Conventional Full-field Digital Mammography Research Articles

Comparison of Full-Field Digital Mammography with Synthesized Mammography from Tomosynthesis in a Diagnostic population: Prospective Study

Abstract Background Synthesized mammography (SM) refers to two-dimensional (2D) images derived from the digital breast tomosynthesis (DBT) data. It can reduce the radiation dose and scan duration when compared with conventional full-field digital mammography (FFDM) plus tomosynthesis. Purpose To compare the diagnostic performance of 2D FFDM with synthetic mammograms obtained from DBT in a diagnostic population. Materials and Methods A total of 1,468 mammograms with both FFDM and SM + DBT images were obtained and analyzed over 2 years, after obtaining approval from the institute ethics committee. The images were reported and compared as per the 2013 American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon in terms of breast density, morphological features of mass, calcifications, and presence of asymmetry or architectural distortion followed by the BI-RADS category. The agreement between the two modalities was studied using the Kappa value, and the radiation exposure dose was recorded in both groups. Results FFDM and SM + DBT showed strong agreement for breast density, mass characteristics, and detection of calcifications (kappa > 0.8). Downstaging of breast density and mass density were seen by SM + DBT without any statistically significant difference. The nipple–areola complex visualization was poor in SM (50.34 vs. 76.29% in FFDM), and there were SM-specific artifacts mainly related to the reconstruction algorithm. The radiation dose was higher with SM. Conclusion FFDM has comparable performance to SM + DBT in diagnostic setup. The latter may be particularly helpful in patients with dense breasts.

OD123 - Small-size details detection performance of digital breast tomosynthesis, synthetic 2D and conventional full-field digital mammography images for different mammography systems: a multicenter study

OD123 - Small-size details detection performance of digital breast tomosynthesis, synthetic 2D and conventional full-field digital mammography images for different mammography systems: a multicenter study

Special issue on future hybrid artificial intelligence and machine learning for smart expert systems

Artificial Intelligence (AI) has grown extensively in recent times and already encroaches into our lives, for example, with smart cars and smart homes. AI current and future trends in areas such as smart cars, face recognition, robotic surgeries, or automatic disease classification, focus on hybridization to improve the performance of smart expert systems. Despite the huge growth of various AI and machine learning techniques, such as decision trees, data mining, or deep convergence learning, there are still many challenges and threats which limit the performance of these smart expert systems. This special issue focuses on new directions in hybrid AI and machine learning methodologies to address current research problems. It focuses on bringing together leading-edge articles on hybrid AI techniques for smart expert systems for various applications. From about 25 articles submitted, 4 papers were selected based on the review process. Each paper was reviewed by at least two independent reviewers and undertook at least one round of review. Their contributions are summarized below. Raghavendra et al. (2019) has proposed a research work on accurate and early detection of breast cancer using effective imaging modalities with medical image analysis. The research results have shown that two-dimensional synthesized mammogram imaging and conventional full-field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold cross validation. The obtained results can be used as a distinct system in rural hospitals. Gupta et al. (2020) has done their research studies on to detect infected leaves using machine learning techniques. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private data set of 236 images. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for the classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. Hammad et al. (2020) has found a new authentication approach based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals. This work shows two novel deep neural network (DNN) models (convolutional neural network (CNN) and ResNet-Attention) using ECG signals for human authentication and the proposed CNN algorithm achieved an accuracy of 98.59% and 99.72% using PTB and CYBHi, respectively. The results obtained by the developed model proved that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. Teles et al. (2020) has devised a decision support system on credit operations which can provide different estimations of expected recovery based on the same data sets. This research work classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. Results reveal that while linear regression can be used for the prediction of a continuous variable in the credit process using collateral, it is not suitable for categorical data. Out of linear and logistic regressions, linear regression is more applicable in determining groups of variables that can significantly predict an outcome. We would like to take this opportunity to thank Dr. Jon G. Hall, Editor-in-Chief of Expert Systems, The Journal of Knowledge Engineering, for the privilege to edit this special issue. Gustavo Ramírez González is a professor at department of telematics engineering in Universidad del Cauca, Colombia. His research interests include image processing, secure communication, and machine learning. He has published several research papers in reputed journals and served as a Guest Editor for several Special Issues at many journals.

Added value of contrast-enhanced spectral mammography in symptomatic patients with dense breasts

BackgroundBreast cancer is the most common malignancy in females around the world representing 25.1% of all cancers.The high prevalence and need for early treatment of breast malignancy highlight the importance of early and accurate diagnosis. In order to achieve this target, it is necessary to select the most appropriate modality for investigation.Early detection of breast cancer by conventional mammography tends to reduce mortality; however, it has a low sensitivity and specificity in young females with dense breasts owing to reduced contrast between a possible tumor and the surrounding breast tissue with superimposition of the glandular tissue obscuring underlying lesions.Our study included 25 patients with dense breasts presented with different breast symptoms, yet the breast lump was the most common complaint. The aim of our study is to evaluate the supplementary value of contrast-enhanced spectral mammography in the assessment of symptomatic patients with dense breasts.ResultsIn our study, the enrolled subjects underwent both contrast-enhanced spectral mammography (CESM) and conventional full-field digital mammography (FFDM). CESM was shown to be better than FFDM in terms of sensitivity, positive predictive value, negative predictive value, and accuracy, measuring 100%, 77.8%, 100%, and 84%, compared to 56%, 75%, 46%, and 60%, respectively, yet both modalities showed low specificity, measuring 63.6% and 66.6% for CESM and FFDM, respectively.The added value of CESM was assessed in terms of ability to detect and correctly characterize the lesions in correlation to histopathological results where CESM could detect 88% of the lesions included in our study and correctly characterized 84% of the lesions; on the other side, FFDM detected only 20% of the lesions and correctly characterized 60% of the lesions. CESM changed the treatment plan to a more extensive surgery +/− neoadjuvant chemotherapy in 57% out of fourteen cases diagnosed with breast cancer emphasizing the role of CESM in assessing the extent of the disease, multicentricity, and multifocality and consequently tailoring the most appropriate treatment plan suitable for each patient.ConclusionContrast-enhanced spectral mammography is superior to full-field digital mammography in patients with dense breasts with a significant supplementary value in detection, characterization of lesions, and tailoring the appropriate treatment plan.

Quantitative analysis of radiation dosage and image quality between digital breast tomosynthesis (DBT) with two-dimensional synthetic mammography and full-field digital mammography (FFDM)

PurposeCurrently in diagnostic setting for breast cancer, FFDM and DBT are performed conjunctively. However, performing two imaging modalities may increase radiation exposure by double. Two-dimensional reconstructed images created from DBT with 2DSM, has a potential to replace conventional FFDM in concerning both radiation dosage and image quality. With increasing concerns for individual radiation exposure, studies analyzing radiation dosage in breast imaging modalities are needed. This study compared radiation dosage and image quality between DBT + 2DSM versus FFDM. Methods and materials374 patients (mean age 52 years) who underwent both DBT and FFDM were retrospectively reviewed. Radiation dosage data were obtained by radiation dosage scoring and monitoring program Radimetrics (Bayer HealthCare, Whippany, NJ). Entrance dose and mean glandular doses in each breast were obtained for both modalities. To compare image quality of DBT + 2DSM and FFDM, a 5-point scoring system for lesion clarity was assessed. The parameters of radiation dosage (entrance dose, mean glandular dose) and image quality (lesion clarity scoring) were compared. ResultsFor entrance dose, DBT had lower mean dosage (14.8 mGy) compared with FFDM (21.8 mGy, p-value < 0.0001). Mean glandular doses for both breasts were lower in DBT (Left 1.74, Right 2.1) compared with FFDM (Left 2.85, Right 2.74, p-value < 0.0001). Lesion clarity score was higher in DBT with 2DSM (mean score 4.03) compared with FFDM (3.82, p-value < 0.0001). ConclusionDBT showed lower radiation entrance dose and mean glandular doses to both breasts compared with FFDM. DBT + 2DSM had better image quality than FFDM, suggesting that DBT with 2DSM has potential as an alternative to FFDM.

Diagnostic Performance of Digital Breast Tomosynthesis for Breast Suspicious Calcifications From Various Populations: A Comparison With Full-field Digital Mammography

The diagnostic performance difference between digital breast tomosynthesis (DBT) and conventional full-field digital mammography (FFDM) for breast suspicious calcifications from various populations is unclear. The objective of this study is to determine whether DBT exhibits the diagnostic advantage for breast suspicious calcifications from various populations compared with FFDM. Three hundred and five patients were enrolled (of which seven patients with bilateral lesions) and 312 breasts images were retrospectively analyzed by three radiologists independently. The postoperative pathology of breast calcifications was the gold standard. Breast cancer was diagnosed utilizing DBT and FFDM with sensitivities of 92.9% and 88.8%, specificities of 87.9% and 75.2%, positive predictive values of 77.8% and 62.1%, negative predictive values of 96.4% and 93.6%, respectively. DBT exhibited significantly higher diagnostic accuracy for benign calcifications compared with FFDM (87.9% vs 75.2%), and no advantage in the diagnosis of malignant calcifications. DBT diagnostic accuracy was notably higher than FFDM in premenopausal (88.4% vs 78.8%), postmenopausal (90.2% vs 77.2%), and dense breast cases (89.4% vs 81.9%). There was no significant difference in non-dense breast cases. In our study, DBT exhibited a superior advantage in dense breasts and benign calcifications cases compared to FFDM, while no advantage was observed in non-dense breasts or malignant calcifications cases. Thus, in the breast cancer screening for young women with dense breasts, DBT may be recommended for accurate diagnosis. Our findings may assist the clinicians in applying the optimal techniques for different patients and provide a theoretical basis for the update of breast cancer screening guideline.

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography

With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammography (FFDM). In this study, we retrospectively collected FFDM and DBT images of 78 biopsy-proven lesions from 76 patients. A region of interest was selected for each lesion on FFDM, synthesized 2D, and DBT key slice images. Features were extracted from each lesion using a pretrained convolutional neural network (CNN) and served as input to a support vector machine classifier trained in the task of predicting likelihood of malignancy. From receiver operating characteristic (ROC) analysis of all 78 lesions, the synthesized 2D image performed best in both the cradiocaudal view (area under the ROC curve [AUC] = 0.81, SE = 0.05) and mediolateral oblique view (AUC = 0.88, SE = 0.04) in the task of lesion characterization. When cradiocaudal and mediolateral oblique data of each lesion were merged through soft voting, DBT key slice image performed best (AUC = 0.89, SE = 0.04). When only masses and architectural distortions (ARDs) were considered, DBT performed significantly better than FFDM (p = 0.024). DBT performed significantly better than FFDM in the merged view classification of mass and ARD lesions. The increased performance suggests that the information extracted by the CNN from DBT images may be more relevant to lesion malignancy status than the information extracted from FFDM images. Therefore, this study provides supporting evidence for the efficacy of computer-aided diagnosis on DBT in the evaluation of mass and ARD lesions.

Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography

To evaluate the diagnostic performance of computer-aided detection (CAD)-enhanced synthetic mammograms in comparison with standard synthetic mammograms and full-field digital mammography (FFDM). A CAD-enhanced synthetic mammogram, a standard synthetic mammogram, and FFDM were available in 68 breast-screening cases recalled for soft-tissue abnormalities (masses, parenchymal deformities, and asymmetric densities). Two radiologists, blinded to image type and final assessment outcome, retrospectively read oblique and craniocaudal projections for each type of mammogram. The resulting 204 pairs of 2D images were presented in random order and scored on a five-point scale (1, normal to 5, malignant) without access to the Digital breast tomosynthesis (DBT) slices. Receiver operating characteristic (ROC) curve analysis was performed. There were 34 biopsy-proven malignancies and 34 normal/benign cases. Diagnostic accuracy was significantly improved for the CAD-enhanced synthetic mammogram compared to the standard synthetic mammogram (area under the ROC curve [AUC]=0.846 and AUC=0.683 respectively, p=0.004) and compared to the conventional 2D FFDM (AUC=0.724, p=0.027). The CAD-enhanced synthetic mammogram had the highest diagnostic accuracy for all soft-tissue abnormalities, and for malignant lesions sensitivity was not affected by tumour size. For all 68 cases, there was an average of 3.2 areas enhanced per image. For the 34 cancer cases, 97.4% of lesions were correctly enhanced, with 2.1 false areas enhanced per image. CAD enhancement significantly improves performance of synthetic 2D mammograms and also exhibits improved diagnostic accuracy compared to conventional 2D FFDM.

Diagnostic performance of digital breast tomosynthesis and full-field digital mammography with new reconstruction and new processing for dose reduction.

The main barrier to adoption of digital breast tomosynthesis (DBT) plus full-field digital mammography (FFDM) is radiation exposure dose. The purpose of this study was to evaluate the diagnostic performance of DBT plus FFDM, both with newly developed technology (nd), at a dose comparable to that of the conventional FFDM alone. Nine hundred and thirteen participants were recruited from May 2014 to January 2016 consecutively. For each subject, the exposure setting for DBT(nd)+FFDM(nd) was also used for the conventional FFDM alone. Retrospective reader studies were performed: DBT(nd)+FFDM(nd) (142 cases, including 42 cancer cases) and conventional FFDM (258 cases, including 87 cancer cases). Eight radiologists provided Japanese categorizations and probability of malignancy independently. Diagnostic performance was assessed by comparing sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Two-sided P values were calculated. DBT(nd)+FFDM(nd) showed sensitivity and AUC significantly increased over the conventional FFDM (85.4 vs. 80.3%, P=0.015 and 90.9 vs. 88.3%, P=0.049) and specificity did not significantly increase (89.6 vs. 88.4%, P=0.52). The mean glandular dose (MGD) difference of DBT(nd)+FFDM(nd) and conventional FFDM was not significant (difference -0.11mGy, P=0.08). In this study population, DBT plus FFDM, both with newly developed technology, provided diagnostic performance improved over the conventional FFDM alone, even at comparable MGD.

Experimental characterization of a direct conversion amorphous selenium detector with thicker conversion layer for dual-energy contrast-enhanced breast imaging.

Dual-energy contrast-enhanced imaging is being investigated as a tool to identify and localize angiogenesis in the breast, a possible indicator of malignant tumors. This imaging technique requires that x-ray images are acquired at energies above the k-shell binding energy of an appropriate radiocontrast agent. Iodinated contrast agents are commonly used for vascular imaging, and require x-ray energies greater than 33 keV. Conventional direct conversion amorphous selenium (a-Se) flat-panel imagers for digital mammography show suboptimal absorption efficiencies at these higher energies. We use spatial-frequency domain image quality metrics to evaluate the performance of a prototype direct conversion flat-panel imager with a thicker a-Se layer, specifically fabricated for dual-energy contrast-enhanced breast imaging. Imaging performance was evaluated in a prototype digital breast tomosynthesis (DBT) system. The spatial resolution, noise characteristics, detective quantum efficiency, and temporal performance of the detector were evaluated for dual-energy imaging for both conventional full-field digital mammography (FFDM) and DBT. The zero-frequency detective quantum efficiency of the prototype detector is improved by approximately 20% over the conventional detector for higher energy beams required for imaging with iodinated contrast agents. The effect of oblique entry of x-rays on spatial resolution does increase with increasing photoconductor thickness, specifically for the most oblique views of a DBT scan. Degradation of spatial resolution due to focal spot motion was also observed. Temporal performance was found to be comparable to conventional mammographic detectors. Increasing the a-Se thickness in direct conversion flat-panel imagers results in better performance for dual-energy contrast-enhanced breast imaging. The reduction in spatial resolution due to oblique entry of x-rays is appreciable in the most extreme clinically relevant cases, but may not profoundly affect reconstructed images due to the algorithms and filters employed. Degradation to projection domain spatial resolution is thus outweighed by the improvement in detective quantum efficiency for high-energy x-rays.

Evaluation of patient dose saving in grid-less x-ray mammography acquisition compared with full field digital mammography (FFDMG) acquisition

Purpose. To investigate the patient radiation dose saving of grid-less x-ray mammography acquisitions compared with conventional full-field digital mammography (FFDMG). Methods and materials. The Siemens Inspiration MAMMOMAT PRIME system with software based scatter correction was used to investigate the dose saving in grid-less acquisition compared with conventional full-field digital mammography (FFDMG) acquisitions. A Piranha 657 solid-state detector was used to measure the entrance exposure. The entrance exposure was directly measured on different PMMA thicknesses of 20–70 mm in steps of 10 mm. The PMMA block thicknesses were then converted to an equivalent compressed breast tissue thicknesses. The average glandular dose (AGD) is calculated. Results. Dose reduction in both the directly measured entrance exposure and the calculated AGD is between 13% and 32% in the grid-less mammography acquisition. The contrast to noise ratio calculated in the grid-less and the FFDMG acquisition also was the same. Conclusion. It was possible to save patient dose (13%–32%) in grid-less mammography acquisition without affecting the image quality.

TU-F-18C-02: Increasing Amorphous Selenium Thickness in Direct Conversion Flat-Panel Imagers for Contrast-Enhanced Dual-Energy Breast Imaging

Purpose: Contrast-enhanced (CE) breast imaging using iodinated contrast agents requires imaging with x-ray spectra at energies greater than those used in mammography. Optimizing amorphous selenium (a-Se) flat panel imagers (FPI) for this higher energy range may increase lesion conspicuity. Methods: We compare imaging performance of a conventional FPI with 200 μm a-Se conversion layer to a prototype FPI with 300 μm a-Se layer. Both detectors are evaluated in a Siemens MAMMOMAT Inspiration prototype digital breast tomosynthesis (DBT) system using low-energy (W/Rh 28 kVp) and high-energy (W/Cu 49 kVp) x-ray spectra. Detectability of iodinated lesions in dual-energy images is evaluated using an iodine contrast phantom. Effects of beam obliquity are investigated in projection and reconstructed images using different reconstruction methods. The ideal observer signal-to-noise ratio is used as a figure-of-merit to predict the optimal a-Se thickness for CE lesion detectability without compromising conventional full-field digital mammography (FFDM) and DBT performance. Results: Increasing a-Se thickness from 200 μm to 300 μm preserves imaging performance at typical mammographic energies (e.g. W/Rh 28 kVp), and improves the detective quantum efficiency (DQE) for high energy (W/Cu 49 kVp) by 30%. While the more penetrating high-energy x-ray photons increase geometric blur due to beam obliquity in the FPI with thicker a-Se layer, the effect on lesion detectability in FBP reconstructions is negligible due to the reconstruction filters employed. Ideal observer SNR for CE objects shows improvements in in-plane detectability with increasing a-Se thicknesses, though small lesion detectability begins to degrade in oblique projections for a-Se thickness above 500 μm. Conclusion: Increasing a-Se thickness in direct conversion FPI from 200 μm to 300 μm improves lesion detectability in CE breast imaging with virtually no cost to conventional FFDM and DBT. This work was partially supported by a research grant from Siemens Healthcare.

Relationship between Detector Size and the Need for Extra Images and their Effect on Radiation Exposure in Digital Mammography Screening

To determine the number of extra images (EI) that are necessary for imaging large breasts when using a detector smaller than 24 cm × 30 cm and to calculate the additional average glandular dose (AGD) for these images. The screening mammograms taken between 2007 and 2011 were assessed for a photon counting full-field digital mammography (PCM) system (detector size: 24 cm × 26 cm) and a computed radiography (CR) system (24 cm × 30 cm). The number of EI was recorded and the AGD calculated. This AGD was compared with the mean AGD of 47 conventional full-field digital mammography (FFDM) systems. A total of 62,466 examinations were analyzed. EI had to be taken in 0.6 % (199/32,766) of all PCM examinations and 0.3 % (90/29 700) of all CR examinations. This corresponded to a total of 327 and 191 EI for the PCM and CR systems, respectively. More than one quarter of the examinations with EI were necessary because the breast was not properly positioned in the original image (PCM 31 %, CR 29 %). The mean AGD per EI was 0.7 ± 0.1 mGy for the PCM and 2.6 ± 1.2 mGy for the CR system. The mean AGD for all breast thicknesses for FFDM was 1.4 ± 0.3 mGy. In general, large breasts cannot be imaged with just one image per view. The number of examinations where EI are needed is doubled with the 24 cm × 26 cm detector of the PCM system. However, the absolute number is small. The total dose, as the sum of the original and the EI, is equal to the mean AGD of a single image of the FFDM systems and lower than the dose of a single image with the CR system.

Preliminary Clinical Experience with Digital Breast Tomosynthesis in the Visualization of Breast Microcalcifications

Objectives:To compare the visualization and image quality of microcalcifications imaged with digital breast tomosynthesis (DBT) versus conventional digital mammography.Materials and Methods:Patients with microcalcifications detected on full field digital mammography (FFDM) recommended for needle core biopsy were enrolled in the study after obtaining patient's consent and institutional review board approval (n = 177 patients, 179 lesions). All had a bilateral combination DBT exam, after undergoing routine digital mammography, prior to biopsy. The study radiologist reviewed the FFDM and DBT images in a non-blinded comparison and assessed the visibility of the microcalcifications with both methods, including image quality and clarity with which the calcifications were seen. Data recorded included patient demographics, lesion size on FFDM, DBT, and surgical excision (when applicable), biopsy, and surgical pathology, if any.Results:Average lesion size on DBT was 1.5 cm; average lesion size on FFDM was 1.4 cm. The image quality of DBT was assessed as equivalent or superior in 92.2% of cases. In 7.8% of the cases, the FFDM image quality was assessed as equivalent or superior.Conclusion:In our review, DBT image quality appears to be comparable to or better than conventional FFDM in terms of demonstrating microcalcifications, as shown in 92.2% of cases.

Full Field Digital Mammography (FFDM) versus CMOS Technology, Specimen Radiography System (SRS) and Tomosynthesis (DBT) - Which System Can Optimise Surgical Therapy?

Aim: This prospective clinical study aimed to evaluate whether it would be possible to reduce the rate of re-excisions using CMOS technology, a specimen radiography system (SRS) or digital breast tomosynthesis (DBT) compared to a conventional full field digital mammography (FFDM) system. Material and Method: Between 12/2012 and 2/2013 50 patients were diagnosed with invasive breast cancer (BI-RADS™5). After histological verification, all patients underwent breast-conserving therapy with intraoperative imaging using 4 different systems and differing magnifications: 1. Inspiration™ (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm pixel pitch, 8 lp/mm; 2. BioVision™ (Bioptics, Tucson, AZ, USA), CMOS technology, photodiode array, flat panel, tungsten source, focus 0.05, resolution 50 µm pixel pitch, 12 lp/mm; 3. the Trident™ specimen radiography system (SRS) (Hologic, Bedford, MA, USA), amorphous selenium, tungsten source, focus 0.05, resolution 70 µm pixel pitch, 7.1 lp/mm; 4. tomosynthesis (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm pixel pitch, 8 lp/mm, angular range 50 degrees, 25 projections, scan time > 20 s, geometry: uniform scanning, reconstruction: filtered back projection. The 600 radiographs were prospectively shown to 3 radiologists. Results: Of the 50 patients with histologically proven breast cancer (BI-RADS™6), 39 patients required no further surgical therapy (re-excision) after breast-conserving surgery. A retrospective analysis (n = 11) showed a significant (p < 0.05) increase of sensitivity with the BioVision™, the Trident™ and tomosynthesis compared to the Inspiration™ at a magnification of 1.0 : 2.0 or 1.0 : 1.0 (tomosynthesis) (2.6, 3.3 or 3.6 %), i.e. re-excision would not have been necessary in 2, 3 or 4 patients, respectively, compared to findings obtained with a standard magnification of 1.0 : 1.0. Conclusion: The sensitivity of the BioVision™, the Trident™ and tomosynthesis was significantly (p < 0.05) higher and the rate of re-excisions was reduced compared to FFDM using a conventional detector at a magnification of 2.0 but without zooming.

Tomosynthesis in X-ray: proven additional value?

Digital breast tomosynthesis (DBT) is a new promising modality for breast imaging. Breast tomosynthesis is based on a full-field digital mammography (FFDM) platforms, and the DBT images are obtained in the same standard projections (cranio-caudal and mediolateral oblique views) as for conventional mammography [1,2]. The tomographic images or “slices” are usually reconstructed at 1mm intervals and presented in the default setting as thin “slices” on the dedicated work-station. The images may, however, also be displayed as a volume consisting of several slices (“slabs”). The idea of using DBT is that the shape and margins of breast masses may be more clearly delineated by reducing or eliminating overlapping tissue, and the ability to distinguish superimposed tissue from breast lesions would be improved. Thus, it is suggested that this new technology may improve the specificity as compared with conventional mammography. Furthermore, the elimination of superimposed breast tissue should theoretically improve the detection of lesions otherwise hidden by dense breast parenchyma. The ability of DBT to reduce or eliminate the interpretation problems caused by overlapping breast tissue may be particularly valuable in women with dense breasts. Furthermore, it is suggested that digital breast tomosynthesis may improve the detection of architectural distortion especially in women with heterogeneous dense breasts. Theoretically, the very thin spiculations seen in architectural distortion would be expected to be more easily identified on 1mm thin slices as compared with a conventional projection mammogram. There has been a lot of concern regarding the perception (detection) and analysis (characterization) of calcifications on digital breast tomosynthesis. Theoretically, micro-calcifications may represent a challenge for DBT due to the so-called “thin-sliceeffect” since only few or microcalcifications might be seen on each slice and not all calcifications of cluster as is the case on conventional FFDM projection images. Viewing the microcalcifications in a “slab”-mode may resolve some of these problems, and a larger study comparing sharpness, contrast, and diagnostic quality of microcalcifications concluded that calcifications can be demonstrated with equal or even greater clarity on DBT than on mammography [3]. Analysis of microcalcification clusters on DBT may have a learning curve effect. There have been only few publications on DBT in a clinical setting so far, and consequently the future role of tomosynthesis, whether its implementation will be in mammography screening or in clinical (i.e., “diagnostic”) imaging, or in both settings, is still not clear.

Full Field Digital Mammography (FFDM) versus CMOS Technology versus Tomosynthesis (DBT) - Which System Increases the Quality of Intraoperative Imaging?

Aim: The aim of this prospective clinical study was to assess whether it would be possible to reduce the rate of re-excisions and improve the quality using CMOS technology or digital breast tomosynthesis (DBT) compared to a conventional FFDM system. Material and Methods: An invasive breast cancer (BI-RADS 5) was diagnosed in 200 patients in the period from 5/2011 to 1/2012. After histological verification, a breast-conserving therapy was performed with intraoperative imaging. Three different imaging systems were used: 1) Inspiration™ (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm pixel pitch, 8 l/mm as the standard; 2) BioVision™ (Bioptics, Tucson, USA), flat panel photodiode array, tungsten source, focus 0.05, resolution 50 µm pixel pitch, 12 l/mm; 3) Tomosynthesis (Siemens, Erlangen, Germany), amorphous selenium, tungsten source, focus 0.1 mm, resolution 85 µm pixel pitch, 8 l/mm, range: 50°, 25 projections, scan time > 20 s, geometry: uniform scanning, reconstruction: filtered back projection. The 600 radiograms were prospectively shown to 3 radiologists. Results: Out of a total of 200 patients with histologically confirmed breast cancer (BI-RADS 6) 156 patients required no further operative therapy (re-excision) after breast-conserving therapy. A retrospective analysis (n = 44) showed an increase in sensitivity with tomosynthesis compared to the BioVision™ (CMOS technology) and the Inspiration™ at a magnification of 1.0 : 1.0 of 8 % (p < 0.05), i.e. re-excision would not have been necessary in 16 patients with tomosynthesis. Conclusions: The sensitivity of tomosynthesis for intraoperative radiography is significantly (p < 0.05) higher compared to both CMOS technology and an FFDM system with a conventional detector. Additional studies using higher magnification, e.g. 2.0 : 1.0, but no zooming will be necessary to evaluate the method further.

Comparison of Radiologist Performance with Photon-Counting Full-Field Digital Mammography to Conventional Full-Field Digital Mammography

The purpose of this study was to assess the performance of a MicroDose photon-counting full-field digital mammography (PCM) system in comparison to full-field digital mammography (FFDM) for area under the receiver-operating characteristic (ROC) curve (AUC), sensitivity, specificity, and feature analysis of standard-view mammography for women presenting for screening mammography, diagnostic mammography, or breast biopsy. A total of 133 women were enrolled in this study at two European medical centers, with 67 women who had a pre-existing 10-36 months FFDM enrolled prospectively into the study and 66 women who underwent breast biopsy and had screening PCM and diagnostic FFDM, including standard craniocaudal and mediolateral oblique views of the breast with the lesion, enrolled retrospectively. The case mix consisted of 49 cancers, 17 biopsy-benign cases, and 67 normal cases. Sixteen radiologists participated in the reader study and interpreted all 133 cases in both conditions, separated by washout period of ≥4 weeks. ROC curve and free-response ROC curve analyses were performed for noninferiority of PCM compared to FFDM using a noninferiority margin Δ value of 0.10. Feature analysis of the 66 cases with lesions was conducted with all 16 readers at the conclusion of the blinded reads. Mean glandular dose was recorded for all cases. The AUC for PCM was 0.947 (95% confidence interval [CI], 0.920-0.974) and for FFDM was 0.931 (95% CI, 0.898-0.964). Sensitivity per case for PCM was 0.936 (95% CI, 0.897-0.976) and for FFDM was 0.908 (95% CI, 0.856-0.960). Specificity per case for PCM was 0.764 (95% CI, 0.688-0.841) and for FFDM was 0.749 (95% CI, 0.668-0.830). Free-response ROC curve figures of merit were 0.920 (95% CI, 0.881-0.959) and 0.903 (95% CI, 0.858-0.948) for PCM and FFDM, respectively. Sensitivity per lesion was 0.903 (95% CI, 0.846-0.960) and 0.883 (95% CI, 0.823-0.944) for PCM and FFDM, respectively. The average false-positive marks per image of noncancer cases were 0.265 (95% CI, 0.171-0.359) and 0.281 (95% CI, 0.188-0.374) for PCM and FFDM, respectively. Noninferiority P values for AUC, sensitivity (per case and per lesion), specificity, and average false-positive marks per image were all statistically significant (P < .001). The noninferiority P value for free-response ROC was <.025, from the 95% CI for the difference. Feature analysis resulted in PCM being preferred to FFDM by the readers for ≥70% of the cases. The average mean glandular dose for PCM was 0.74 mGy (95% CI, 0.722-0.759 mGy) and for FFDM was 1.23 mGy (95% CI, 1.199-1.262 mGy). In this study, radiologist performance with PCM was not inferior to that with conventional FFDM at an average 40% lower mean glandular dose.