Contrast-enhanced digital mammography

Contrast-enhanced Mammography versus MR Imaging of the Breast

Breast MR imaging and contrast-enhanced mammography (CEM) are both techniques that employ intravenously injected contrast agent to assess breast lesions. This approach is associated with a very high sensitivity for malignant lesions that typically exhibit rapid enhancement due to the leakiness of neovasculature. CEM may be readily available at the breast imaging department and can be performed on the spot. Breast MR imaging provides stronger enhancement than the x-ray-based techniques and offers higher sensitivity. From a patient perspective, both modalities have their benefits and downsides; thus, patient preference could also play a role in the selection of the imaging technique.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

OBJECTIVE

To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer.

METHODS

From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2-  and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered.

RESULTS

The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set.

CONCLUSIONS

The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Monte-Carlo study of contrast-enhanced spectral mammography with cadmium telluride photon-counting x-ray detectors

BACKGROUND

Contrast-enhanced spectral mammography (CESM) with photon-counting x-ray detectors (PCDs) can be used to improve the classification of breast cancers as benign or malignant. Commercially-available PCD-based mammography systems use silicon-based PCDs. Cadmium-telluride (CdTe) PCDs may provide a practical advantage over silicon-based PCDs because they can be implemented as large-area detectors that are more easily adaptable to existing mammography systems.

PURPOSE

The purpose of this work is to optimize CESM implemented with CdTe PCDs and to investigate the influence of the number of energy bins, electronic noise level, pixel size, and anode material on image quality.

METHODS

We developed a Monte Carlo model of the energy-bin-dependent modulation transfer functions (MTFs) and noise power spectra, including spatioenergetic noise correlations. We validated model predictions using a CdTe PCD with analog charge summing for charge-sharing suppression. Using the ideal-observer detectability, we optimized CESM for the task of detecting a 7-mm-diameter iodine nodule embedded in a breast with 50% glandularity. We optimized the tube voltage, beam filtration, and the location of energy thresholds for 50 and 100- μ $\mu$ m pixels, tungsten and molybdenum anodes, and two electronic noise levels. One of the electronic noise levels was that of the experimental system; the other was half that of the experimental system. Optimization was performed for CdTe PCDs with two or three energy bins. We also estimated the impact of anatomic noise due to background parenchymal enhancement and computed the minimum detectable iodine area density in the presence of quantum and anatomic noise.

RESULTS

Model predictions of the MTFs and noise power spectra agreed well with experiment. For optimized systems, adding a third energy bin increased quantum noise levels and reduced detectability by ∼55% compared to two-bin approaches that simply suppress contrast between fibroglandular and adipose tissue. Decreasing the electronic noise standard deviation from 3.4 to 1.7 keV increased iodine detectability by ∼5% and ∼30% for two-bin imaging and three-bin imaging, respectively. After optimizing for tube voltage, beam filtration, and the location of energy thresholds, there was ∼a 3% difference in iodine detectability between molybdenum and tungsten anodes for two-bin imaging, but for three-bin imaging, molybdenum anodes provided up to 14% increase in detectability relative to tungsten anodes. Anatomic noise decreased iodine detectability by 15% to 40%, with greater impact for lower electronic noise settings and larger pixel sizes.

CONCLUSIONS

For CESM implemented with CdTe PCDs, (1) quantitatively-accurate three-material decompositions using three energy bins are associated with substantial increases in quantum noise relative to two-energy-bin approaches that simply suppress contrast between fibroglandular and adipose tissues; (2) tungsten and molybdenum anodes can provide nearly equal iodine detectability for two-bin imaging, but molybdenum provides a modest detectability advantage for three-bin imaging provided that all other technique parameters are optimized; (3) reducing pixel sizes from 100 to 50  μ $\mu$ m can reduce detectability by up to 20% due to charge sharing; (4) anatomic noise due to background parenchymal enhancement is estimated to have a substantial impact on lesion visibility, reducing detectability by approximately 30%.

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part I - Macroscopic scales

BACKGROUND

X-ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used.

PURPOSE

To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters).

METHODS

Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast-enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC-GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons).

RESULTS

The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD.

CONCLUSIONS

For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry.

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part II - Microscopic scales

BACKGROUND

Although the benefits of breast screening and early diagnosis are known for reducing breast cancer mortality rates, the effects and risks of low radiation doses to the cells in the breast are still ongoing topics of study.

PURPOSE

To study specific energy distributions (f ( z , D g ) $f(z,D_{g})$ ) in cytoplasm and nuclei of cells corresponding to glandular tissue for different x-ray breast imaging modalities.

METHODS

A cubic lattice (500 μm length side) containing 4064 spherical cells was irradiated with photons loaded from phase space files with varying glandular voxel doses (D g $D_{g}$ ). Specific energy distributions were scored for nucleus and cytoplasm compartments using the PENELOPE (v. 2018) + penEasy (v. 2020) Monte Carlo (MC) code. The phase space files, generated in part I of this work, were obtained from MC simulations in a voxelized anthropomorphic phantom corresponding to glandular voxels for different breast imaging modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), contrast enhanced digital mammography (CEDM) and breast CT (BCT).

RESULTS

In general, the average specific energy in nuclei is higher than the respective glandular dose scored in the same region, by up to 10%. The specific energy distributions for nucleus and cytoplasm are directly related to the magnitude of the glandular dose in the voxel (D g $D_{g}$ ), with little dependence on the spatial location. For similarD g $D_{g}$ values,f ( z , D g ) $f(z,D_{g})$ for nuclei is different between DM/DBT and CEDM/BCT, indicating that distinct x-ray spectra play significant roles inf ( z , D g ) $f(z,D_{g})$ . In addition, this behavior is also present when the specific energy distribution (F g ( z ) $F_{g}(z)$ ) is considered taking into account the GDD in the breast.

CONCLUSIONS

Microdosimetry studies are complementary to the traditional macroscopic breast dosimetry based on the mean glandular dose (MGD). For the same MGD, the specific energy distribution in glandular tissue varies between breast imaging modalities, indicating that this effect could be considered for studying the risks of exposing the breast to ionizing radiation.

Breast Imaging Physics in Mammography (Part I)

Breast cancer is the most frequently diagnosed neoplasm in women in Italy. There are several risk factors, but thanks to screening and increased awareness, most breast cancers are diagnosed at an early stage when surgical treatment can most often be conservative and the adopted therapy is more effective. Regular screening is essential but advanced technology is needed to achieve quality diagnoses. Mammography is the gold standard for early detection of breast cancer. It is a specialized technique for detecting breast cancer and, thus, distinguishing normal tissue from cancerous breast tissue. Mammography techniques are based on physical principles: through the proper use of X-rays, the structures of different tissues can be observed. This first part of the paper attempts to explain the physical principles used in mammography. In particular, we will see how a mammogram is composed and what physical principles are used to obtain diagnostic images.

Contrast enhanced mammography: focus on frequently encountered benign and malignant diagnoses

Contrast-enhanced mammography (CEM) is becoming a widely adopted modality in breast imaging over the past few decades and exponentially so over the last few years, with strong evidence of high diagnostic performance in cancer detection. Evidence is also growing indicating comparative performance of CEM to MRI in sensitivity with fewer false positive rates. As application of CEM ranges from potential use in screening dense breast populations to staging of known breast malignancy, increased familiarity with the modality and its implementation, and disease processes encountered becomes of great clinical significance. This review emphasizes expected normal findings on CEM followed by a focus on examples of the commonly encountered benign and malignant pathologies on CEM.

Patient perspectives on repeated contrast-enhanced mammography and magnetic resonance during neoadjuvant chemotherapy of breast cancer

BACKGROUND

The burden perceived by the patient of repeated imaging required for neoadjuvant chemotherapy (NAC) monitoring warrants attention due to the increased use of NAC and imaging.

PURPOSE

To evaluate and compare the experienced burden associated with repeated contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) during NAC for breast cancer from the patient perspective.

MATERIAL AND METHODS

Approval from the ethics committee and written informed consent were obtained. In this prospective study, CEM and MRI were performed on 38 patients with breast cancer before, during, and after NAC in a tertiary cancer center. The experienced burden was evaluated with a self-reported questionnaire addressing duration, comfort, anxiety, positioning, and intravenous contrast administration, each measured on a 5-point Likert scale. The participants were asked their preference between CEM or MRI. Statistical comparisons were performed and P<0.05 was considered significant.

RESULTS

Most participants (n = 29, 76%) preferred CEM over MRI (P = 0.0008). CEM was associated with a significantly shorter duration (P < 0.001), greater overall comfort (P < 0.01), more comfortable positioning (P = 0.01), and lower anxiety (P = 0.03). Intravenous contrast administration perception revealed no significant difference. Only 4 (10%) participants preferred MRI over CEM, due to the absence of breast compression.

CONCLUSION

In the hypothetical scenario of equal diagnostic accuracy, most participants preferred CEM and compared CEM favorably to MRI in all investigated features at repeated imaging required for NAC response assessment. Our results indicate that repeated examinations with CEM is well tolerated and constitutes a patient-friendly alternative for NAC imaging monitoring in breast cancer.

Evaluation of exposure factors of dual-energy contrast-enhanced mammography to optimize radiation dose with improved image quality

Background

Dual-energy contrast-enhanced mammography (DECEM) is an advanced breast imaging technique of digital mammography.

Purpose

To assess the total radiation dose received from complete DECEM using different combinations of exposure parameters for low- and high-energy images.

Materials and methods

A dedicated phantom with three different concentrations of iodine inserts was used. Each iodine insert was 10 mm in diameter and concentration of 1.0 mgI/cm³, 2.0 mgI/cm³, and 4.0 mgI/cm³. The phantom was exposed at varying kVp levels. Mean glandular dose (MGD) was estimated. Contrast to noise ratio (CNR) and figure of merit (FOM) of the iodine inserts were used to assess the image quality.

Results

The optimum CNR of the recombined images was obtained by using 28 kVp + 49 kVp tube voltage combination for 50 mm thickness, 50% fibroglandular phantom only with a 26% dose increase compared to the highest voltages (32 kVp + 49 kVp) that can be used for low energy (LE) and high energy (HE) imaging. The CNR value was increased with increasing iodine concentration (R² > 0.99).

Conclusion

The use of as low as possible tube voltage for the LE imaging of standard 50% fibroglandular–50% adipose, 50 mm thickness breast while using the highest tube voltage for HE imaging has reduced the MGD while keeping optimum image quality.

Use of Diagnostic Imaging Modalities in Modern Screening, Diagnostics and Management of Breast Tumours 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

Breast radiologists and nuclear medicine specialists updated their previous recommendation/guidance at the 4th Hungarian Breast Cancer Consensus Conference in Kecskemét. A recommendation is hereby made that breast tumours should be screened, diagnosed and treated according to these guidelines. These professional guidelines include the latest technical developments and research findings, including the role of imaging methods in therapy and follow-up. It includes details on domestic development proposals and also addresses related areas (forensic medicine, media, regulations, reimbursement). The entire material has been agreed with the related medical disciplines.

Impact of preoperative staging with contrast-enhanced mammography for localized breast cancer management

OBJECTIVE

A precise evaluation of the disease extent is mandatory before surgery for early breast cancer (EBC). Contrast-enhanced mammography (CEDM) is a recent technique that may help define adequate surgery.

METHODS

This retrospective study included consecutive patients referred to a cancer center between November 2016 and July 2017 for biopsy-confirmed invasive EBC management. The primary objective was to evaluate the rate of surgical changes after incorporating the results of the preoperative staging examination, including CEDM.

RESULTS

A total of 231 patients were screened for inclusion, and 132 patients were included, corresponding to 134 lesions. The first surgical plan was modified for 33 patients (25%), which represented 34 lesions. For 8 patients (6%), the surgery was cancelled in preference for neoadjuvant chemotherapy; for 16 patients (12.1%), the primary tumor procedure was enlarged; and for 23 patients (17.4%) the lymph node management was modified. Surgery was changed only due to the CEDM results for 24 patients (18.5%) and consisted of a more invasive procedure due to a more extended, multifocal or multicentric lesion than seen on the standard imaging. Anatomopathological surgery piece findings were well correlated with contrast-enhanced mammography results. Overall, there was no increase in the delay between the planned date of surgery and the effective surgical procedure (median 0 days).

CONCLUSION

CEDM added to preoperative staging helped define better surgical management without increasing delay in the surgical procedure.

ADVANCES IN KNOWLEDGE

CEDM is a reliable technique that should be considered as part of preoperative staging for EBC.

Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography

Simple Summary

The assessment of breast lesions through mammographic images is currently challenging, especially in dense breasts. Contrast-enhanced mammography has been shown to overcome the limitations of standard mammography but it greatly depends on the interpretative skills of the physician. The aim of this study was to evaluate the potentialities of statistical and artificial intelligence algorithms as a tool for helping the radiologists in the interpretation of images. The most remarkable results were achieved in discriminating benign from malignant lesions and in the identification of the presence of the hormone receptor. A tool to support the physician’s decision-making process may be designed starting from simple logistic regression and tree-based algorithms. This type of tool may help the radiologist in assessing the investigated breast and in choosing the appropriate follow-up without resorting to histology.

Abstract

Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.

Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours

Objective

To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).

Methods

This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour.

Results

In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower.

Conclusion

Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status.

Key Points

• A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography.

• The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors.

• Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08538-4.

Single Center Evaluation of Comparative Breast Radiation dose of Contrast Enhanced Digital Mammography (CEDM), Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT)

RATIONALE AND OBJECTIVES

The aim of this retrospective study is to compare the radiation dose received during CEDM, short and long protocol (CEDM SP and CEDM LP), with dose received during DM and DBT on patients with varying breast thickness, age and density.

MATERIALS AND METHODS

Between January 2019 and December 2019, patients having 6214 DM, 3662 DBT and 173 CEDM examinations in our department were analyzed. Protocol total single breast AGD has been evaluated for all clinical imaging protocols, extracting AGD values and exposure data from the dose DICOM Structured Report (SR) information stored in the hospital PACS system. Protocol AGD was calculated as the sum of single projection AGDs carried out in every exam for each clinical protocol. A total amount of 23,383 exams for each breast were analyzed. Protocol AGDs, stratified as a function of patient breast compression thickness, age, and breast density were assessed.

RESULTS

The total protocol AGD median values for each protocol are: 2.8 mGy for DM, 3.2 mGy for DBT, 6.0 mGy for DM+DBT, 4.5 mGy for CEDM SP, 7.4 mGy for CEDM SP_DBT (CEDM SP protocol with DBT), 8.4 mGy for CEDM LP and 11.6 mGy for CEDM LP_DBT (CEDM LP protocol with DBT). CEDM SP AGD median value is 59% higher than DM AGD median value and 40% lesser than DM+DBT AGD median; this last difference was statistically confirmed with a p-value <0.001. AGD value for each standard breast CEDM SP projection results to be below 3-mGy limit. AGD value for each standard breast CEDM SP projection results to be below 3 mGy, as required by international legislation. For dense breasts, the AGD median value is 4.2 mGy, with the first and third quartile of 3.3 mGy and 6.0 mGy respectively; for non-dense breasts, the AGD median value is 4.7 mGy, with first and third quartile of 3.5 mGy and 6.3 mGy respectively. The difference between the two groups was statistically tested and confirmed, with a p-value of 0.039.

CONCLUSION

CEDM SP results in higher radiation exposure compared with conventional DM and DBT but lower than the Combo mode. The dose administered during the CEDM SP is lower in patients with dense breasts regardless of their size. An interesting outcome, considering the ongoing studies on CEDM screening in patients with dense breasts.

Influence of double layer filter on mean glandular dose (MGD) and image quality in low energy image of contrast enhanced spectral mammography (LE-CESM)

INTRODUCTION

The aim of this study was to evaluate mean glandular dose (MGD) and image quality in low energy imaging from contrast-enhanced spectral mammography (CESM) when using double-layer filtration.

METHODOLOGY

A dedicated phantom was used to quantitatively estimate the MGD and image quality. The target slab of the phantom consisted of three iodine coins having a concentration of 1.0 mgI/cm³, 2.0 mgI/cm³, 4.0 mgI/cm³, a 100% adipose equivalent coin and a 100% glandular equivalent coin. The phantom was exposed using a semiautomated function at 28 k, 30 kV and 32 kV. MGD. Contrast to noise ratio (CNR) and figure of merit (FOM) were estimated for Mo/Rh, Mo/Rh + Cu, Mo/Rh + Al and Mo/Rh + Cd combinations using three breast equivalent compositions.

RESULTS

MGD was reduced up to a maximum of 1.03 mGy from 1.17 mGy for 100% adipose tissue. 1.18 mGy from 1.34 mGy for 50% glandular tissue and 1.39 mGy from 1.72 mGy for the 100% glandular phantom when using double-layer filtration. All of the above-mentioned results were obtained for the 50 mm phantom using 32 kV. CNR and FOM values were not significantly reduced with a double-layer filter when compared to a single-layer filter.

CONCLUSION

The present study concluded that Mo/Rh + Cu is the best combination to reduce the MGD significantly when compared to Mo/Rh + Al or Mo/Rh + Cd. Mo/Rh + Cu also achieved optimal image quality when compared to the Mo/Rh single filter combination.

IMPLICATIONS OF PRACTICE

The use of a double-layer filter in low energy imaging of CESM results in a significant reduction in MGD without degrading the quality of the image.

Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions

Background

While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection.

Methods

Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology.

Results

The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74–0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60–0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues.

Conclusion

Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

Leong et al. use a dual-energy mammography technique termed three-compartment breast imaging to study breast composition and detect malignant lesions. Combining compositional information with morphology-based computer-aided diagnosis improves breast cancer detection.

Breast cancers are detected by mammography. This study explored the use of a particular kind of mammography technique to obtain information about the composition of cancerous and non-cancerous breast tissue. This technique provided measures of lipid (fat), water, and protein content in addition to shape characteristics provided from standard mammography. Adding information about the composition of the tissue to its shape characteristics resulted in an increased ability to distinguish invasive cancerous tissue from unaffected surroundings. Invasive breast cancer tissues were also found to exhibit lower lipid, higher protein and higher water content when compared to other non-invasive, non-cancerous breast tissues in which cancer was suspected. Our findings highlight the added value of including the composition of breast tissue when deciding if biopsy of the suspicious tissue is warranted.

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions.

Diagnostic Performance of Adjunctive Imaging Modalities Compared to Mammography Alone in Women with Non-Dense and Dense Breasts: A Systematic Review and Meta-Analysis

PURPOSE

To compare the diagnostic performance of mammography (MG) alone versus MG combined with adjunctive imaging modalities, including handheld ultrasound (HHUS), automated breast ultrasound (ABUS), digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in women with non-dense and dense breasts.

PATIENTS AND METHODS

Medline, Embase, PubMed, CINAHL, Scopus, and the Web of Science databases were searched up to October 2019. Quality assessment was performed using QUADAS-2. RevMan 5.3 was used to conduct a meta-analysis of the studies.

RESULTS

In dense breasts, adding adjunctive modalities significantly increased cancer detection rates (CDRs): HHUS (relative risk [RR] = 1.49; 95% confidence interval [CI], 1.19-1.86; P = .0005); ABUS (RR = 1.44; 95% CI, 1.16-1.78; P = .0008); DBT (RR = 1.38; 95% CI, 1.14-1.67; P = .001); CEM (RR = 1.37; 95% CI, 1.12-1.69; P = .003); and MRI (RR = 2.16; 95% CI, 1.81-2.58; P < .00001). The recall rate was significantly increased by HHUS (RR = 2.03; 95% CI, 1.89-2.17; P < .00001), ABUS (RR = 1.90; 95% CI, 1.81-1.99; P < .00001), and MRI (RR = 2.71; 95% CI, 1.73-4.25; P < .0001), but not by DBT (RR = 1.14; 95% CI, 0.95-1.36; P = .15). In non-dense breasts, HHUS and MRI showed significant increases in CDRs but not DBT: HHUS (RR = 1.14; 95% CI, 1.01-1.29; P = .04); MRI (RR = 1.78; 95% CI, 1.14-2.77; P = .01); and DBT (RR = 1.09; 95% CI, 1.13-1.75; P = .08). The recall rate was also significantly increased by HHUS (RR = 1.43; 95% CI, 1.28-1.59; P < .00001) and MRI (RR = 3.01; 95% CI, 1.68-5.39; P = .0002), whereas DBT showed a non-significant reduction (RR = 0.83; 95% CI, 0.65-1.05; P = .12).

CONCLUSION

Adding adjunctive modalities to MG increases CDRs in women with dense and non-dense breasts. Ultrasound and MRI increase recall rates across all breast densities; however, MRI results in higher values for both CDRs and recall rates.

Advanced and futuristic approaches for breast cancer diagnosis

Background

Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage.

Main body

Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool.

Conclusion

It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.

Background Parenchymal Enhancement at Contrast-Enhanced Spectral Mammography (CESM) as a Breast Cancer Risk Factor

RATIONALE AND OBJECTIVES

To assess the extent of background parenchymal enhancement (BPE) at contrast-enhanced spectral mammography (CESM), association between clinical factors and BPE, and between BPE extent and breast cancer.

MATERIALS AND METHODS

This study included 516 women who underwent CESM imaging for screening and diagnostic purposes between 2012 and 2015 in a single center. BPE at CESM images was retrospectively, independently and blindly graded by six experienced radiologists using the following scale: minimal, mild, moderate, or marked. Agreement between readers was estimated using Kendall's W coefficient of concordance. Associations between clinical factors and BPE, and between BPE and breast cancer were examined using generalized estimating equations. Association between BPE and breast cancer was assessed for the whole study group, and for the screening population separately.

RESULTS

Most women underwent CESM for breast cancer screening (424/516, 82.2%). Mean age was 53 years, the majority had dense breasts (50.4-94%, depending on the reviewer), and minimal to mild BPE (75.8-89.9%). A total of 53/516 women had breast cancer. Overall concordance (W) values between the readers were 0.611 for breast density and 0.789 on BPE. Increased breast density and younger age were positive predictors for increased BPE (odds ratio [OR] 4.07, 95% confidence interval [CI] 2.32-7.14, p < 0.001; OR 2.88, 95% CI 1.87-4.42, p < 0.001, respectively). Breast radiation therapy was a negative predictor for BPE (OR 0.13, 95% CI 0.06-0.31, p < 0.001). Women with increased BPE had increased odds for breast cancer (OR 2.24, 95% CI 1.23-4.09, p = 0.008). This result was consistent when screening cases were analyzed separately (OR 6.27, 95% CI 2.38-16.53, p < 0.001).

CONCLUSION

BPE at CESM was associated with breast density. Women with increased BPE had increased odds for breast cancer, independently of other potential risk factors.

The diagnostic performance of CESM and CE-MRI in evaluating the pathological response to neoadjuvant therapy in breast cancer: a systematic review and meta-analysis

OBJECTIVES

Neoadjuvant chemotherapy (NAC) is an important method for breast cancer treatment. By monitoring its pathological response, the selection of clinical treatment strategies can be guided. In this study, the meta-analysis was used to compare the accuracy of contrast-enhanced MRI (CE-MRI) and contrast-enhanced spectral mammography (CESM) in detecting the pathological response of NAC.

METHODS

Literatures associated to CE-MRI and CESM in the evaluation of pathological response of NAC were searched from PubMed, Cochrane Library, web of science, and EMBASE databases. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the quality of studies. Pooled sensitivity, specificity, and the area under the SROC curve were calculated to evaluate the diagnostic accuracy of CE-MRI and CESM in monitoring the pathological response of NAC.

RESULTS

There were 24 studies involved, 18 of which only underwent CE-MRI examination, three of which only underwent CESM examination, and three of which underwent both CE-MRI and CESM examination. The pooled sensitivity and specificity of CE-MRI were 0.77 (95%CI, 0.67-0.84) and 0.82 (95%CI, 0.73-0.89), respectively. The pooled sensitivity and specificity of CESM were 0.83 (95%CI, 0.66-0.93) and 0.82 (95%CI, 0.68-0.91), respectively. The AUCs of SROC curve for CE-MRI and CESM were 0.86 and 0.89, respectively.

CONCLUSIONS

Compared to CE-MRI, CESM has equal specificity, greater sensitivity and excellent performance, which may have a brighter prospect in evaluating the pathological response of breast cancer to NAC.

ADVANCES IN KNOWLEDGE

CESM showed equal specificity, greater sensitivity, and excellent performance than CE-MRI.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Evaluation of average glandular dose and investigation of the relationship with compressed breast thickness in dual energy contrast enhanced digital mammography and digital breast tomosynthesis

PURPOSE

To quantitatively assess the dose of Dual energy contrast enhanced digital mammography (CEDM) and digital breast tomosynthesis (DBT) and to investigate the relationship between average absorbed glandular dose (AGD), compressed breast thickness (CBT) and compression force (CF).

MATERIALS AND METHODS

All CEDM and DBT examinations were performed in cranio-caudal (CC) and medio-lateral oblique (MLO) view. Exposure parameters of 135 mammographic procedures that using AEC (automatic exposure control) mode were recorded. AGDs were calculated. Kruskal Wallis test was performed.

RESULTS

CBT population ranged from 23 to 94 mm with a thickness median value of 52 mm in CC view and of 57 mm in MLO views. CEDM AGD median value was significatively lower than DBT AGD in each views (p << 0.01). AGD showed a positive correlation and linear regression with CBT for both CEDM and DBT while CF did not show a correlation and linear regression with AGD. The highest values were found for MLO view: R2 of 0.74 for CEDM and R2 of 0.61 for DBT. Kruskal Wallis test shows that there was a difference statistically significant between AGD values of CEDM and DBT in CC view respect to MLO views (p < 0.01).

CONCLUSIONS

Dose values of both techniques meet the recommendations for maximum dose in mammography. The results of the present study indicated that there was significant difference between AGD for CEDM and DBT exposure in different views (AGD in CC views had the lowest value) and that CBT could influence the AGD while CF was not correlated to AGD.

Dual Energy X-ray Methods for the Characterization, Quantification and Imaging of Calcification Minerals and Masses in Breast

Dual energy (DE) technique has been used by numerous studies in order to detect breast cancer in early stages. Although mammography is the gold standard, the dual energy technique offers the advantage of the suppression of the contrast between adipose and glandular tissues and reveals pathogenesis that is not present in conventional mammography. Both dual energy subtraction and dual energy contrast enhanced techniques were used in order to study the potential of dual energy technique to assist in detection or/and visualization of calcification minerals, masses and lesions obscured by overlapping tissue. This article reviews recent developments in this field, regarding: i) simulation studies carried out for the optimizations of the dual energy technique used in order to characterize and quantify calcification minerals or/and visualize suspected findings, and ii) the subsequent experimental verifications, and finally, the adaptation of the dual energy technique in clinical practice.

Contrast-enhanced spectral mammography (CESM) in women presenting with palpable breast findings

OBJECTIVE

Palpable breast abnormalities in women warrant diagnostic evaluation. Contrast-enhanced spectral mammography (CESM) is a novel technique which has demonstrated early promising results in the diagnostic imaging setting. The purpose of this study was to evaluate the role of CESM for imaging of palpable breast abnormalities and compare it to the current routine imaging work-up.

MATERIALS AND METHODS

This retrospective study included women with palpable breast masses who underwent diagnostic CESM and ultrasound between 2012 and 2019. Diagnostic parameters for low-energy images, CESM and targeted ultrasound were calculated and compared. Analysis was performed at the lesion level. Additional incidental findings were reported separately.

RESULTS

Included in this study were 138 women with 147 palpable breast abnormalities, of which 38 were cancers. Standard 2D mammography revealed 36/38 cancers (sensitivity 94.7%). All 38 cancers (100%) were detected at CESM and at targeted ultrasound. Negative predictive value for 2D mammography was 97.8% (91/93), and 100% for both ultrasound (74/74) and for CESM (80/80). None of the palpable masses that were negative at CESM but positive at ultrasound (n = 13) were malignant. Two additional incidental cancers were detected with CESM at the contralateral breast to the palpable lump.

CONCLUSION

CESM could be useful for assessment of palpable breast abnormalities, potentially decreasing the number of unnecessary benign biopsies performed.

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions.

Cascaded systems analysis of anatomic noise in digital mammography and dual-energy digital mammography

In x-ray based imaging of the breast, contrast between fibroglandular (Fg) tissue and adipose (Ad) tissue is a source of anatomic noise. The goal of this work was to validate by simulation and experiment a mathematical framework for modelling the Fg component of anatomic noise in digital mammograpy (DM) and dual-energy (DE) DM. Our mathematical framework unifies and generalizes existing approaches. We compared mathematical predictions directly with empirical measurements of the anatomic noise power spectrum of the CIRS BR3D structured breast phantom using two clinical mammography systems and four beam qualities. Our simulation and experimental results showed agreement with mathematical predictions. As a demonstration of utility, we used our mathematical framework in a theoretical spectral optimization of DM for the task of detecting breast masses. Our theoretical optimization showed that the optimal tube voltage for DM may be higher than that based on predictions that do not account for anatomic noise, in agreement with recent theoretical findings. Additionally, our theoretical optimization predicts that filtering tungsten-anode x-ray spectra with rhodium has little influence on lesion detectability, in contrast with previous findings. The mathematical methods validated in this work can be incorporated easily into cascaded systems analysis of breast imaging systems and will be useful when optimizating novel techniques for x-ray-based imaging of the breast.

BIRADS 4 breast lesions: comparison of contrast-enhanced spectral mammography and contrast-enhanced MRI

Background

Breast cancer is the most common cancer in women worldwide. It is responsible for about 23% of cancer in females in both developed and developing countries [1]. We aimed to assess the accuracy of contrast-enhanced spectral mammography (CESM) versus contrast-enhanced breast MRI in the evaluation of BIRADS 4 breast lesions.

Results

Fifty patients were included in this study; there were 28 malignant cases and 22 benign cases; all cases were proved by histopathological result either by core biopsy or excision biopsy. CESM was found to have less sensitivity (94.1%) than MRI (100%) but CESM has higher specificity (100%) than MRI (95.5%). The accuracy of CESM was 96.4%, while the accuracy of MRI was 98.2% with no statistical significance (P value 0.827).

Conclusion

CESM can be used as a sensitive diagnostic tool in the detection and staging of breast cancer with higher specificity and less sensitivity as compared to contrast enhanced breast MRI.

Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM): a systematic review

We reviewed technical parameters, acquisition protocols and adverse reactions (ARs) for contrast-enhanced spectral mammography (CESM). A systematic search in databases, including MEDLINE/EMBASE, was performed to extract publication year, country of origin, study design; patients; mammography unit/vendor, radiation dose, low-/high-energy tube voltage; contrast molecule, concentration and dose; injection modality, ARs and acquisition delay; order of views; examination time. Of 120 retrieved articles, 84 were included from 22 countries (September 2003-January 2019), totalling 14012 patients. Design was prospective in 44/84 studies (52%); in 70/84 articles (83%), a General Electric unit with factory-set kVp was used. Per-view average glandular dose, reported in 12/84 studies (14%), ranged 0.43-2.65 mGy. Contrast type/concentration was reported in 79/84 studies (94%), with Iohexol 350 mgI/mL mostly used (25/79, 32%), dose and flow rate in 72/84 (86%), with 1.5 mL/kg dose at 3 mL/s in 62/72 studies (86%). Injection was described in 69/84 articles (82%), automated in 59/69 (85%), manual in 10/69 (15%) and flush in 35/84 (42%), with 10-30 mL dose in 19/35 (54%). An examination time < 10 min was reported in 65/84 studies (77%), 120 s acquisition delay in 65/84 (77%) and order of views in 42/84 (50%) studies, beginning with the craniocaudal view of the non-suspected breast in 7/42 (17%). Thirty ARs were reported by 14/84 (17%) studies (26 mild, 3 moderate, 1 severe non-fatal) with a pooled rate of 0.82% (fixed-effect model). Only half of CESM studies were prospective; factory-set kVp, contrast 1.5 mL/kg at 3 mL/s and 120 s acquisition delay were mostly used; only 1 severe AR was reported. CESM protocol standardisation is advisable.

Comparison of contrast-enhanced digital mammography and contrast-enhanced digital breast tomosynthesis for lesion assessment

Contrast-enhanced digital mammography (CEDM) reveals neovasculature of breast lesions in a two-dimensional contrast enhancement map. Contrast-enhanced digital breast tomosynthesis (CEDBT) provides contrast enhancement in three dimensions, which may improve lesion characterization and localization. We aim to compare CEDM and CEDBT for lesion assessment. Women with breast imaging-reporting and data system 4 or 5 suspicious breast lesion(s) were recruited in our study and were imaged with CEDM and CEDBT in succession under one breast compression. Two radiologists assessed CEDM and CEDBT with both images displayed side-by-side and compared (1) contrast enhancement of lesions and (2) lesion margin using a five-point scale ranging from - 2 (CEDM much better) to + 2 (CEDBT much better). Biopsy identified 19 malignant lesions with contrast enhancement. Our results show that CEDBT provides better lesion margins than CEDM with limited reduction in contrast enhancement. CEDBT delivers less radiation dose compared to CEDM + DBT. Synthetic CEDM can be generated from CEDBT data and provides lesion contrast enhancement comparable to CEDM. CEDBT has potential for clinical applications, such as treatment response monitoring and guidance for biopsy.

Can high school students help to improve breast radiologists in detecting missed breast cancer lesions on full-field digital mammography?

Aim: To investigate whether full-field digital mammography (FFDM) and contrast-enhanced mammography (CEM), evaluated by non-experienced high school students, improves detection of missed breast cancer lesions on FFDM, in the same cohort of patients. Methods: Non-experienced first- and second year high school students examined fourteen cases of patients diagnosed with breast cancer. These cases consisted of missed breast cancer lesions on FFDM by a breast radiologist. Sensitivity of assessment of the students on FFDM and CEM was analysed and compared with the initial results of the breast radiologists. Results: A total of 134 high school students participated in this study. Mean age was 12.8 years (range 10-14). Based on FFDM, mean overall sensitivity of the students was 29.2% (18.9 - 39.6%). When recombined CEM images were used, mean overall sensitivity of students improved to 82.6% (74.0 - 91.2%) (p=0.001). Mean overall sensitivity of FFDM exams evaluated by radiologists was 75.7% (64.2 - 87.3%), which was lower when compared to student's evaluations on recombined CEM exams, yet not statistically significant (p=0.098). Conclusions: Contrast-enhanced mammography evaluated by non-experienced high school students might improve detection rate of breast cancer when compared to evaluations of only full-field digital mammography by radiologists.

Susceptibility of iodine concentration map of dual-energy contrast-enhanced digital mammography for quantitative and tumor enhancement assessment

Background Dual-energy (DE) contrast-enhanced digital mammography (DE-CEDM) provides additional information on tumor angiogenesis. Purpose To investigate the susceptibility of reconstructing color-coded iodine concentration maps on the basis of quantitative calibrations of the iodine concentration and contrast-to-noise ratio (CNR) in DE-CEDM applications. Material and Methods A custom-made phantom filled with iodine concentrations in the range of 0.1-10 mg/cm² was used in calibrations. All DE images were acquired using the GE Senographe Essential system. From DE subtraction images, the image contrast and CNR were obtained, and the quantitative relationship between these two metrics and the iodine concentration at each phantom thickness was investigated. The quantitative CNR calibration curves were applied to reconstruct color-coded iodine maps on a pixel-by-pixel basis. Results Both the mean contrast and mean CNR increased linearly with the iodine concentration. The iodine concentration estimated from the iodine map reconstructed from quantitative CNR calibrations was highly consistent with the desired iodine concentration (R²= 0.989), and smaller relative errors (in the range of 3.0-19.5%) were observed with iodine concentrations not less than 1 mg/cm². Conclusion An iodine concentration map could be reconstructed based on the linear relationship between the CNR and iodine concentration. From the color-coded iodine concentration map, the contrast medium enhancement phenomenon could be further estimated quantitatively, and tumor enhancement patterns could be easily observed.

Comparison of screening CEDM and MRI for women at increased risk for breast cancer: A pilot study

OBJECTIVES

Contrast enhanced digital mammography (CEDM) is a new breast imaging technology increasingly used in the diagnostic setting but its utility in the pure screening setting has not been reported. The goal of this pilot study is to prospectively compare screening CEDM to breast MRI in women with an increased risk for breast cancer.

METHODS

In this IRB-approved HIPAA-compliant study, 318 women at increased breast cancer risk were consented (December 2012-May 2015) to undergo CEDM in addition to their scheduled MRI. CEDM was performed within 30days of screening MRI. CEDM was interpreted blinded to MRI. The reference standard was defined as a combination of pathology and 2-year imaging follow-up.

RESULTS

Data from 307/318 patients were evaluable. Three cancers (two invasive cancers, one ductal carcinoma in situ) were detected at first round screening: MRI detected all three and CEDM detected the two invasive cancers. None of the three cancers was seen on the low energy mammograms which are comparable to conventional mammography. At 2year imaging follow up, there were 5 additional screen detected cancers and no palpable cancers. The positive predictive value 3 (PPV₃) for CEDM was 15% (2/13, 95% CI: 2-45%) and 14% for MRI (3/21, 95% CI: 3-36%). The specificity of CEDM and MRI were 94.7% and 94.1% respectively.

CONCLUSIONS

Both CEDM and MRI detected additional cancers not seen on conventional mammography, primarily invasive cancers. Our pilot data suggest that CEDM could be valuable as a supplemental imaging exam for women at increased risk for breast cancer who do not meet the criteria for MRI or for whom access to MRI is limited. Validation in larger multi institutional trials is warranted.

High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography

Contrast-enhanced digital mammography (CEDM) is an alternative to conventional X-ray mammography for imaging dense breasts. However, conventional approaches to CEDM require a double exposure of the patient, implying higher dose, and risk of incorrect image registration due to motion artifacts. A novel approach is presented, based on hyperspectral imaging, where a detector combining positional and high-resolution spectral information (in this case based on Cadmium Telluride) is used. This allows simultaneous acquisition of the two images required for CEDM. The approach was tested on a custom breast-equivalent phantom containing iodinated contrast agent (Niopam 150®). Two algorithms were used to obtain images of the contrast agent distribution: K-edge subtraction (KES), providing images of the distribution of the contrast agent with the background structures removed, and a dual-energy (DE) algorithm, providing an iodine-equivalent image and a water-equivalent image. The high energy resolution of the detector allowed the selection of two close-by energies, maximising the signal in KES images, and enhancing the visibility of details with the low surface concentration of contrast agent. DE performed consistently better than KES in terms of contrast-to-noise ratio of the details; moreover, it allowed a correct reconstruction of the surface concentration of the contrast agent in the iodine image. Comparison with CEDM with a conventional detector proved the superior performance of hyperspectral CEDM in terms of the image quality/dose tradeoff.

Accuracy of contrast-enhanced spectral mammography for estimating residual tumor size after neoadjuvant chemotherapy in patients with breast cancer: a feasibility study

Objective

To assess the feasibility of contrast-enhanced spectral mammography (CESM) of the breast for assessing the size of residual tumors after neoadjuvant chemotherapy (NAC).

Materials and methods

In breast cancer patients who underwent NAC between 2011 and 2013, we evaluated residual tumor measurements obtained with CESM and full-field digital mammography (FFDM). We determined the concordance between the methods, as well as their level of agreement with the pathology. Three radiologists analyzed eight CESM and FFDM measurements separately, considering the size of the residual tumor at its largest diameter and correlating it with that determined in the pathological analysis. Interobserver agreement was also evaluated.

Results

The sensitivity, specificity, positive predictive value, and negative predictive value were higher for CESM than for FFDM (83.33%, 100%, 100%, and 66% vs. 50%, 50%, 50%, and 25%, respectively). The CESM measurements showed a strong, consistent correlation with the pathological findings (correlation coefficient = 0.76-0.92; intraclass correlation coefficient = 0.692-0.886). The correlation between the FFDM measurements and the pathological findings was not statistically significant, with questionable consistency (intraclass correlation coefficient = 0.488-0.598). Agreement with the pathological findings was narrower for CESM measurements than for FFDM measurements. Interobserver agreement was higher for CESM than for FFDM (0.94 vs. 0.88).

Conclusion

CESM is a feasible means of evaluating residual tumor size after NAC, showing a good correlation and good agreement with pathological findings. For CESM measurements, the interobserver agreement was excellent.

Quantitative contrast-enhanced spectral mammography based on photon-counting detectors: A feasibility study

PURPOSE

To investigate the feasibility of accurate quantification of iodine mass thickness in contrast-enhanced spectral mammography.

MATERIALS AND METHODS

A computer simulation model was developed to evaluate the performance of a photon-counting spectral mammography system in the application of contrast-enhanced spectral mammography. A figure-of-merit (FOM), which was defined as the decomposed iodine signal-to-noise ratio (SNR) with respect to the square root of the mean glandular dose (MGD), was chosen to optimize the imaging parameters, in terms of beam energy, splitting energy, and prefiltrations for breasts of various thicknesses and densities. Experimental phantom studies were also performed using a beam energy of 40 kVp and a splitting energy of 34 keV with 3 mm Al prefiltration. A two-step calibration method was investigated to quantify the iodine mass thickness, and was validated using phantoms composed of a mixture of glandular and adipose materials, for various breast thicknesses and densities. Finally, the traditional dual-energy log-weighted subtraction method was also studied as a comparison. The measured iodine signal from both methods was compared to the known value to characterize the quantification accuracy and precision.

RESULTS

The optimal imaging parameters, which lead to the highest FOM, were found at a beam energy between 42 and 46 kVp with a splitting energy at 34 keV. The optimal tube voltage decreased as the breast thickness or the Al prefiltration increased. The proposed quantification method was able to measure iodine mass thickness on phantoms of various thicknesses and densities with high accuracy. The root-mean-square (RMS) error for cm-scale lesion phantoms was estimated to be 0.20 mg/cm² . The precision of the technique, characterized by the standard deviation of the measurements, was estimated to be 0.18 mg/cm² . The traditional weighted subtraction method also predicted a linear correlation between the measured signal and the known iodine mass thickness. However, the correlation slope and offset values were strongly dependent on the total breast thickness and density.

CONCLUSION

The results of this study suggest that iodine mass thickness for cm-scale lesions can be accurately quantified with contrast-enhanced spectral mammography. The quantitative information can potentially improve the differential power for malignancy.

False-negative contrast-enhanced spectral mammography: use of more than one imaging modality and application of the triple test avoids misdiagnosis

A 50-year-old woman presented with chest tenderness. On examination, both breasts were lumpy. Bilateral mammography showed heterogeneously dense parenchyma, with possible stromal distortion laterally on the right at the 0900 position. On ultrasound (US), a corresponding 13×9×10 mm irregular hypoechoic mass with internal vascularity was noted and both breasts had a complex heterogeneous fibroglandular background pattern. US-guided core biopsy with marker clip insertion was performed with the diagnosis of a grade 2 invasive ductal carcinoma (IDC). In view of the parenchymal pattern on mammography and US, contrast-enhanced spectral mammography (CESM) was performed for local staging. Mild background enhancement was noted, but there was no enhancement at the lesion site. The patient elected to have bilateral mastectomies and sentinel node biopsies. Final histopathology showed a node negative 11 mm grade 2 oestrogen and progesterone receptor positive, IDC.