Breast cancer risk prediction using machine learning: a systematic review.

Background: Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies. Objective and methods: This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers. Results: A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI. Conclusion: This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.

Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.

OBJECTIVE: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. MATERIALS AND METHODS: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. RESULTS: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). CONCLUSION: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

Mamografía con contraste: nuestra experiencia / Contrast-enhanced digital mammography: our experience

Resumen La mamografía contrastada (CEDM, contrast-enhanced digital mammography) es una herramienta nueva que ha ido implementándose de forma creciente. Aparece como alternativa a la resonancia magnética (RM), y al igual que esta, tiene como principio el uso de contraste endovenoso para explorar la angiogénesis tumoral. Combina la imagen de mamografía convencional (Mx) con la técnica de sustracción con energía dual poscontraste, lo que resulta en un incremento en la detección de cáncer de mama, en un tiempo corto de estudio y a un bajo costo. Es un método prometedor en casos seleccionados y de fácil lectura, siendo útil principalmente en pacientes con diagnóstico de cáncer de mama para detectar lesiones adicionales y determinar el tamaño tumoral, ayudando en la planificación quirúrgica, así como también en la evaluación de la respuesta a la neoadyuvancia. También en el seguimiento de pacientes operadas, para caracterizar lesiones dudosas en Mx y ecografía, o como alternativa ante contraindicación de la RM. El objetivo de este trabajo es valorar la utilidad de la mamografía contrastada en la práctica diaria y determinar sus principales indicaciones. Repasamos con casos propios las utilidades y características del método.

Abstract Contrast-enhanced digital mammography (CEDM) is an emerging tool that has been increasingly implemented. It appears as an alternative to magnetic resonance imaging (MRI), using intravenous contrast to explore tumor angiogenesis. It combines conventional mammography (Mx) with post-contrast dual energy subtraction technique, resulting in increased detection of breast cancer, in a short study time and at a low cost. It is a promising method in selected cases and easy to read, being useful mainly in patients with breast cancer to detect additional lesions and determine the tumor size, that helps surgical planning, as well as in the evaluation of post-neoadjuvant chemotherapy response in the follow-up of patients treated with surgery, to address inconclusive findings in screening mammogram, or as an alternative when MRI is contraindicated. The purpose of this article is to assess the usefulness of contrasted mammography in daily practice and to determine its main indications. We review with our own cases the applications and characteristics of this method.

State-of-the-art for contrast-enhanced mammography.

Contrast-enhanced mammography (CEM) is an emerging breast imaging technology with promise for breast cancer screening, diagnosis, and procedural guidance. However, best uses of CEM in comparison with other breast imaging modalities such as tomosynthesis, ultrasound, and MRI remain inconclusive in many clinical settings. This review article summarizes recent peer-reviewed literature, emphasizing retrospective reviews, prospective clinical trials, and meta-analyses published from 2020 to 2023. The intent of this article is to supplement prior comprehensive reviews and summarize the current state-of-the-art of CEM.

Breast Cancer Detection Using a Low-Dose Positron Emission Digital Mammography System.

Purpose To investigate the feasibility of low-dose positron emission mammography (PEM) concurrently to MRI to identify breast cancer and determine its local extent. Materials and Methods In this research ethics board-approved prospective study, participants newly diagnosed with breast cancer with concurrent breast MRI acquisitions were assigned independently of breast density, tumor size, and histopathologic cancer subtype to undergo low-dose PEM with up to 185 MBq of fluorine 18-labeled fluorodeoxyglucose (18F-FDG). Two breast radiologists, unaware of the cancer location, reviewed PEM images taken 1 and 4 hours following 18F-FDG injection. Findings were correlated with histopathologic results. Detection accuracy and participant details were examined using logistic regression and summary statistics, and a comparative analysis assessed the efficacy of PEM and MRI additional lesions detection (ClinicalTrials.gov: NCT03520218). Results Twenty-five female participants (median age, 52 years; range, 32-85 years) comprised the cohort. Twenty-four of 25 (96%) cancers (19 invasive cancers and five in situ diseases) were identified with PEM from 100 sets of bilateral images, showcasing comparable performance even after 3 hours of radiotracer uptake. The median invasive cancer size was 31 mm (range, 10-120). Three additional in situ grade 2 lesions were missed at PEM. While not significant, PEM detected fewer false-positive additional lesions compared with MRI (one of six [16%] vs eight of 13 [62%]; P = .14). Conclusion This study suggests the feasibility of a low-dose PEM system in helping to detect invasive breast cancer. Though large-scale clinical trials are essential to confirm these preliminary results, this study underscores the potential of this low-dose PEM system as a promising imaging tool in breast cancer diagnosis. ClinicalTrials.gov registration no. NCT03520218 Keywords: Positron Emission Digital Mammography, Invasive Breast Cancer, Oncology, MRI Supplemental material is available for this article. © RSNA, 2024 See also commentary by Barreto and Rapelyea in this issue.

Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography.

Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results: Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion: Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.

A spiculated mass target model for clinical image quality control in digital mammography.

OBJECTIVES: Quality assurance of breast imaging has a long history of using test objects to optimize and follow up imaging devices. In particular, the evaluation of new techniques benefits from suitable test objects. The applicability of a phantom consisting of spiculated masses to assess image quality and its dependence on dose in flat field digital mammography (FFDM) and digital breast tomosynthesis systems (DBT) is investigated. METHODS: Two spiculated masses in five different sizes each were created from a database of clinical tumour models. The masses were produced using 3D printing and embedded into a cuboid phantom. Image quality is determined by the number of spicules identified by human observers. RESULTS: The results suggest that the effect of dose on spicule detection is limited especially in cases with smaller objects and probably hidden by the inter-reader variability. Here, an average relative inter-reader variation of the counted number of 31% was found (maximum 83%). The mean relative intra-reader variability was found to be 17%. In DBT, sufficiently good results were obtained only for the largest masses. CONCLUSIONS: It is possible to integrate spiculated masses into a cuboid phantom. It is easy to print and should allow a direct and prompt evaluation of the quality status of the device by counting visible spicules. Human readout presented the major uncertainty in this study, indicating that automated readout may improve the reproducibility and consistency of the results considerably. ADVANCES IN KNOWLEDGE: A cuboid phantom including clinical objects as spiculated lesion models for visual assessing the image quality in FFDM and DBT was developed and is introduced in this work. The evaluation of image quality works best with the two larger masses with 21 spicules.

Threshold in breast compression reduction for full-field digital mammography and digital breast tomosynthesis.

INTRODUCTION: Breast compression is essential in mammography to improve image quality and reduce radiation dose. However, it can cause discomfort or even pain in women which could discourage them from attending future mammography examinations. Therefore, this study aims to explore the maximum reduction in breast compression in full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) that is achievable without impacting on image quality and dose. METHODS: Ten compression force (CF) levels (20N-110N, with 10N intervals) were assessed on Siemens MAMMOMAT Inspiration with Nuclear Associates 18-228 phantom. Imaging was carried out in craniocaudal projection using Automatic Exposure Control at 28 kVp with a Tungsten/Rhodium anode/filter combination, and at 50° sweep angle for DBT. Using ImageJ software, image quality of the acquired mammograms and central tomosynthesis slices were examined based on mass conspicuity (MC) and microcalcification conspicuity (MicroC). Entrance skin dose (ESD) and mean glandular dose (MGD) were recorded from Digital Imaging and Communication in Medicine image header. Linear regression was performed to examine the relationship between CF with ESD, MGD, MC and MicroC. Differences in image quality and radiation dose were assessed with one-way analysis of variance and Kruskal-Wallis H test. RESULTS: Significant correlations were noted between CF with ESD and MicroC for FFDM and DBT, with DBT also demonstrating associations with MGD and MC. No significant differences were observed for ESD, MGD, MC and MicroC when CF was reduced to 40N and 80N in FFDM and DBT respectively. CONCLUSION: This study demonstrated that CF can be reduced as low as 40N and 80N in FFDM and DBT respectively, without significant impact on image quality and radiation dose. IMPLICATIONS FOR PRACTICE: Reduced mammographic compression may reduce discomfort or pain in women, which may improve attendance rate in breast screening programmes. Findings from this study will provide reference for future work examining breast compression in mammography.

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

Screening in Patients With Dense Breasts: Comparison of Mammography, Artificial Intelligence, and Supplementary Ultrasound.

BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.

The role of an artificial intelligence software in clinical senology: a mammography multi-reader study.

PURPOSE: To evaluate the diagnostic role of a dedicated AI software in detecting anomalous breast findings on mammography and tomosynthesis images in the clinical setting, stand-alone and as aid of four readers. METHODS: A total of 210 patients with complete clinical and radiologic records were retrospectively analyzed. Pathology was used as the reference standard for patients undergoing surgery or biopsy, and a 1-year follow-up was used to confirm no change in the remaining patients. The image evaluation was performed by four readers with different levels of experience (a junior and three senior breast radiologists) using a 5-point Likert scale moving from 1 (definitively no cancer) to 5 (definitively cancer). The positivity of mammograms was assessed on the presence of any breast lesion (masses, architectural distortions, asymmetries, calcifications), including malignant and benign ones. A multi-reader multi-case analysis was performed. A p value < 0.05 was considered statistically significant. RESULTS: The stand-alone AI system achieved an accuracy of 71% (69% sensitivity and 73% specificity), which is overall lower than the value achieved by readers without AI. However, with the aid of AI, a significant increase of accuracy (p value = 0.004) and specificity (p value = 0.04) was achieved for the less experienced radiologist and a senior one. CONCLUSION: The use of AI software as a second reader for breast lesions assessment could play a crucial role in the clinical setting, by increasing sensitivity and specificity, especially for less experienced radiologists.

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.

Digital Breast Tomosynthesis With Synthetic Mammography: Are We Missing Calcifications That Matter?

OBJECTIVE: To determine whether there are differences in the biopsy outcomes for suspicious calcifications detected with screening mammography using the digital breast tomosynthesis and synthetic 2D (DBT/SM) technique compared to calcifications detected using the full-field digital (DM) technique. METHODS: This retrospective study was IRB approved. The records for all stereotactic biopsies performed for suspicious calcifications detected on screening mammograms using DM in 2011-2014 and DBT/SM in 2017-2020 were reviewed. We collected patient, imaging, and pathology data from the breast imaging database and from retrospective review of a subset of mammograms. The biopsy outcome results were categorized as benign, benign with upgrade potential (BWUP), and malignant based on final pathology. Frequencies and proportions of outcomes were calculated and compared using Mann-Whitney U tests and Wilcoxson signed-rank tests with P-values and 95% confidence intervals (95% CIs). RESULTS: From 2011 to 2014 (DM), 1274 stereotactic biopsies of calcifications yielded 74.2% (945/1274) benign, 11.5% (147/1274) BWUP, and 14.3% (182/1274) malignant outcomes. From 2017 to 2020 (DBT/SM), 1049 stereotactic biopsies yielded 65.2% (684/1049) benign, 15.6% (164/1049) BWUP, and 19.2% (201/1049) malignant outcomes. With DBT/SM, benign biopsy outcomes decreased (9.0%, 95% CI 0.87-11.53, P < 0.05), whereas malignant biopsy outcomes increased (4.9%, 95% CI 0.94-8.36, P < 0.05). There was no significant difference in BWUP biopsy outcomes and total biopsy rates between techniques (P > 0.05). CONCLUSION: Calcifications detected with screening DBT/SM technique were significantly more likely to be malignant than those found using DM. These results support using the DBT/SM technique without obtaining concurrent DM images.

Trends in patient dose and compression force for digital (DR) mammography systems over an eleven-year period.

This study evaluated trends in patient dose and compression force for screening digital (DR) mammography systems. The results of five audits (carried out in 2011, 2014, 2018, 2020 and 2022) were compared. For every audit, anonymised screening examinations from each system consisting of the standard craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts were analysed. Exposure parameters were extracted from the Digital Imaging and Communications in Medicine (DICOM) header and the mean glandular dose (MGD) for each image was calculated. Trends in the distribution of MGD, compressed breast thickness, compression force and compression force per radiographer were investigated. The mean MGD per image (and mean compressed breast thickness) was 1.20 mGy (58 mm), 1.53 mGy (59 mm), 1.83 mGy (61 mm), 1.94 mGy (60 mm) and 2.11 mGy (61 mm) for 2011, 2014, 2018, 2020 and 2022 respectively. The mean (and standard deviation) compression force was 114 (32) N, 112 (29) N, 108 (27) N, 104 (24) N and 100 (23) N for 2011, 2014, 2018, 2020 and 2022 respectively. The mean MGD per image has increased over time but remains below internationally established Diagnostic Reference Levels (DRLs). This increase is primarily due to a change in the distribution of the different manufacturers and digital detector technologies, rather than an increase in the dose of the individual systems over time. The mean compression force has decreased over time in response to client feedback surveys. The standard deviation has also reduced, indicating more consistent application of force.

Diagnostic value of digital breast tomosynthesis and synthetic images in patients with breast cancer.

Background: Different imaging techniques are used in the diagnosis of breast cancer. The low sensitivity of mammography to detect cancer in the dense breast parenchyma and the lack of standard application of digital breast tomosynthesis (DBT) are some of the problems. Therefore, breast cancer imaging techniques should be compared in terms of conspicuity and characterization of lesions. Aim: Full-field digital mammography (DM) and synthetic mammography (SM) which are obtained from the slices of digital breast tomosynthesis (DBT) give similar results in terms of conspicuity and characterization of the lesions in detecting breast cancer. Patients and Methods: In this retrospective study, 47 women diagnosed with breast cancer were included in the study. DM, SM, and DBT images were evaluated by scoring the conspicuity of the index lesion in the parenchyma and its characterization in terms of contour and shape with a 4-point scale. In addition, the conspicuity of the lesions in relation to lesion size and breast density was examined with these three techniques. Results: There is no significant difference between DM and SM techniques for index lesion conspicuity and characterization; however, the imaging score of DBT is significantly higher than other techniques for the conspicuity and characterization of the lesions. In terms of the conspicuity of the lesions in relation to lesion size, DM and SM techniques show significant difference according to the size of the lesion, whereas the DBT technique did not show significant difference. While mammography type is a determinant of lesion conspicuity in only DM and SM techniques, conspicuity findings do not differ significantly in the DBT technique. Conclusion: In conclusion, it was shown that standard images and SM images obtained from DBT did not differ significantly in terms of conspicuity and characterization of lesions. Thus, DBT is significantly superior to the DM and SM images. While the DM and SM images are more successful in showing large lesions and lesion detection in nondense breasts, DBT images were not affected by lesion size and breast density.

Diagnostic accuracy of cone-beam breast computed tomography and head-to-head comparison of digital mammography, magnetic resonance imaging and cone-beam breast computed tomography for breast cancer: a systematic review and meta-analysis.

Background: Cone-beam breast computed tomography (CBBCT) is a new breast imaging technique, however, CBBCT is not yet widely used, and its future application will depend on its diagnostic potential and application value. Therefore, it is of great clinical significance to systematically review and analyze the diagnostic accuracy of CBBCT for breast cancer detection in existing studies and compare it with other traditional imaging methods for the diagnosis of breast lesions. Methods: We searched PubMed, Embase, Web of Science, and Chinese databases until August 2022 for relevant papers. Studies evaluating the diagnostic accuracy of CBBCT in women with suspected breast cancer were included. Each study's quality was evaluated using the Quality Assessment of Diagnostic Performance Studies-2 (QUADAS-2) instrument. Results: Eighteen studies with a total of 1,792 patients were included in the analysis. The overall pooled sensitivity and specificity of CBBCT in diagnosing breast cancer were 0.95 [95% confidence interval (CI): 0.91-0.97] and 0.72 (95% CI: 0.62-0.80), respectively. The area under the curve (AUC) for CBBCT was 0.92 (95% CI: 0.90-0.94). In a head-to-head comparison of CBBCT and digital mammography (DM), eight trials with 992 patients were included in the study, and the AUCs for CBBCT and DM were 0.94 (95% CI: 0.92-0.96) and 0.83 (95% CI: 0.80-0.83), respectively. In a head-to-head comparison of CBBCT and magnetic resonance imaging (MRI), four trials with 203 patients were included in the analysis; the AUC for CBBCT and MRI were 0.88 (95% CI: 0.85-0.91) and 0.96 (95% CI: 0.94-0.97), respectively. Conclusions: This meta-analysis of CBBCT test accuracy indicated encouraging diagnostic performance. In the summary of head-to-head comparative studies, there is a tendency for CBBCT to have greater diagnostic accuracy than DM, although its diagnostic performance is marginally inferior to that of MRI. However, the meta-analysis results were derived from studies with limited sample sizes. There is a need for more extensive research in this setting.

Investigation of test methods for QC in dual-energy based contrast-enhanced digital mammography systems: II. Artefacts/uniformity, exposure time and phantom-based dosimetry.

Part II of this study describes constancy tests for artefacts and image uniformity, exposure time, and phantom-based dosimetry; these are applied to four mammography systems equipped with contrast enhanced mammography (CEM) capability. Artefacts were tested using a breast phantom that simulated breast shape and thickness change at the breast edge. Image uniformity was assessed using rectangular poly(methyl)methacrylate PMMA plates at phantom thicknesses of 20, 40 and 60 mm, for the low energy (LE), high energy (HE) images and the recombined CEM image. Uniformity of signal and of the signal to noise ratio was quantified. To estimate CEM exposure times, breast simulating blocks were imaged in automatic exposure mode. The resulting x-ray technique factors were then set manually and exposure time for LE and HE images and total CEM acquisition time was measured with a multimeter. Mean glandular dose (MGD) was assessed as a function of simulated breast thickness using three different phantom compositions: (i) glandular and adipose breast tissue simulating blocks combined to give glandularity values that were typical of those in a screening population, as thickness was changed (ii) PMMA sheets combined with polyethylene blocks (iii) PMMA sheets with spacers. Image uniformity was superior for LE compared to HE images. Two systems did not generate recombined images for the uniformity test when the detector was fully covered. Acquisition time for a CEM image pair for a 60 mm thick breast equivalent phantom ranged from 3.4 to 10.3 s. Phantom composition did not have a strong influence on MGD, with differences generally smaller than 10%. MGD for the HE images was lower than for the LE images, by a factor of between 1.3 and 4.0, depending on system and simulated breast thickness. When combined with the iodine signal assessment in part I, these tests provide a comprehensive assessment of CEM system imaging performance.

A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment.

OBJECTIVE: To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments. METHODS: This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance. RESULTS: Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83-0.91), 0.91 (95% CI, 0.87 - 0.96), and 0.92 (95% CI, 0.86-0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from - 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram. CONCLUSION: When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses. CLINICAL RELEVANCE STATEMENT: The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice. KEY POINTS: • Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions.

A cadaveric breast cancer tissue phantom for phase-contrast X-ray imaging applications.

BACKGROUND: As mammography X-ray imaging technologies advance and provide elevated contrast in soft tissues, a need has developed for reliable imaging phantoms for use in system design and component calibration. In advanced imaging modalities such as refraction-based methods, it is critical that developed phantoms capture the biological details seen in clinical precancerous and cancerous cases while minimizing artifacts that may be caused due to phantom production. This work presents the fabrication of a breast tissue imaging phantom from cadaveric breast tissue suitable for use in both transmission and refraction-enhanced imaging systems. METHODS: Human cancer cell tumors were grown orthotopically in nude athymic mice and implanted into the fixed tissue while maintaining the native tumor/adipose tissue interface. RESULTS: The resulting human-murine tissue hybrid phantom was mounted on a clear acrylic housing for absorption and refraction X-ray imaging. Digital breast tomosynthesis was also performed. CONCLUSION: Both attenuation-based imaging and refraction-based imaging of the phantom are presented to confirm the suitability of this phantom's use in both imaging modalities.

Quality control for digital tomosynthesis in the ECOG-ACRIN EA1151 TMIST trial.

BACKGROUND: The Tomosynthesis Mammography Imaging Screening Trial (TMIST), EA1151 conducted by the Eastern Cooperative Oncology Group (ECOG)/American College of Radiology Imaging Network (ACRIN) is a randomized clinical trial designed to assess the effectiveness for breast cancer screening of digital breast tomosynthesis (TM) compared to digital mammography (DM). Equipment from multiple vendors is being used in the study. PURPOSE: For the findings of the study to be valid and capture the true capacities of the two technology types, it is important that all equipment is operated within appropriate parameters with regard to image quality and dose. A harmonized QC program was established by a core physics team. Since there are over 120 trial sites, a centralized, automated QC program was chosen as the most practical design. This report presents results of the weekly QC testing program. A companion paper will review quality monitoring based on data from the headers of the patient images. METHODS: Study images are collected centrally after de-identification using the "TRIAD" application developed by ACR. The core physics team devised and implemented a minimal set of quality control (QC) tests to evaluate the tomosynthesis and 2D mammography systems. Weekly, monthly and annual testing is performed by the site mammography technologists with images submitted directly to the physics core. The weekly physics QC tests are described: SDNR of a low-contrast mass object, artifact spread, spatial resolution, tracking of technical factors, and in-slice noise power spectra. RESULTS: As of December 31, 2022 (5 years), 145 sites with 411 machines had submitted QC data. A total of 136 742 TMIST participant screening imaging studies had been performed. The 5th and 95th percentile mean glandular doses for a single tomosynthesis exposure to a 4.0 cm thick PMMA phantom ("standard breast phantom") were 1.24 and 1.68 mGy respectively. The largest sources of QC non-conformance were: operator error, not following the QC protocol exactly, unreported software updates and preventive maintenance activities that affected QC setpoints. Noise power spectra were measured, however, standardization of performance targets across machine types and software revisions was difficult. Nevertheless, for each machine type, test measurement results were very consistent when the protocol was followed. Deviations in test results were mostly related to software and hardware changes. CONCLUSION: Most systems performed very consistently. Although this is a harmonized program using identical phantoms and testing protocols, it is not appropriate to apply universal threshold or target metrics across the machine types because the systems have different non-linear reconstruction algorithms and image display filters. It was found to be more useful to assess pass/fail criteria in terms of relative deviations from baseline values established when a system is first characterized and after equipment is changed. Generally, systems which needed repair failed suddenly, but in retrospect, for a few cases, drops in SDNR and increases in mAs were observed prior to tube failure. TMIST is registered as NCT03233191 by Clinicaltrials.gov.