Contrast enhanced digital mammography versus magnetic resonance imaging for accurate measurement of the size of breast cancer

Factors Influencing Background Parenchymal Enhancement in Contrast-Enhanced Mammography Images

Background: The aim of this study is to evaluate the correlation between background parenchymal enhancement (BPE) and various patient-related and technical factors in recombined contrast-enhanced spectral mammography (CESM) images. Material and Methods: We assessed CESM images from 62 female patients who underwent CESM between May 2017 and October 2019, focusing on factors influencing BPE. A total of 235 images, all acquired using the same mammography machine, were analyzed. A region of interest (ROI) with a standard size of 0.75 to 1 cm2 was used to evaluate the minimal, maximal, and average pixel intensity enhancement. Additionally, the images were qualitatively assessed on a scale from 1 (minimal BPE) to 4 (marked BPE). We examined correlations with body mass index (BMI), age, hematocrit, hemoglobin levels, cardiovascular conditions, and the amount of pressure applied during the examination. Results: Our study identified a significant correlation between the amount of pressure applied during the examination and the BPE (Spearman's ρ = 0.546). Additionally, a significant but weak correlation was observed between BPE and BMI (Spearman's ρ = 0.421). No significant associations were found between BPE and menopausal status, cardiovascular preconditions, hematocrit, hemoglobin levels, breast density, or age. Conclusions: Patient-related and procedural factors significantly influence BPE in CESM images. Specifically, increased applied pressure and BMI are associated with higher BPE.

Breast cancer diagnosis from contrast-enhanced mammography using multi-feature fusion neural network

OBJECTIVES

To develop an end-to-end deep neural network for the classification of contrast-enhanced mammography (CEM) images to facilitate breast cancer diagnosis in the clinic.

METHODS

In this retrospective mono-centric study, patients who underwent CEM examinations from January 2019 to August 2021 were enrolled. A multi-feature fusion network combining low-energy (LE) and dual-energy subtracted (DES) images and dual view, as well as bilateral information, was trained and tested using a large CEM dataset with a diversity of breast tumors for breast lesion classification. Its generalization performance was further evaluated on two external datasets. Results were reported using AUC, accuracy, sensitivity, and specificity.

RESULTS

A total of 2496 patients (mean age, 53 years ± 12 (standard deviation)) were included and divided into a training set (1718), a validation set (255), and a testing set (523). The proposed CEM-based multi-feature fusion network achieved the best diagnosis performance with an AUC of 0.96 (95% confidence interval (CI): 0.95, 0.97), compared with the no-fusion model, the left-right fusion model, and the multi-feature fusion network with only LE image inputs. Our models reached an AUC of 0.90 (95% CI: 0.85, 0.94) on a full-field digital mammograph (FFDM) external dataset (86 patients), and an AUC of 0.92 (95% CI: 0.89, 0.95) on a CEM external dataset (193 patients).

CONCLUSION

The developed multi-feature fusion neural network achieved high performance in CEM image classification and was able to facilitate CEM-based breast cancer diagnosis.

CLINICAL RELEVANCE STATEMENT

Compared with low-energy images, CEM images have greater sensitivity and similar specificity in malignant breast lesion detection. The multi-feature fusion neural network is a promising computer-aided diagnostic tool for the clinical diagnosis of breast cancer.

KEY POINTS

• Deep convolutional neural networks have the potential to facilitate contrast-enhanced mammography-based breast cancer diagnosis. • The multi-feature fusion neural network reaches high accuracies in the classification of contrast-enhanced mammography images. • The developed model is a promising diagnostic tool to facilitate clinical breast cancer diagnosis.

False-Positive and False-Negative Contrast-enhanced Mammograms: Pitfalls and Strategies to Improve Cancer Detection

Contrast-enhanced mammography (CEM) is a relatively new breast imaging modality that uses intravenous contrast material to increase detection of breast cancer. CEM combines the structural information of conventional mammography with the functional information of tumor neovascularity. Initial studies have demonstrated that CEM and MRI perform with similar accuracies, with CEM having a slightly higher specificity (fewer false positives), although larger studies are needed. There are various reasons for false positives and false negatives at CEM. False positives at CEM can be caused by benign lesions with vascularity, including benign tumors, infection or inflammation, benign lesions in the skin, and imaging artifacts. False negatives at CEM can be attributed to incomplete or inadequate visualization of lesions, marked background parenchymal enhancement (BPE) obscuring cancer, lack of lesion contrast enhancement due to technical issues or less-vascular cancers, artifacts, and errors of lesion perception or characterization. When possible, real-time interpretation of CEM studies is ideal. If additional views are necessary, they may be obtained while contrast material is still in the breast parenchyma. Until recently, a limitation of CEM was the lack of CEM-guided biopsy capability. However, in 2020, the U.S. Food and Drug Administration cleared two devices to support CEM-guided biopsy using a stereotactic biopsy technique. The authors review various causes of false-positive and false-negative contrast-enhanced mammograms and discuss strategies to reduce these diagnostic errors to improve cancer detection while mitigating unnecessary additional imaging and procedures. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Interpreting contrast imaging to plan breast surgery

BACKGROUND

Contrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) are more accurate than conventional imaging (CI) for breast cancer staging. How adding CEM and MRI to CI might change the surgical plan is understudied.

METHODS

Surgical plans (breast conserving surgery (BCS), wider BCS, BCS with diagnostic excision (>1BCS), mastectomy) were devised by mock-MDT (radiologist, surgeon and pathology reports) according to disease extent on CI, CI + CEM and CI + MRI. Differences in the mock-MDT's surgical plans following the addition of CEM or MRI were investigated. Using pre-defined criteria, the appropriateness of the modified plans was assessed by comparing estimated disease extent on imaging with final pathology. Surgery performed was recorded from patient records.

RESULTS

Contrast imaging modified mock-MDT plans for 20 of 61(32.8%) breasts. The addition of CEM changed the plan in 16/20 (80%) and MRI in 17/20 breasts (85%). Identical changes were proposed by both CEM and MRI in 13/20 (65%) breasts. The modified surgical plan based on CI + CEM was possibly appropriate for 6/16 (37.5%), and CI + MRI in 9/17, (52.9%) breasts. The surgery performed was concordant with the mock-MDT plan for all 10 patients where the plans could be compared (BCS 1, >1 BCS 2 and mastectomy 7).

CONCLUSION

Adding CEM or MRI to CI changed mock-MDT plans in up to one third of women, but not all were appropriate. Changing surgical plans following addition of contrast imaging to CI without biopsy confirmation could lead to over or under-treatment.

Association of Clinical Factors and Degree of Early Background Parenchymal Enhancement on Contrast-Enhanced Mammography

BACKGROUND. Background parenchymal enhancement (BPE) may impact contrast-enhanced mammography (CEM) interpretation, although factors influencing the degree of BPE on CEM are poorly understood. OBJECTIVE. The purpose of our study was to evaluate relationships between clinical factors and the degree of early BPE on CEM. METHODS. This retrospective study included 207 patients (median age, 46 years) who underwent CEM between April 2020 and September 2021. Two radiologists independently assessed the degree of BPE on CEM as minimal, mild, moderate, or marked on the basis of two criteria (criterion 1, using the first of four obtained views; criterion 2, using the first two of four obtained views). The radiologists reached consensus for breast density on CEM. The EMR was reviewed for clinical factors. Radiologists' agreement for degree of BPE was assessed using weighted kappa coefficients. Univariable and multivariable analyses were performed to assess relationships between clinical factors and degree of BPE, treating readers' independent assessments as repeated measurements. RESULTS. Interreader agreement for degree of BPE, expressed as kappa, was 0.80 for both criteria. For both criteria, univariable analyses found degree of BPE to be negatively associated with age (both OR = 0.94), personal history of breast cancer (OR = 0.22-0.30), history of chemotherapy (OR = 0.18-0.21), history of radiation therapy (OR = 0.20-0.21), perimenopausal status (OR = 0.22-0.34), and postmenopausal status (OR = 0.10-0.11) and to be positively associated with dense breasts (OR = 4.13-4.26) and premenopausal status with irregular menstrual cycles (OR = 7.94-14.02). Among premenopausal patients with regular menstrual cycles, degree of BPE was lowest (using postmenopausal patients as reference) for patients in menstrual cycle days 8-14 (OR = 2.56-3.30). In multivariable analysis for both criteria, the only independent predictors of degree of BPE related to menstrual status and time of menstrual cycle (e.g., using premenopausal patients in days 1-7 as reference: OR = 0.21 for both criteria for premenopausal patients in days 8-14 and OR = 0.03-0.04 for postmenopausal patients). CONCLUSION. Clinical factors, including history of breast cancer or breast cancer treatment, breast density, menstrual status, and time of menstrual cycle, are associated with degree of early BPE on CEM. In premenopausal patients, the degree of BPE is lowest on days 8-14 of the menstrual cycle. CLINICAL IMPACT. Given the potential impact of BPE on diagnostic performance, the findings have implications for CEM scheduling and interpretation.

Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems?

OBJECTIVE

To evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared.

METHODS

In a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results.

RESULTS

Significant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases.

CONCLUSION

Mammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible.

KEY POINTS

• Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively. • Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement. • Lesion conspicuity is higher in cases of lower background enhancement on CESM.

Accuracy and precision of contrast enhanced mammography versus MRI for predicting breast cancer size: how "good" are they really?

OBJECTIVE

To evaluate and compare the accuracy and precision of contrast-enhanced mammography (CEM) vs MRI to predict the size of biopsy-proven invasive breast cancer.

METHODS

Prospective study, 59 women with invasive breast cancer on needle biopsy underwent CEM and breast MRI. Two breast radiologists read each patient's study, with access limited to one modality. CEM lesion size was measured using low-energy and recombined images and on MRI, the first post-contrast series. Extent of abnormality per quadrant was measured for multifocal lesions. Reference standards were size of largest invasive malignant lesion, invasive (PathInvasive) and whole (PathTotal). Pre-defined clinical concordance ±10 mm.

RESULTS

Mean patient age 56 years, 42 (71%) asymptomatic. Lesions were invasive ductal carcinoma 40 (68%) with ductal carcinoma in situ (31/40) in 78%, multifocal in 12 (20%). Median lesion size was 17 mm (invasive) and 27 mm (total), range (5-125 mm). Lin's concordance correlation coefficients for PathTotal 0.75 (95% CI 0.6, 0.84) and 0.71 (95% CI 0.56, 0.82) for MRI and contrast-enhanced spectral mammography (CESM) respectively. Mean difference for total size, 3% underestimated and 4% overestimated, and for invasive 41% and 50% overestimate on MRI and CESM respectively. LOAs for PathTotal varied from 60% under to a 2.4 or almost threefold over estimation. MRI was concordant with PathTotal in 36 (64%) cases compared with 32 (57%) for CESM. Both modalities concordant in 26 (46%) cases respectively.

CONCLUSION

Neither CEM nor MRI have sufficient accuracy to direct changes in planned treatment without needle biopsy confirmation.

ADVANCES IN KNOWLEDGE

Despite small mean differences in lesion size estimates using CEM or MRI, the 95% limits of agreement do not meet clinically acceptable levels.

Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis

Background: To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced breast magnetic resonance imaging (breast MRI). Like breast MRI, CEM enables tumour visualization by contrast accumulation. CEM seems to be a viable substitute for breast MRI. Methods: This systematic search assessed the diagnostic accuracy of these techniques in women with suspicious breast lesions on prior imaging or physical examination, who have undergone both breast MRI and CEM. CEM had to be performed on a commercially available system. The MRI sequence parameters had to be described sufficiently to ensure that standard breast MRI sequence protocols were used. Pooled values of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR), were estimated using bivariate mixed-effects logistic regression modeling. Hierarchical summary receiver operating characteristic curves for CEM and breast MRI were also constructed. Results: Six studies (607 patients with 775 lesions) met the predefined inclusion criteria. Pooled sensitivity was 96% for CEM and 97% for breast MRI. Pooled specificity was 77% for both modalities. DOR was 79.5 for CEM and 122.9 for breast MRI. Between-study heterogeneity expressed as the I2 -index was substantial with values over 80%. Conclusion: Pooled sensitivity was high for both CEM and breast MRI, with moderate specificity. The pooled DOR estimates, however, indicate higher overall diagnostic performance of breast MRI compared to CEM. Nonetheless, current scientific evidence is too limited to prematurely discard CEM as an alternative for breast MRI.

Accuracy of different sonomammographic imaging modalities in assessment of breast tumor size

Background

Accurate breast cancer size is crucial for staging and an important prognostic factor in patient management. Therapeutic decisions heavily depend on tumor size detection by radiological imaging. The purpose of our prospective comparative study is to compare the diagnostic accuracy of different sonomammographic breast imaging modalities, namely DM, DBT, CESM, 2D US and 3D US in the preoperative tumor size measurement.

Results

CESM, 3D US and 2D US achieved moderately strong correlation with the pathological size measurements, while (DM) and (DBT) showed fair correlation with the pathology. CESM showed the highest correlation coefficient (0.789), while (DBT) showed the lowest correlation coefficient (0.411). Regarding the agreement, there was good agreement of the size measured by CESM, 3D US and 2D US with the pathology as the ICC was (0.798), (0.769) and (0.624), respectively. The highest agreement with the pathology was achieved with CESM. The agreement of the size measured by (DM) and (DBT) with the pathology was moderate as the ICC was (0.439) and (0.416), respectively. The lowest agreement was achieved with the size measured by (DBT).

Conclusions

CESM and 3D US are more superior to DM, 2D US and DBT regarding preoperative size measurement. 3D US can be used as preoperative noninvasive technique, especially in patients with impaired renal function who cannot tolerate CESM.

Evaluation of a new method of calculating breast tumor volume based on automated breast ultrasound

Objective

To evaluate the effectiveness and advantages of a new method for calculating breast tumor volume based on an automated breast ultrasound system (ABUS).

Methods

A total of 42 patients (18–70 years old) with breast lesions were selected for this study. The Ivenia ABUS 2.0 (General Electric Company, USA) was used, with a probe frequency of 6–15 MHz. Adobe Photoshop CS6 software was used to calculate the pixel ratio of each ABUS image, and to draw an outline of the tumor cross-section. The resulting area (in pixels) was multiplied by the pixel ratio to yield the area of the tumor cross-section. The Wilcoxon signed rank test and Bland-Altman plot were used to compare mean differences and mean values, respectively, between the two methods.

Results

There was no significant difference between the tumor volumes calculated by pixel method as compared to the traditional method (P>0.05). Repeated measurements of the same tumor volume were more consistent with the pixel method.

Conclusion

The new pixel method is feasible for measuring breast tumor volume and has good validity and measurement stability.

Contrast-Enhanced Mammography versus Breast Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

BACKGROUND

Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic review aimed to summarize, critically analyse, and meta-analyse the available evidence regarding the role of CE-MRI and CEM in the early detection, diagnosis, and preoperative assessment of breast cancer.

METHODS

The search was performed on PubMed, Google Scholar, and Web of Science on 28 July 2021 using the following terms "breast cancer", "preoperative staging", "contrast-enhanced mammography", "contrast-enhanced spectral mammography", "contrast enhanced digital mammography", "contrast-enhanced breast magnetic resonance imaging" "CEM", "CESM", "CEDM", and "CE-MRI". We selected only those papers comparing the clinical efficacy of CEM and CE-MRI. The study quality was assessed using the QUADAS-2 criteria. The pooled sensitivities and specificity of CEM and CE-MRI were computed using a random-effects model directly from the STATA "metaprop" command. The between-study statistical heterogeneity was tested (I2-statistics).

RESULTS

Nineteen studies were selected for this systematic review. Fifteen studies (1315 patients) were included in the metanalysis. Both CEM and CE-MRI detect breast lesions with a high sensitivity, without a significant difference in performance (97% and 96%, respectively).

CONCLUSIONS

Our findings confirm the potential of CEM as a supplemental screening imaging modality, even for intermediate-risk women, including females with dense breasts and a history of breast cancer.

Preoperative MRI features predict failed breast-conserving surgery: construction of a predictive model

Background

Breast-conserving surgery (BCS) is the preferred method for early breast cancer, and the accurate preoperative prediction of the feasibility of BCS can formulate the surgical plan and reduce the violation of the patient's will. The present study proposed to explore the preoperative magnetic resonance imaging (MRI) features associated with failed BCS and constructed an MRI-based model to predict BCS.

Methods

This retrospective study included patients between March 2015 and July 2016, who planned to undergo BCS, had preoperative MRI examination, and had at least 2 years of follow-up. A total of 30 patients with failed BCS were identified and matched with 90 patients with successful BCS (ratio 1:3) according to age, neoadjuvant therapy, and hormone receptor expression. The patients were divided into the training group for model construction and the testing group for model validation. The MRI features, including the site of the tumor, the lesion type, and the lesion and breast volume, were compared between failure and successful BCS groups. A multivariate logistic model for predicting failed BCS was constructed using independent factors associated with failed BCS from the training group and was evaluated in the testing group. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve.

Results

The mean age of the cohort was 45.7±10.3 years. A significantly more non-mass lesion and multifocality, the larger volume of lesion, and the ratio of lesion and breast volume were observed in failed BCS group compared to the successful BCS group. The ratio of lesion and breast volume and multifocality were independent factors associated with failed BCS, odds ratios were 1.044 (95% CI: 1.016-1.074) and 11.161 (95% CI: 1.739-71.652), respectively. An MRI-based model for predicting failed BCS was established, the area under the ROC curves in the training and testing group were 0.902 and 0.821, respectively.

Conclusions

This model might help clinicians predict failed BCS preoperatively and make an accurate surgical strategy.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions

OBJECTIVES

To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images.

METHODS

This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001).

CONCLUSION

Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance.

KEY POINTS

• Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

The diagnostic value of contrast-enhanced 2D mammography in everyday clinical use

Contrast-enhanced mammography (CEM) has shown to be superior to full-field digital mammography (FFDM), but current results are dominated by studies performed on systems by one vendor. Information on diagnostic accuracy of other CEM systems is limited. Therefore, we aimed to evaluate the diagnostic performance of CEM on an alternative vendor’s system. We included all patients who underwent CEM in one hospital in 2019, except those with missing data or in whom CEM was used as response monitoring tool. Three experienced breast radiologists scored the low-energy images using the BI-RADS classification. Next, the complete CEM exams were scored similarly. Histopathological results or a minimum of one year follow-up were used as reference standard. Diagnostic performance and AUC were calculated and compared between low-energy images and the complete CEM examination, for all readers independently as well as combined. Breast cancer was diagnosed in 23.0% of the patients (35/152). Compared to low-energy images, overall CEM sensitivity increased from 74.3 to 87.6% (p < 0.0001), specificity from 87.8 to 94.6% (p = 0.0146). AUC increased from 0.872 to 0.957 (p = 0.0001). Performing CEM on the system tested, showed that, similar to earlier studies mainly performed on another vendor’s systems, both sensitivity and specificity improved when compared to FFDM.

Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment

Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.

Contrast-enhanced Mammography: A Guide to Setting Up a New Clinical Program

Contrast-enhanced mammography (CEM) is gaining rapid traction following the U.S. Food and Drug Administration approval for diagnostic indications. Contrast-enhanced mammography is an alternative form of mammography that uses a dual-energy technique for image acquisition after the intravenous administration of iodinated contrast material. The resulting exam includes a dual set of images, one that appears similar to a routine 2D mammogram and one that highlights areas of contrast uptake. Studies have shown improved sensitivity compared to mammography and similar performance to contrast-enhanced breast MRI. As radiology groups incorporate CEM into clinical practice they must first select the indications for which CEM will be used. Many practices initially use CEM as an MRI alternative or in cases recommended for biopsy. Practices should then define the CEM clinical workflow and patient selection to include ordering, scheduling, contrast safety screening, and managing imaging on the day of the exam. The main equipment requirements for performing CEM include CEM-capable mammography equipment, a power injector for contrast administration, and imaging-viewing capability. The main staffing requirements include personnel to place the intravenous line, perform the CEM exam, and interpret the CEM. To safely and appropriately perform CEM, staff must be trained in their respective roles and to manage potential contrast-related events. Lastly, informing referring colleagues and patients of CEM through marketing campaigns is helpful for successful implementation.

Application of intravoxel incoherent motion diffusion-weighted imaging in differential diagnosis and molecular subtype analysis of breast cancer

OBJECTIVE

To explore the application of incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis and molecular subtype analysis of breast cancer.

METHODS

The clinical data of 225 patients with breast masses were selected, including breast cancers (n = 135) and benign breast tumors (n = 90). According to pathological results, breast cancers were divided into four subtypes: Luminal A (n = 24), Luminal B (n = 57), HER-2-overexpression (n = 27) and triple-negative breast cancers (n = 27). The patients were detected by IVIM-DWI, and then the average diffusion coefficient (ADC), perfusion fraction (f) value, true dispersion coefficient (D) value and false dispersion coefficient (D*) value were compared and analyzed. The above index were used to identify breast cancer and its molecular subtypes by using the receiver operating characteristic (ROC) curve.

RESULTS

The ADC, D and D*-value in breast cancer group were significantly lower than those in benign tumor group, while the f-value in breast cancer group was higher than that in benign tumor group (P<0.001); The ADC, D, D*, f-value and the combination of four have high diagnostic value in breast cancer; The D-value in PR-positive group was higher than that in the PR-negative group, while it was lower in PR-positive group (P<0.05), and the ADC, D and D*-value in the ER-positive group were significantly lower than those in the ER-negative group (P<0.001); The f-value in HER-2 positive group was higher than that in human epidermal growth factor receptor-2 (HER-2) negative group (P<0.001); The ADC and D-value of Ki-67 high-expression was lower than those of Ki-67 low-expression, while the D-value of Ki-67 high-expression was higher than that of Ki-67 low expression group (P<0.05); The ADC, D, D*, f-value and the combination of four have high diagnostic value in triple negative breast cancer.

CONCLUSION

IVIM-DWI technology has a significant value in differential diagnosis of benign and malignant breast tumors, and the relevant parameters of IVIM-DWI technology have definite value in the differential diagnosis of breast cancer molecular typing.

Background Parenchymal Enhancement on Contrast-Enhanced Spectral Mammography: Influence of Age, Breast Density, Menstruation Status, and Menstrual Cycle Timing

To evaluate the relationship of the extent and quantitative intensity of background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM) with age, breast density, menstruation status, and menstrual cycle timing. This retrospective study included women who underwent CESM from July 2017 to March 2019 and who had menstruation status records. BPE category assessment was performed subjectively. BPE intensity was quantitatively measured using regions-of-interest. 208 subjects were included (150 were regular menstrual cycle and 58 were postmenopausal). The breast density was classified as category B in 11 subjects, category C in 231 subjects, and category D in 23 subjects. Subjects based on menstrual cycle timing, 24 at days 1-7, 55 at days 8-14, 48 at days 15-21, and 23 at days 22-28. Both quantitative and categorical analyses show a weak negative correlation between BPE and age in all subjects, but there was no significant correlation in premenopausal patients. Both the BPE pixel intensity value and BPE category was significantly lower in postmenopausal patients than in premenopausal patients, and there was no significant difference in breast density according to BPE. The minimum and maximum pixel values of BPE on days 8-14 of the menstrual cycle was significantly lower than those on days 15-21. There was no correlation between BPE level and menstrual cycle timing. Breast density with category D was more likely to have a lower BPE level than category C. We show here that BPE level is affected by menstruation status and menstrual cycle timing. We suggest that CESM should not be performed on days 15-21 of the menstrual cycle, but on days 8-14.

Contrast-Enhanced Mammography: A Scientific Review

In this paper we provide an overview of contrast-enhanced mammography (CEM) and a review of the published literature in order to provide a picture of the current state of the evidence on the performance of CEM. Clinical research was fairly sparse following the demonstration of the technique in research subjects about 18 years ago, but the number of publications rapidly increased following commercialization 9 years ago, and even more so in the last 5 years. Initial studies compared CEM with mammography, and clearly showed that CEM could detect cancers not visible on mammography. More recent studies have primarily focused on comparing the performance of CEM with contrast-enhanced magnetic resonance imaging (MRI) in selected cohorts. These studies have almost uniformly shown CEM and MRI to have similar sensitivities, with sensitivity and accuracy showing more variability from study to study. With increasing clinical use, a large number of retrospective reviews of CEM have appeared, showing utility of CEM in the diagnostic clinical setting. Most recently, a small number of papers have been published looking at CEM for high-risk and dense breast screening, two potentially large applications of the technique, showing it to outperform mammography in both populations. CEM has clearly been shown to have clinical utility, but more prospective studies, including screening studies, are needed to further evaluate its performance, especially in comparison with MRI.

Diagnostic Performance of MRI, Molecular Breast Imaging, and Contrast-enhanced Mammography in Women with Newly Diagnosed Breast Cancer

Background Staging newly diagnosed breast cancer by using dynamic contrast material-enhanced MRI is limited by access, high cost, and false-positive findings. The utility of contrast-enhanced mammography (CEM) and ^99mTc sestamibi-based molecular breast imaging (MBI) in this setting is largely unknown. Purpose To compare extent-of-disease assessments by using MRI, CEM, and MBI versus pathology in women with breast cancer. Materials and Methods In this HIPAA-compliant prospective study, women with biopsy-proven breast cancer underwent MRI, CEM, and MBI between October 2014 and April 2018. Eight radiologists independently interpreted each examination result prospectively and were blinded to interpretations of findings with the other modalities. Visibility of index malignancies, lesion size, and additional suspicious lesions (malignant or benign) were compared during pathology review. Accuracy of index lesion sizing and detection of additional lesions in women without neoadjuvant chemotherapy were compared. Results A total of 102 women were enrolled and 99 completed the study protocol (mean age, 51 years ± 11 [standard deviation]; range, 32-77 years). Lumpectomy or mastectomy was performed in 71 women (79 index malignancies) without neoadjuvant chemotherapy and in 28 women (31 index malignancies) with neoadjuvant chemotherapy. Of the 110 index malignancies, MRI, CEM, and MBI depicted 102 (93%; 95% confidence interval [CI]: 86%, 97%), 100 (91%; 95% CI: 84%, 96%), and 101 (92%; 95% CI: 85%, 96%) malignancies, respectively. In patients without neoadjuvant chemotherapy, pathologic size of index malignancies was overestimated with all modalities (P = .02). MRI led to overestimation of 24% (17 of 72) of malignancies by more than 1.5 cm compared with 11% (eight of 70) with CEM and 15% (11 of 72) with MBI. MRI depicted more (P = .007) nonindex lesions, with sensitivity similar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of 46 [28%; 95% CI: 17%, 44%] for MRI; 14 of 27 [52%; 95% CI: 32%, 71%] for CEM; and 11 of 25 [44%; 95% CI: 24%, 65%] for MBI (overall P = .01). Conclusion Contrast-enhanced mammography, molecular breast imaging, and MRI showed similar detection of all malignancies. MRI depicted more nonindex suspicious benign lesions than did contrast-enhanced mammography or molecular breast imaging, leading to lower positive predictive value of additional biopsies. All three modalities led to overestimation of index tumor size, particularly MRI. © RSNA, 2019 Online supplemental material is available for this article.