > 0.05).

CONCLUSION

The diagnostic performance of CEUS+conventional ultrasound may be better than that of MRI for NMLs detected by conventional ultrasound. However, the specificity of both methods decrease significantly as lesion size increases.

ADVANCES IN KNOWLEDGE

This is the first study to compare the diagnostic performance of CEUS+conventional ultrasound vs that of MRI for malignant NMLs detected by conventional ultrasound. While CEUS+conventional ultrasound appears to be superior to MRI, subgroup analysis suggests that diagnostic performance is poorer for larger NMLs.

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment

BACKGROUND

Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable.

PURPOSE

To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results.

FIELD STRENGTH/SEQUENCE

A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence.

ASSESSMENT

In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant.

RESULTS

For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635.

DATA CONCLUSION

Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Invasive lobular carcinoma of the breast detected with real-time virtual sonography: a case report

BACKGROUND

Invasive lobular carcinoma (ILC) sometimes presents with unique clinical, pathologic, and radiographic features. In this case report, we describe a patient with ILC, whose initial presentation consisted with symptoms secondary to bone-marrow dissemination. In addition, the breast primary was revealed only by magnetic resonance imaging (MRI) followed by real-time virtual sonography (RVS).

CASE PRESENTATION

A 51-year-old woman presented to our outpatient clinic with dyspnea on exertion. She had severe anemia (hemoglobin, 5.3 g/dL) and thrombocytopenia (platelet count, 31 × 10³/mL). Bone-marrow biopsy was performed to evaluate hematopoietic function. The pathologic diagnosis was bone-marrow carcinomatosis due to metastatic breast cancer. Initial mammography followed by ultrasonography (US) failed to detect the primary tumor. On MRI, a non-mass-enhancement lesion was observed. While second-look US also did not detect the lesion, it was clearly visualized with RVS. We were finally able to biopsy the breast lesion. The pathologic diagnosis was ILC positive for both estrogen receptor and progesterone receptor, with 1 + immunohistochemical staining for human epidermal growth factor receptor 2. This case of ILC was characterized by bone-marrow metastasis. Due to decreased cell adhesion, the risk of bone-marrow metastasis is higher in ILC than in invasive ductal carcinoma, the most prevalent type of breast cancer. Biopsy of the primary lesion, which was initially only detected with MRI, was successfully performed with clear visualization during RVS, which is based on the fusion of MRI and US images.

CONCLUSION

In this case report and literature review, we describe the unique clinical characteristics of ILC and a strategy for identifying primary lesions that are initially only visualized with MRI.

The road to breast cancer screening with diffusion MRI

Breast cancer is the leading cause of cancer in women with a huge medical, social and economic impact. Mammography (MMG) has been the gold standard method until now because it is relatively inexpensive and widely available. However, MMG suffers from certain limitations, such as exposure to X-rays and difficulty of interpretation in dense breasts. Among other imaging methods, MRI has clearly the highest sensitivity and specificity, and breast MRI is the gold standard for the investigation and management of suspicious lesions revealed by MMG. Despite this performance, MRI, which does not rely on X-rays, is not used for screening except for a well-defined category of women at risk, because of its high cost and limited availability. In addition, the standard approach to breast MRI relies on Dynamic Contrast Enhanced (DCE) MRI with the injection of Gadolinium based contrast agents (GBCA), which have their own contraindications and can lead to deposit of gadolinium in tissues, including the brain, when examinations are repeated. On the other hand, diffusion MRI of breast, which provides information on tissue microstructure and tumor perfusion without the use of contrast agents, has been shown to offer higher specificity than DCE MRI with similar sensitivity, superior to MMG. Diffusion MRI thus appears to be a promising alternative approach to breast cancer screening, with the primary goal of eliminating with a very high probability the existence of a life-threatening lesion. To achieve this goal, it is first necessary to standardize the protocols for acquisition and analysis of diffusion MRI data, which have been found to vary largely in the literature. Second, the accessibility and cost-effectiveness of MRI examinations must be significantly improved, which may become possible with the development of dedicated low-field MRI units for breast cancer screening. In this article, we will first review the principles and current status of diffusion MRI, comparing its clinical performance with MMG and DCE MRI. We will then look at how breast diffusion MRI could be implemented and standardized to optimize accuracy of results. Finally, we will discuss how a dedicated, low-cost prototype of breast MRI system could be implemented and introduced to the healthcare market.

Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI

Importance

The utilization of artificial intelligence for the differentiation of benign and malignant breast lesions in multiparametric MRI (mpMRI) assists radiologists to improve diagnostic performance.

Objectives

To develop an automated deep learning model for breast lesion segmentation and characterization and to evaluate the characterization performance of AI models and radiologists.

Materials and methods

For lesion segmentation, 2,823 patients were used for the training, validation, and testing of the VNet-based segmentation models, and the average Dice similarity coefficient (DSC) between the manual segmentation by radiologists and the mask generated by VNet was calculated. For lesion characterization, 3,303 female patients with 3,607 pathologically confirmed lesions (2,213 malignant and 1,394 benign lesions) were used for the three ResNet-based characterization models (two single-input and one multi-input models). Histopathology was used as the diagnostic criterion standard to assess the characterization performance of the AI models and the BI-RADS categorized by the radiologists, in terms of sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). An additional 123 patients with 136 lesions (81 malignant and 55 benign lesions) from another institution were available for external testing.

Results

Of the 5,811 patients included in the study, the mean age was 46.14 (range 11–89) years. In the segmentation task, a DSC of 0.860 was obtained between the VNet-generated mask and manual segmentation by radiologists. In the characterization task, the AUCs of the multi-input and the other two single-input models were 0.927, 0.821, and 0.795, respectively. Compared to the single-input DWI or DCE model, the multi-input DCE and DWI model obtained a significant increase in sensitivity, specificity, and accuracy (0.831 vs. 0.772/0.776, 0.874 vs. 0.630/0.709, 0.846 vs. 0.721/0.752). Furthermore, the specificity of the multi-input model was higher than that of the radiologists, whether using BI-RADS category 3 or 4 as a cutoff point (0.874 vs. 0.404/0.841), and the accuracy was intermediate between the two assessment methods (0.846 vs. 0.773/0.882). For the external testing, the performance of the three models remained robust with AUCs of 0.812, 0.831, and 0.885, respectively.

Conclusions

Combining DCE with DWI was superior to applying a single sequence for breast lesion characterization. The deep learning computer-aided diagnosis (CADx) model we developed significantly improved specificity and achieved comparable accuracy to the radiologists with promise for clinical application to provide preliminary diagnoses.

Diagnostic performance of DCE-MRI, multiparametric MRI and multimodality imaging for discrimination of breast non-mass-like enhancement lesions

OBJECTIVE

The aim of this study was to investigate and compare the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI, multiparametric MRI (mpMRI), and multimodality imaging (MMI) combining mpMRI and mammography (MG) for discriminating breast non-mass-like enhancement (NME) lesions.

METHODS

This retrospective study enrolled 193 patients with 199 lesions who underwent 3.0 T MRI and MG from January 2017 to December 2019. The features of DCE-MRI, turbo inversion recovery magnitude (TIRM), and diffusion-weighted imaging (DWI) were assessed by two breast radiologists. Then, all lesions were divided into microcalcification and non-microcalcification groups to assess the features of MG. Comparisons were performed between groups using univariate analyses. Then, multivariate analyses were performed to construct diagnostic models for distinguishing NME lesions. Diagnostic performance was evaluated by using the area under the curve (AUC) and the differences between AUCs were evaluated by using the DeLong test.

RESULTS

Overall (n = 199), mpMRI outperformed DCE-MRI alone (AUCmpMRI = 0.924 vs. AUCDCE-MRI = 0.884; p = 0.007). Furthermore, MMI outperformed both mpMRI and MG (the microcalcification group [n = 140]: AUCMMI = 0.997 vs. AUCmpMRI = 0.978, p = 0.018 and AUCMMI = 0.997 vs. AUCMG = 0.912, p < 0.001; the non-microcalcification group [n = 59]: AUCMMI = 0.857 vs. AUCmpMRI = 0.768, p = 0.044 and AUCMMI = 0.857 vs. AUCMG = 0.759, p = 0.039).

CONCLUSION & ADVANCES IN KNOWLEDGE

DCE-MRI combined with DWI and TIRM information could improve the diagnostic performance for discriminating NME lesions compared with DCE-MRI alone. Furthermore, MMI combining mpMRI and MG showed better discrimination than both mpMRI and MG.

A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging

BACKGROUND

The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS.

METHODS

This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology.

RESULTS

There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant.

CONCLUSIONS

Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.

Whole-lesion histogram analysis of apparent diffusion coefficient for the assessment of non-mass enhancement lesions on breast MRI

Objectives

To investigate the application of apparent diffusion coefficient (ADC) histogram analysis in differentiating between benign and malignant breast lesions detected as non-mass enhancement on MRI.

Materials and Methods

A retrospective study was conducted for 25 malignant and 26 benign breast lesions showing non-mass enhancement on breast MRI. An experienced radiologist without prior knowledge of the pathological results drew a region of interest (ROI) outlining the periphery of each lesion on the ADC map. A histogram was then made for each lesion. Following a univariate analysis of 18 summary statistics values, we conducted statistical discrimination after hierarchical clustering using Ward’s method. A comparison between the malignant and the benign groups was made using multiple logistic regression analysis and the Mann-Whitney U test. A P -value of less than 0.05 was considered statistically significant.

Results

Univariate analysis for the 18 summary statistics values showed the malignant group had greater entropy (P < 0.001) and lower uniformity (P < 0.001). While there was no significant difference in mean and skewness values, the malignant group tended to show a lower mean (P = 0.090) and a higher skewness (P = 0.065). Hierarchical clustering of the 18 summary statistics values identified four values (10th percentile, entropy, skewness, and uniformity) of which the 10th percentile values were significantly lower for the malignant group (P = 0.035).

Conclusions

Whole-lesion ADC histogram analysis may be useful for differentiating malignant from benign lesions which show non-mass enhancement on breast MRI.

Differentiation Between Benign and Metastatic Breast Lymph Nodes Using Apparent Diffusion Coefficients

The aim of this study was to determine the range of apparent diffusion coefficient (ADC) values for benign axillary lymph nodes in contrast to malignant axillary lymph nodes, and to define the optimal ADC thresholds for three different ADC parameters (minimum, maximum, and mean ADC) in differentiating between benign and malignant lymph nodes. This retrospective study included consecutive patients who underwent breast MRI from January 2017–December 2020. Two-year follow-up breast imaging or histopathology served as the reference standard for axillary lymph node status. Area under the receiver operating characteristic curve (AUC) values for minimum, maximum, and mean ADC (min ADC, max ADC, and mean ADC) for benign vs malignant axillary lymph nodes were determined using the Wilcoxon rank sum test, and optimal ADC thresholds were determined using Youden’s Index. The final study sample consisted of 217 patients (100% female, median age of 52 years (range, 22–81), 110 with benign axillary lymph nodes and 107 with malignant axillary lymph nodes. For benign axillary lymph nodes, ADC values (×10⁻³ mm²/s) ranged from 0.522–2.712 for mean ADC, 0.774–3.382 for max ADC, and 0.071–2.409 for min ADC; for malignant axillary lymph nodes, ADC values (×10⁻³ mm²/s) ranged from 0.796–1.080 for mean ADC, 1.168–1.592 for max ADC, and 0.351–0.688 for min ADC for malignant axillary lymph nodes. While there was a statistically difference in all ADC parameters (p<0.001) between benign and malignant axillary lymph nodes, boxplots illustrate overlaps in ADC values, with the least overlap occurring with mean ADC, suggesting that this is the most useful ADC parameter for differentiating between benign and malignant axillary lymph nodes. The mean ADC threshold that resulted in the highest diagnostic accuracy for differentiating between benign and malignant lymph nodes was 1.004×10⁻³ mm²/s, yielding an accuracy of 75%, sensitivity of 71%, specificity of 79%, positive predictive value of 77%, and negative predictive value of 74%. This mean ADC threshold is lower than the European Society of Breast Imaging (EUSOBI) mean ADC threshold of 1.300×10⁻³ mm²/s, therefore suggesting that the EUSOBI threshold which was recently recommended for breast tumors should not be extrapolated to evaluate the axillary lymph nodes.

Assessment of Suspected Breast Lesions in Early-Stage Triple-Negative Breast Cancer during Follow-Up after Breast-Conserving Surgery Using Multiparametric MRI

Background

The local recurrence rate of triple-negative breast cancer (TNBC) can be as high as 12%.The standard treatment for early-stage TNBC is breast-conserving surgery (BCS), followed by postoperative radiotherapy with or without chemotherapy. However, detection of the local recurrence of the disease after radiotherapy is a major issue.

Objective

The aim of this study was at investigating the role of dynamic and functional magnetic resonance imaging (MRI) during follow-up after BCS and radiotherapy with/without chemotherapy to differentiate between locoregional recurrence and postoperative fibrosis. Patients and Methods. This prospective study was conducted at the oncology, radiology, and pathology departments, Tanta University. It involved 50 patients with early-stage TNBC who were treated with BCS, followed by radiotherapy with/without chemotherapy. The suspected lesions were evaluated during the follow-up period by sonomammography. All patients were subjected to MRI, including conventional sequences, diffusion-weighted imaging (DWI), and dynamic postcontrast study.

Results

Ten cases were confirmed as recurrent malignant lesions. After contrast administration, they all exhibited irregular T1 hypodense lesions of variable morphology with diffusion restriction and positive enhancement. Eight cases displayed a type III curve, while two showed a type II curve. Histopathological assessment was consistent with the MRI findings in all eight cases. The combination of the data produced by DWI-MRI and dynamic contrast-enhanced (DCE) MRI resulted in 100%sensitivity, 92.5% specificity, 90.9% positive predictive value, 100% negative predictive value, and 98% accuracy.

Conclusion

Combination of DWI-MRI and DCE-MRI could have high diagnostic value for evaluating postoperative changes in patients with TNBC after BCS, followed by radiotherapy with/without chemotherapy. Trial Registrations. No trial to be registered.

Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses

Purpose

We aimed to assess the additional value of a radiomics-based signature for distinguishing between benign and malignant non-mass enhancement lesions (NMEs) on dynamic contrast-enhanced breast magnetic resonance imaging (breast DCE-MRI).

Methods

In this retrospective study, 232 patients with 247 histopathologically confirmed NMEs (malignant: 191; benign: 56) were enrolled from December 2017 to October 2020 as a primary cohort to develop the discriminative models. Radiomic features were extracted from one post-contrast phase (around 90s after contrast injection) of breast DCE-MRI images. The least absolute shrinkage and selection operator (LASSO) regression model was adapted to select features and construct the radiomics-based signature. Based on clinical and routine MR features, radiomics features, and combined information, three discriminative models were built using multivariable logistic regression analyses. In addition, an independent cohort of 72 patients with 72 NMEs (malignant: 50; benign: 22) was collected from November 2020 to April 2021 for the validation of the three discriminative models. Finally, the combined model was assessed using nomogram and decision curve analyses.

Results

The routine MR model with two selected features of the time-intensity curve (TIC) type and MR-reported axillary lymph node (ALN) status showed a high sensitivity of 0.942 (95%CI, 0.906 - 0.974) and low specificity of 0.589 (95%CI, 0.464 - 0.714). The radiomics model with six selected features was significantly correlated with malignancy (P<0.001 for both primary and validation cohorts). Finally, the individual combined model, which contained factors including TIC types and radiomics signatures, showed good discrimination, with an acceptable sensitivity of 0.869 (95%CI, 0.816 to 0.916), improved specificity of 0.839 (95%CI, 0.750 to 0.929). The nomogram was applied to the validation cohort, reaching good discrimination, with a sensitivity of 0.820 (95%CI, 0.700 to 0.920), specificity of 0.864 (95%CI,0.682 to 1.000). The combined model was clinically helpful, as demonstrated by decision curve analysis.

Conclusions

Our study added radiomics signatures into a conventional clinical model and developed a radiomics nomogram including radiomics signatures and TIC types. This radiomics model could be used to differentiate benign from malignant NMEs in patients with suspicious lesions on breast MRI.

The cone-beam breast computed tomography characteristics of breast non-mass enhancement lesions

BACKGROUND

Cone-beam computed tomography (CBBCT) of the breast is emerging as a way of improving breast cancer diagnostic yield.

PURPOSE

To find characteristics of non-mass enhancement (NME) lesions on breast CBBCT and to identify the characteristics that distinguish malignant and benign lesions.

MATERIAL AND METHODS

Breast CBBCT images of 84 NME lesions were analyzed. Internal enhancement distribution and patterns, calcification distribution and suspicious morphology, and ΔHU enhancement values were compared between post-contrast and pre-contrast malignant and benign lesions. Univariate analyses were applied to find the strongest indicators of malignancy, and logistic regression analysis was used to develop a fitting equation for the combined diagnostic model.

RESULTS

In the 84 NME lesions, the indicators of malignancy were as follows: segmental enhancement distribution (P = 0.011, 53.62% sensitivity, 86.67% specificity, 94.87% positive predictive value [PPV], and 28.89% negative predictive value [NPV]), clumped internal enhancement patterns (P = 0.017, 50.72% sensitivity, 86.67% specificity, 94.59% PPV, and 27.66% NPV), ΔHU ≥ 93.57 Hounsfield units (HU) (P = 0.004, 66.67% sensitivity, 73.33% specificity, 92.00% PPV, and 32.35% NPV), and NME lesions with calcification (P = 0.002, 36.23% sensitivity, 20.00% specificity, 82.14% PPV, and 67.57% NPV). The fitting equation for the combined diagnostic model was as follows: Logit (P) = -0.579 +1.318 × enhancement distribution + 1.000 × internal enhancement patterns + 1.539 × ΔHU value + 1.641 ×NME type.

CONCLUSION

Individual diagnostic criteria based on breast CBBCT characteristics (segmental enhancement distribution, clumped internal enhancement patterns, ΔHU values > 93.57 HU, and NME lesions with calcification) had high specificity and PPV; when combined, they had high sensitivity in predicting malignant NME lesions.

AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a "second opinion" review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine-learning (ML) techniques. In this review article, we describe applications of ML-based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state-of-the-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

The diagnostic dilemma with the plateau pattern of the time-intensity curve: can the relative apparent diffusion coefficient (rADC) optimise the ADC parameter for differentiating breast lesions?

AIM

To assess the performance of the apparent diffusion coefficient (ADC) and relative ADC (rADC) to differentiate benign from malignant breast lesions using the plateau pattern of the time-intensity curve (Type II TIC), including the impact of lesions-enhancement subtypes and menopausal status of patients.

MATERIALS AND METHODS

Between September 2016 and December 2019, 408 patients with 169 benign and 239 malignant lesions with Type II TIC underwent magnetic resonance imaging (MRI), including diffusion-weighted imaging, with b-values of 50 and 800 s/mm². ADC and rADC values were calculated by placing regions of interest (ROIs) on the lesion, the parenchyma of the normal breast, and the pectoralis major muscle. A receiver operating characteristic (ROC) curve was generated to compare the diagnostic performance of each parameter in distinguishing between benign and malignant breast lesions. Further classification was undertaken to study the discriminatory performance of each parameter in the different lesions enhancement subtypes (mass-like enhancement [MLE] and non-MLE [NMLE]) and menopausal status of patients (pre-menopausal and post-menopausal).

RESULTS

There was a significant difference in the ADC and rADC values between benign and malignant lesions. The sensitivities of lesion ADC, gland rADC, and muscle rADC were 79.29%, 77.51%, and 79.29%, respectively, with specificities of 94.56%, 82.01%, and 94.98%, respectively. The area under the ROC curve (AUC) of muscle rADC was the highest (AUC=0.92), especially in the MLE subtype (AUC=0.96), and was not affected by the menopausal status.

CONCLUSION

Muscle rADC and lesion ADC assessment improved the diagnostic performance of breast MRI in distinguishing between benign and malignant breast lesions with Type II TIC, especially muscle rADC in the MLE subtype.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

Qualitative characterization of breast tumors with diffusion-weighted imaging has comparable accuracy to quantitative analysis

PURPOSE

To evaluate the applicability and accuracy of a new qualitative diffusion-weighted imaging (DWI) assessment method in the characterization of breast tumors compared to quantitative ADC measurement and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

After review board approval, MRIs of 216 consecutive women with final diagnoses (131 malignant, 85 benign) were retrospectively analyzed. Two radiologists independently scored DWI and dynamic contrast-enhanced MRI (DCE-MRI) according to malignancy probability. Qualitative assessments were performed by combined analysis of tumor morphology and diffusion signal. Quantitative data was obtained from apparent diffusion coefficient (ADC) measurements. Lastly, descriptive DWI features were evaluated and recorded. Cohen's kappa, receiver operating characteristic and multivariate analyzes were applied.

RESULTS

Of malignant tumors, 97% were visible on DWI. Qualitative and quantitative DWI assessments provided comparable sensitivities of 89-94% and 88-92% and specificities of 51-61% and 59-67%, respectively. There was no statistical difference between the accuracies of qualitative and quantitative DWI (p ≥ 0.105). Best diagnostic values were obtained with DCE-MRI (sensitivity, 99-100%; specificity, 69-71%). Inter-reader agreement was moderate (kappa = 0.597) for qualitative DWI and substantial (kappa = 0.689) for DCE-MRI (p < 0.001). Agreement between qualitative DWI and DCE-MRI scores was moderate (kappa = 0.536 and 0.442). Visual diffusion signal, mass margin and shape were the most predictive features of malignancy on multivariate analysis of qualitative assessment.

CONCLUSION

Qualitative characterization of breast tumors on DWI has comparable accuracy to quantitative ADC analysis. This method might be used to make DWI more widely available with eliminating the need to a predetermined ADC threshold in tumor characterization. However, lower accuracy and inter-reader agreement of it compared to DCE-MRI should be considered.

Role of diffusion-weighted magnetic resonance imaging in assessment of mammographically detected asymmetric densities

Background

Asymmetric breast density is a potentially perplexing finding; it may be due to normal hormonal variation of the parenchymal pattern and summation artifact or it may indicate an underlying true pathology. The current study aimed to identify the role of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) values in the assessment of breast asymmetries.

Results

Fifty breast lesions were detected corresponding to the mammographic asymmetry. There were 35 (70%) benign lesions and 15 (30%) malignant lesions. The mean ADC value was 1.59 ± 0.4 × 10–3 mm2/s for benign lesions and 0.82 ± 0.3 × 10–3 mm2/s for malignant lesions. The ADC cutoff value to differentiate between benign and malignant lesions was 1.10 × 10–3 mm2/s with sensitivity 80%, specificity 88.6%, positive predictive value 75%, negative predictive value 91%, and accuracy 86%. Best results were achieved by implementation of the combined DCE-MRI and DWI protocol, with sensitivity 93.3%, specificity 94.3%, positive predictive value 87.5%, negative predictive value 97.1%, and accuracy 94%.

Conclusion

Dynamic contrast-enhanced MRI (DCE-MRI) was the most sensitive method for the detection of the underlying malignant pathology of breast asymmetries. However, it provided a limited specificity that may cause improper final BIRADS classification and may increase the unnecessary invasive procedures. DWI was used as an adjunctive method to DCE-MRI that maintained high sensitivity and increased specificity and the overall diagnostic accuracy of breast MRI examination. Best results can be achieved by the combined protocol of DCE-MRI and DWI.

Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography

INTRODUCTION

We aimed to interpret MR mammography (MRM) using the Kaiser scores for equivocal or inconclusive lesions on mammography (MG).

METHODS

Retrospective IRB-approved evaluation of 3623 MG for which MRM was deployed as a problem-solving tool, after inclusion-exclusion criteria were met. Three readers with different levels of experience assigned a final score from 1 to 11 based on the previously established tree classification system. Area under the curve (AUC) derived from receiver operating characteristic (ROC) analysis was used to determine the overall diagnostic performance for all lesions and separately for mass and non-mass enhancement. Sensitivity, specificity, and likelihood ratio values were obtained at different cut-off values of >4, > 5, and > 8 to rule in and rule out malignancy.

RESULT

Histopathology of 183 mass and 133 non-mass enhancement (NME) lesions show benign etiology in 95 and malignant in 221. The AUC was 0.796 [0.851 for mass and 0.715 for NME]. Applying the Kaiser score upgraded 202 lesions with correct prediction in 77 %, and downgraded 28 lesions with correct prediction in 60.8 %. Using a score <5 instead of <4 to rule out malignancy improved our diagnostic ability to correctly identify 100 % benign lesions. Applying Kaiser score correctly downgraded 60.8 % (17/28) lesions; thus avoiding biopsies in these. Using a high cut-off value>8 to rule-in malignancy, we correctly identified 59.7 % of lesions with 80 % specificity and positive likelihood ratio of 3.

CONCLUSION

The Kaiser score has clinical translation benefits when used as a problem-solving tool for inconclusive MG findings.

Significance of Non-Mass Enhancement in the Subareolar Region on Preoperative Breast Magnetic Resonance Imaging for Nipple-Sparing Mastectomy

PURPOSE

The eligibility for nipple-sparing mastectomy (NSM) regarding subareolar non-mass enhancement (NME) on breast magnetic resonance imaging (MRI) was not clear. This study aimed to evaluate the eligibility for NSM according to the NME-to-nipple distance on preoperative breast MRI.

METHODS

We identified patients with breast cancer who underwent mastectomy with NME suspected of malignancy in the subareolar region on preoperative breast MRI. The incidence of nipple invasion was pathologically evaluated according to the NME-to-nipple distance on breast MRI, and the clinicopathologic factors related to pathologic nipple invasion were analyzed.

RESULTS

Of 137 patients, 55 (40.1%) had NME extension to the nipple, 53 (38.7%) had radiologic distance less than 2 cm, and 29 (21.2%) had radiologic distance of 2 cm or more. The rate of pathologic nipple invasion was 52.7% (29 of 55) in patients with NME extension to nipple, 7.5% (4 of 53) in patients with NME-to-nipple distance less than 2 cm, and 3.4% (1 of 29) in patients with NME-to-nipple distance of 2 cm or more (P < .001). NME extension to the nipple was an independent risk factor for pathologic nipple invasion (odds ratio 21.702; 95% confidence interval, 2.613-180.225; P = .004). The survival outcome was not different between NSM and conventional total mastectomy/skin-sparing mastectomy in patients with radiologic distance less than 2 cm, but without NME extension to the nipple.

CONCLUSIONS

NSM is an acceptable procedure in patients with breast cancer with a low incidence of pathologic nipple invasion when there is no evidence of NME extension to the nipple on preoperative breast MRI.

Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features

PURPOSE

In this study, we propose a new computer-aided diagnosis (CADx) to distinguish between malign and benign mass and non-mass lesions in breast DCE-MRI. For this purpose, we introduce new frequency textural features.

METHODS

In this paper, we propose novel normalized frequency-based features. These are obtained by applying the dual-tree complex wavelet transform to MRI slices containing a lesion for specific decomposition levels. The low-pass and band-pass frequency coefficients of the dual-tree complex wavelet transform represent the general shape and texture features, respectively, of the lesion. The extraction of these features is computationally efficient. We employ a support vector machine to classify the lesions, and investigate modified cost functions and under- and oversampling strategies to handle the class imbalance.

RESULTS

The proposed method has been tested on a dataset of 80 patients containing 103 lesions. An area under the curve of 0.98 for the mass and 0.94 for the non-mass lesions is obtained. Similarly, accuracies of 96.9% and 89.8%, sensitivities of 93.8% and 84.6% and specificities of 98% and 92.3% are obtained for the mass and non-mass lesions, respectively.

CONCLUSION

Normalized frequency-based features can characterize benign and malignant lesions efficiently in both mass- and non-mass-like lesions. Additionally, the combination of normalized frequency-based features and three-dimensional shape descriptors improves the CADx performance.

Digital Analysis in Breast Imaging

Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Adding a Model-free Diffusion MRI Marker to BI-RADS Assessment Improves Specificity for Diagnosing Breast Lesions

Background The apparent diffusion coefficient (ADC) is a commonly used quantitative diffusion-weighted (DW) imaging marker in breast lesion assessment; however, reported ADC values to distinguish malignant and benign lesions show wide variability. Purpose To investigate the diagnostic performance of a tissue signature index (S-index) as a model-free diffusion marker to differentiate malignant and benign breast lesions. Materials and Methods This was a single-institution retrospective study of patients who underwent breast MRI from April 2017 to September 2018. Dynamic contrast-enhanced (DCE) MRI and DW imaging were performed with a 3-T MRI system. For DW imaging, three b values (0, 200, and 1500 sec/mm²) were used for Breast Imaging Reporting and Data Systems (BI-RADS) scoring and to calculate the S-index and a shifted ADC. The diagnostic performances of S-index, shifted ADC, and BI-RADS scoring were evaluated by using receiver operating coefficient analysis. Results The study involved 99 women (mean age, 54 years ± 14 [standard deviation]) with 69 malignant and 38 benign lesions. The S-index was higher for malignant lesions (mean, 75.9 ± 17.4) than for benign lesions (mean, 31.6 ± 21.0; P < .001). Overall diagnostic performance was identical for S-index and shifted ADC (area under the receiver operating characteristic curve [AUC], 0.95; 95% confidence interval [CI]: 0.91, 0.99) and slightly higher than for BI-RADS (AUC, 0.91; 95% CI: 0.87, 0.96; P = .22). The AUC of S-index combined with BI-RADS reached 0.98 (95% CI: 0.96, 1.00), higher than for BI-RADS alone (P < .001), yielding high sensitivity (65 of 69 [94%]; 95% CI: 85%, 98%) and specificity (36 of 38 [95%]; 95% CI: 81%, 99%). Significant differences were identified with the S-index for progesterone receptor and human epidermal growth factor receptor type 2 status (P = .003 and P < .001, respectively). Conclusion The signature index has the potential to enable classification of breast lesion types with high accuracy, especially in combination with dynamic contrast-enhanced MRI and correlates with histologic prognostic factors in invasive breast cancer. © RSNA, 2019 Online supplemental material is available for this article.

The MRI characteristics of non-mass enhancement lesions of the breast: associations with malignancy

OBJECTIVE:

The American College of Radiology updated the terms used for expressing the imaging characteristics of non-mass enhancement (NME) lesions in the fifth edition of the breast imaging-reporting data system (BI-RADS) lexicon. Both the distribution and internal enhancement descriptors were revised for NME lesions. Our aim was to determine the MRI characteristics of NME lesions and to investigate their association with malignancy.

METHODS:

The MRI results of 129 NME lesions were retrospectively evaluated. The medical files, biopsy results and follow-up findings of lesions were recorded. Patients who had benign biopsy and those who had stable or regressed lesions during follow-up were classified as benign. All MRI results had been obtained with a 1.5 Tesla Signa HDx MR system (GE Healthcare).

RESULTS:

Segmental and diffuse distribution along with clustered-ring internal enhancement were significantly associated with malignancy, while linear distribution and homogeneous enhancement pattern were associated with benignancy. Additionally, the plateau type (Type II) curve was significantly more frequent in malignant lesions. There was no association between the presence of cystic structures and the benign/malignant nature of the lesion. However, multivariate logistic regression showed that only segmental distribution and diffusion restriction were associated with malignancy.

CONCLUSION:

In the current study, segmental distribution, clustered-ring enhancement, Type II dynamic curve and the presence of diffusion restriction were found to be associated with malignancy. There is a requirement for multicenter studies which include higher numbers of patients in order to better evaluate lesions with rarer characteristics for distribution and enhancement pattern.

ADVANCES IN KNOWLEDGE:

Our aim in this study was to investigate the MRI characteristics of NME lesions. We have reported the MRI findings of NME lesions and have found that segmental distribution and clustered-ring enhancement patterns are significantly more frequent in malignant lesions.

Differentiation of breast tuberculosis and breast cancer using diffusion-weighted, T2-weighted and dynamic contrast-enhanced magnetic resonance imaging

Background

The use of multi-parametric magnetic resonance imaging (MRI) in the evaluation of breast tuberculosis (BTB).

Objectives

To evaluate the value of diffusion-weighted imaging (DWI), T2-weighted (T2W) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating breast cancer (BCA) from BTB.

Method

We retrospectively studied images of 17 patients with BCA who had undergone pre-operative MRI and 6 patients with pathologically proven BTB who underwent DCE-MRI during January 2014 to January 2015.

Results

All patients were female, with the age range of BTB patients being 23–43 years and the BCA patients being 31–74 years. Breast cancer patients had a statistically significant lower mean apparent diffusion coefficient (ADC) value (1072.10 ± 365.14), compared to the BTB group (1690.77 ± 624.05, p = 0.006). The mean T2-weighted signal intensity (T2SI) was lower for the BCA group (521.56 ± 233.73) than the BTB group (787.74 ± 196.04, p = 0.020). An ADC mean cut-off value of 1558.79 yielded 66% sensitivity and 94% specificity, whilst the T2SI cut-off value of 790.20 yielded 83% sensitivity and 83% specificity for differentiating between BTB and BCA. The homogeneous internal enhancement for focal mass was seen in BCA patients only.

Conclusion

Multi-parametric MRI incorporating the DWI, T2W and DCE-MRI may be a useful tool to differentiate BCA from BTB.

Quantitative evaluation of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for differential diagnosis and grading prediction of benign and malignant breast lesions

BACKGROUND

That breast carcinoma is the most common malignant lesion in women. This study aimed to differentiate benign from malignant breast lesions and to predict grading of the latter by comparing the diagnostic value of different parameters in intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

MATERIALS AND METHODS

Retrospective analysis was performed utilizing imaging and pathological data from 112 patients with 124 breast lesions that underwent IVIM-DWI examination with 3.0 T MRI. Out of 124, 47 benign and 77 malignant lesions were confirmed by pathological diagnosis. The diagnostic performance of f, D, and D* value to distinguish benign from malignant breast lesions, was evaluated using pathological results as the gold standard. Correlation between D value and Ki-67 index was evaluated to predict grading of malignant breast lesions.

RESULTS

The D value (0.99 ± 0.21) of patients with malignant lesions was significantly lower than that (1.34 ± 0.18) of patients harboring benign lesions (P = .00). The D* value (7.60 ± 2.10) in malignant lesion group was higher than that (6.83 ± 2.13) of the benign lesion group (P = .113). The f value (8.50 ± 2.13) in malignant lesion group was remarkably higher than that (7.68 ± 1.98) of benign lesion group (P = .035). For differential diagnosis of benign from malignant breast lesions, optimal diagnostic threshold of D value and f value were 1.21 and 7.86, respectively. The areas of D and f values under the ROC curve were 0.883 and 0.601, respectively. The sensitivity, specificity, and accuracy of D value were 83.0%, 86.7%, and 85.5%, respectively. Accordingly, those indexes of f value were 64.9%, 57.4%, and 62.1%, respectively. Furthermore, the Ki-67 staining index of malignant lesions was robustly negatively correlated with D value (r = -0.395, P < .01).

CONCLUSION

Concrete parameters of IVIM-DWI can help to improve the specificity and accuracy in differential diagnosis of breast benign and malignant lesions. D value is most relevant and valuable in predicting the grading of malignant breast lesions.

Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma?

OBJECTIVE

MRI is being used increasingly as a modality that can provide important information about breast cancer. Diffusion-weighted imaging (DWI) is an imaging technique from which apparent diffusion coefficient (ADC) values can be calculated in addition to obtaining important structural information which cannot be obtained from other imaging studies. We did not find any significant relationships between ADC values and prognostic factors, but did provide some explanations for conflicting results in the literature.

METHODS

The ADC results of 61 females with invasive ductal carcinomas were evaluated. DWI was performed and ADC values were calculated from the area in which restriction of diffusion was the highest in ADC mapping. B value was 500 and region of interest (ROI) was designated between 49 and 100 mm². Calculations were performed automatically by the device. Tissue samples were obtained for prognostic factor evaluation. The relationships between ADC and prognostic factors were investigated. Comparisons between groups were made with one-way ANOVA and Kruskal Wallis test. Pairwise comparisons were made with Dunn's test. Analyses of categorical variables were made with Chi-square test.

RESULTS

We found a weak negative correlation between ADC and Ki-67 values (r = -0.279; p = 0.029). When we compared ADC values in regard to tumour type, we found no significant differences for tumour grade, Ki-67 positivity, estrogen receptor positivity, progesterone receptor positivity, C-erb B2, lymphovascular invasion and ductal carcinoma in situ or lobular carcinoma in situ component. On a side note, we found that mean ADC values decreased as tumour grade increased; however, this was not statistically significant.

CONCLUSION

The literature contains studies that report conflicting results which may be caused by differences in B values, ROI area and magnetic field strength. Multicentre studies and systematic reviews of these findings may produce crucial data for the use of DWI in breast cancer. Advances in knowledge: To determine if any significant relationship exists between DWI findings and prognostic factors of breast cancer.

MRI in the Assessment of BI-RADS® 4 lesions

The American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) lexicon, which is used ubiquitously to standardize reporting of breast magnetic resonance imaging (MRI), provides 7 BI-RADS assessment categories to indicate the level of suspicion of malignancy and guide further management. A BI-RADS category 4 assessment is assigned when an imaging abnormality does not fulfill the typical criteria for malignancy, but is suspicious enough to warrant a recommendation for biopsy. The BI-RADS category 4 assessment covers a wide range of probability of malignancy, from >2 to <95%. MRI is an essential noninvasive technique in breast imaging and the role of MRI in the assessment of ACR BI-RADS 4 lesions is manifold. In lesions classified as suspicious on imaging with mammography, digital breast tomosynthesis, and sonography, MRI can aid in the noninvasive differentiation of benign and malignant lesions and obviate unnecessary breast biopsies. When the suspicion of cancer is confirmed with MRI, concurrent staging of disease for treatment planning can be accomplished. This article will provide a comprehensive overview of the role of breast MRI in the assessment of ACR BI-RADS 4 lesions. In addition, we will discuss strategies to decrease false positives and avoid false negative results when reporting MRI of the breast.

Breast Dynamic Contrast Enhanced MRI: Fibrocystic Changes Presenting as a Non-mass Enhancement Mimicking Malignancy

BACKGROUND

We aimed to analyse the morphokinetic features of breast fibrocystic changes (nonproliferative lesions, proliferative lesions without atypia and proliferative lesions with atypia) presenting as a non-mass enhancement (NME)in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examination.

PATIENTS AND METHODS

Forty-six patients with histologically proven fibrocystic changes (FCCs) were retrospectively reviewed, according to Breast Imaging Reporting and Data System (BI-RADS) lexicon. Prior to DCE-MRI examination, a unilateral breast lesion suspicious of malignancy was detected clinically, on mammography or breast ultrasonography.

RESULTS

The predominant features of FCCs presenting as NME in DCE-MRI examination were: unilateral regional or diffuse distribution (in 35 patients or 76.1%), heterogeneous or clumped internal pattern of enhancement (in 36 patients or 78.3%), plateau time-intensity curve (in 25 patients or 54.3%), moderate or fast wash-in (in 31 patients or 67.4%).Nonproliferative lesions were found in 11 patients (24%), proliferative lesions without atypia in 29 patients (63%) and lesions with atypia in six patients (13%), without statistically significant difference of morphokinetic features, except of the association of clustered microcysts with proliferative dysplasia without atypia.

CONCLUSIONS

FCCs presenting as NME in DCE-MRI examination have several morphokinetic features suspicious of malignancy, therefore requiring biopsy (BI-RADS 4). Nonproliferative lesions, proliferative lesions without atypia and proliferative lesions with atypia predominantly share the same predefined DCE-MRI morphokinetic features.

MRI in the differential diagnosis of primary architectural distortion detected by mammography

PURPOSE

We aimed to evaluate the diagnostic accuracy of a combination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) values in lesions that manifest with architectural distortion (AD) on mammography.

METHODS

All full-field digital mammography (FFDM) images obtained between August 2010 and January 2013 were reviewed retrospectively, and 57 lesions showing AD were included in the study. Two independent radiologists reviewed all mammograms and MRI data and recorded lesion characteristics according to the BI-RADS lexicon. The gold standard was histopathologic results from biopsies or surgical excisions and results of the two-year follow-up. Receiver operating characteristic curve analysis was carried out to define the most effective threshold ADC value to differentiate malignant from benign breast lesions. We investigated the sensitivity and specificity of FFDM, DCE-MRI, FFDM+DCE-MRI, and DCE-MRI+ADC.

RESULTS

Of the 57 lesions analyzed, 28 were malignant and 29 were benign. The most effective threshold for the normalized ADC (nADC) was 0.61 with 93.1% sensitivity and 75.0% specificity. The sensitivity and specificity of DCE-MRI combined with nADC was 92.9% and 79.3%, respectively. DCE-MRI combined with nADC showed the highest specificity and equal sensitivity compared with other modalities, independent of the presentation of calcification.

CONCLUSION

DCE-MRI combined with nADC values was more reliable than mammography in differentiating the nature of disease manifesting as primary AD on mammography.

Significant MRI indicators of malignancy for breast non-mass enhancement

OBJECTIVE

To explore and evaluate new malignant predictors of breast non-mass enhancement lesions using the new BI-RADS MRI lexicon.

METHODS

A dataset involving 422 consecutive women underwent breast 3.0 T MRI between January 2014 and July 2016 was assembled for this study. Each case was retrospectively reviewed by 3 radiologists. Eighty-four lesions that present non-mass enhancement in 79 patients were identified in the study. Dynamic contrast-enhanced MRI features were analyzed using univariate and multivariate analyses to identify significant indicators of malignancy.

RESULTS

Of 84 non-mass enhancement lesions, 52 (61.9%) were malignant and 32 (38.1%) were benign. Segmental distribution (P = 0.015 from univariate analysis; OR = 4.739, P = 0.008 from multivariate analysis), cluster ring enhancement (P = 0.017 from univariate analysis; OR = 3.601, P = 0.032 from multivariate analysis), time-intensity curve of plateau (P = 0.002 from univariate analysis; OR = 3.525, P = 0.027 from multivariate analysis) and phase to peak (P = 0.06 from univariate analysis; OR = 6.327, P = 0.015 from multivariate analysis) were significantly different between malignant and benign lesions.

CONCLUSIONS

This study demonstrated that segmental distribution, clustered ring enhancement, and short time to peak could act as new malignant predictors for breast non-mass enhancement detected on 3.0 T MRI.

Idiopathic granulomatous mastitis: magnetic resonance imaging findings with diffusion MRI

BACKGROUND

Idiopathic granulomatous mastitis (IGM) is a rare benign breast disease with unknown etiology which can mimic breast carcinoma, both clinically and radiologically. Magnetic resonance imaging (MRI) findings of IGM have been previously described; however there is no study evaluating diffusion-weighted MRI findings of IGM.

PURPOSE

To analyze conventional, dynamic contrast-enhanced, and diffusion-weighted MRI signal characteristics of IGM by comparing it with the contralateral normal breast parenchyma.

MATERIAL AND METHODS

A total of 39 patients were included in the study. On dynamic contrast-enhanced MRI, the distribution and enhancement patterns of the lesions were evaluated. We also detected the frequencies of involving quadrants, retroareolar involvement, accompanying abscess, and skin edema. T2-weighted (T2W) and STIR signal intensities and both mean and minimum apparent diffusion coefficient (ADC) values were compared with the contralateral normal parenchyma.

RESULTS

IGM showed significantly lower mean and minimum ADC values when compared with the normal parenchyma. Signal intensities on T2W and STIR sequences of the lesion were significantly higher than the normal parenchyma. On dynamic contrast-enhanced MRI, 7.7% of the patients had mass-like contrast enhancement, 92.3% of the patients had non-mass-like contrast enhancement. Abscess was positive in 33.3% of the patients.

CONCLUSION

As a result, IGM showed commonly non-mass-like lesions with restricted diffusion. Although it is a benign pathology, it may show clustered ring-like enhancement like malignant lesions.

Diagnostic Value of Diffusion-weighted Imaging and Apparent Diffusion Coefficient Values in the Differentiation of Breast Lesions, Histpathologic Subgroups and Correlatıon with Prognostıc Factors using 3.0 Tesla MR

OBJECTIVE

The aim of this study was to evaluate the effect of the apparent diffusion coefficient (ADC) and diffusion-weighted imaging in differentiating benign from malignant breast lesions, histopathologic subtypes of breast tumors, and to find a correlation with prognostic factors using 3T MR.

MATERIALS AND METHODS

A total of 165 patients aged between 16 and 78 years with 181 histopathologically-verifed breast lesions were enrolled in this study. A 3T MR system and bilateral phased array breast coil was used. Diffusion-weighted imaging was performed with spin echo "echo planar" with "b" values: 50, 400, and 800 seconds/mm². ADC values were calculated for normal fibroglandular tissue and breast lesions. ADC values of independent groups were compared using Student's t-test. ROC analysis was used to find a threshold ADC value in the differentiation of lesions.

RESULTS

The mean ADC values were 1.35±0.16 × 10^-3 mm²/s for normal fibroglandular tissue, 1.41±0.24 × 10^-3 mm²/s for benign breast lesions and 0.83±0.19 × 10^-3 mm²/s for malignant breast lesions. The AUC with ROC analysis was 0.945 and the threshold for ADC was 1.08 × 10^-3 mm²/s with a sensitivity and specificity of 92% and 92%, respectively. The threshold value for ADC ratio was 0.9 with 96% sensitivity and 89% specificity. The mean ADC of malignant breast lesions was statistically lower for benign lesions (p<0.01). We found no correlation between the mean ADC values and ER-PR receptor, Her2, and Ki-67 values.

CONCLUSION

Diffusion-weighted imaging has high diagnostic value with high sensitivity and specificity in differentiating malignant and benign breast lesions.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

OBJECTIVE:

To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM).

METHODS:

The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for "focus" category, (2) the shape, margin and internal enhancement for "mass" category and (3) the distribution and internal enhancement for "non-mass" category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs).

RESULTS:

The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of "irregular"-shape (PPV: 92.4%) and "non-circumscribed" margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones.

CONCLUSION:

We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies.

ADVANCES IN KNOWLEDGE:

(1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination.

Accuracy of combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging for breast cancer detection: a meta-analysis

BACKGROUND

Magnetic resonance imaging (MRI) is increasingly being used to examine patients with suspected breast cancer.

PURPOSE

To determine the diagnostic performance of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) for breast cancer detection.

MATERIAL AND METHODS

A comprehensive search of the PUBMED, EMBASE, Web of Science, and Cochrane Library databases was performed up to September 2014. Statistical analysis included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.2), and Meta-Disc 1.4 software programs.

RESULTS

Fourteen studies were analyzed, which included a total of 1140 patients with 1276 breast lesions. The pooled sensitivity and specificity of combined DCE-MRI and DWI were 91.6% and 85.5%, respectively. The pooled sensitivity and specificity of DWI-MRI were 86.0% and 75.6%, respectively. The pooled sensitivity and specificity of DCE-MRI were 93.2% and 71.1%. The area under the SROC curve (AUC-SROC) of combined DCE-MRI and DWI was 0.94, the DCE-MRI of 0.85. Deeks testing confirmed no significant publication bias in all studies.

CONCLUSION

Combined DCE-MRI and DWI had superior diagnostic accuracy than either DCE-MRI or DWI alone for the diagnosis of breast cancer.

Descriptors of Malignant Non-mass Enhancement of Breast MRI: Their Correlation to the Presence of Invasion

RATIONALE AND OBJECTIVES

This study aims to investigate the clinical significance of malignant non-mass enhancement (NME) descriptors in breast magnetic resonance images by assessing their correlation to the presence of invasion or lymph node metastasis.

MATERIALS AND METHODS

Three radiologists independently reviewed magnetic resonance images with malignant NMEs between January 2008 and December 2009. Distribution was assessed first, and then each of four internal enhancement patterns-clumped, clustered ring, branching, and hypointense area-was evaluated dichotomously (yes or no). Because clustered rings and hypointense areas were thought to be major structural elements of heterogeneous NMEs, they were also evaluated by integrating them into one collective descriptor we called the "heterogeneous structures." Chi-square test, Fisher exact test, or Student t test was used to analyze differences of variables by each reviewer. Positive predictive values (PPVs) of descriptors in predicting presence of invasion or lymph node metastasis were calculated. P < 0.05 was considered significant.

RESULTS

We included 131 malignant NMEs (76 in situ and 55 invasive) in 129 patients (two bilateral). All three observers' results showed clustered rings (PPVs 54.5%, 54.5%, 50.0%) (P = 0.0005, 0.038, 0.029) and hypointense areas (PPVs 63.6%, 61.5%, 73.9%) (P = 0.004, 0.024, 0.0006) to be significantly associated with invasion. When clustered rings and hypointense areas were integrated into heterogeneous structures, they were significantly associated with invasion (PPVs 54.3%, 53.3%, 51.8%) (P = 0.0003, 0.016, 0.003).

CONCLUSIONS

The NME descriptors clustered rings, hypoechoic areas, and heterogeneous structures, assessed collectively, were associated with invasive breast cancer.

Significance of Additional Non-Mass Enhancement in Patients with Breast Cancer on Preoperative 3T Dynamic Contrast Enhanced MRI of the Breast

Background

In preoperative assessment of breast cancer, MRI has been shown to identify more additional breast lesions than are detectable using conventional imaging techniques. The characterization of additional lesions is more important than detection for optimal surgical treatment. Additional breast lesions can be included in focus, mass, and non-mass enhancement (NME) on MRI. According to the fifth edition of the breast imaging reporting and data system (BI-RADS®), which includes several changes in the NME descriptors, few studies to date have evaluated NME in preoperative assessment of breast cancer.

Objectives

We investigated the diagnostic accuracy of BI-RADS descriptors in predicting malignancy for additional NME lesions detected on preoperative 3T dynamic contrast enhanced MRI (DCE-MRI) in patients with newly diagnosed breast cancer.

Patients and Methods

Between January 2008 and December 2012, 88 patients were enrolled in our study, all with NME lesions other than the index cancer on preoperative 3T DCE-MRI and all with accompanying histopathologic examination. The MRI findings were analyzed according to the BI-RADS MRI lexicon. We evaluated the size, distribution, internal enhancement pattern, and location of NME lesions relative to the index cancer (i.e., same quadrant, different quadrant, or contralateral breast).

Results

On histopathologic analysis of the 88 NME lesions, 73 (83%) were malignant and 15 (17%) were benign. Lesion size did not differ significantly between malignant and benign lesions (P = 0.410). Malignancy was more frequent in linear (P = 0.005) and segmental (P = 0.011) distributions, and benignancy was more frequent in focal (P = 0.004) and regional (P < 0.001) NME lesions. The highest positive predictive value (PPV) for malignancy occurred in segmental (96.8%), linear (95.1%), clustered ring (100%), and clumped (92.0%) enhancement. Asymmetry demonstrated a high positive predictive value of 85.9%. The frequency of malignancy was higher for NME lesions located in the same quadrant with the index cancer (P = 0.006), and benignancy was higher in the contralateral breast (P = 0.015). On multivariate analysis, linear (P = 0.001) and segmental (P = 0.005) distributions were significant predictors of malignancy.

Conclusion

The possibility of malignancy is strongly indicated when additional NME lesions show linear or segmental enhancement on preoperative 3T DCE-MRI in patients with recently diagnosed breast cancer.

Morphology evaluation of nonmass enhancement on breast MRI: Effect of a three-step interpretation model for readers' performances and biopsy recommendations

OBJECTIVE

To evaluate and compare the use of a newly introduced interpretation model for breast nonmass enhancement (NME, defined as an area of enhancement without a three-dimensional, space-occupying lesion) with the use of the standard interpretation method based on BI-RADS.

MATERIALS AND METHODS

Two expert and two less-experienced breast imaging radiologists performed reading sessions of 86 malignant and 64 benign NME lesions twice. First, radiologists characterized NME using BI-RADS descriptors and assessed the likelihood of malignancy and need for a biopsy. Second, the likelihood of malignancy and need for a biopsy were assessed with the use of the model, in which three-step characterization of morphological features were performed: (1) selection of distribution modifiers, (2) homogeneous vs. heterogeneous internal enhancement (IE) pattern, and (3) evaluation of presence of "clumped", "clustered ring enhancement (CRE)", and "branching" IE signs. Multireader-multicase receiver operating characteristic analysis was used to evaluate observers' performances. Univariate and multivariate logistic regression analyses were performed for morphology descriptors.

RESULTS

With use of the model, average Az of less-experienced radiologists (0.77-0.83; p=0.013) and average sensitivity of all radiologists (96.2-98.2%; p=0.007) improved significantly. NPV also improved but nonsignificantly (81.1-91.9%; p=0.055). Multivariate analyses of the second reading showed branching, clumped, and CRE signs to be significant predictors of malignancy in the results of 3, 2, and 2 readers, respectively.

CONCLUSION

The three-step interpretation model for NME has the potential to improve less-experienced radiologists' performances, making them comparable to expert breast imagers.