Showing papers on "Digital mammography published in 2022"

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Abstract: Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.

Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation

Abstract: Background Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality. Purpose To retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations. Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired between January 2015 and December 2016 were retrospectively collected from the Tomosynthesis Cordoba Screening Trial. An AI system computed a cancer risk score (range, 1-100) for DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have noninferior sensitivity compared with the human readings (noninferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test. Results A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women (mean age, 58 years ± 6 [standard deviation]) were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved noninferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P < .001) of up to 2% (95% CI: -2.4, -1.6). For DBT, AI achieved noninferior sensitivity as a single (77%; 87 of 113; 95% CI: 68.4, 83.8) or double (81.4%; 92 of 113; 95% CI: 73.3, 87.5) reader, but with a higher recall rate (P < .001) of up to 12.3% (95% CI: 11.7, 12.9). Conclusion Artificial intelligence could replace radiologists' readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital mammography but a higher recall rate for digital breast tomosynthesis. Published under a CC BY 4.0 license. See also the editorial by Fuchsjäger and Adelsmayr in this issue.

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Abstract: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.

Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis.

Abstract: Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the use of AI to reduce workload by filtering out normal DBT screens. Materials and Methods The retrospective study included 13 306 DBT examinations from 9919 women performed between June 2013 and November 2018 from two health care networks. The cohort was split into training, validation, and test sets (3948, 1661, and 4310 women, respectively). A workflow was simulated in which the AI model classified cancer-free examinations that could be dismissed from the screening worklist and used the original radiologists' interpretations on the rest of the worklist examinations. The AI system was also evaluated with a reader study of five breast radiologists reading the DBT mammograms of 205 women. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate were evaluated in both studies. Statistics were computed across 10 000 bootstrap samples to assess 95% CIs, noninferiority, and superiority tests. Results The model was tested on 4310 screened women (mean age, 60 years ± 11 [standard deviation]; 5182 DBT examinations). Compared with the radiologists' performance (417 of 459 detected cancers [90.8%], 477 recalls in 5182 examinations [9.2%]), the use of AI to automatically filter out cases would result in 39.6% less workload, noninferior sensitivity (413 of 459 detected cancers; 90.0%; P = .002), and 25% lower recall rate (358 recalls in 5182 examinations; 6.9%; P = .002). In the reader study, AUC was higher in the standalone AI compared with the mean reader (0.84 vs 0.81; P = .002). Conclusion The artificial intelligence model was able to identify normal digital breast tomosynthesis screening examinations, which decreased the number of examinations that required radiologist interpretation in a simulated clinical workflow. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Association of Screening With Digital Breast Tomosynthesis vs Digital Mammography With Risk of Interval Invasive and Advanced Breast Cancer.

Abstract: Importance Digital breast tomosynthesis (DBT) was developed with the expectation of improving cancer detection in women with dense breasts. Studies are needed to evaluate interval invasive and advanced breast cancer rates, intermediary outcomes related to breast cancer mortality, by breast density and breast cancer risk. Objective To evaluate whether DBT screening is associated with a lower likelihood of interval invasive cancer and advanced breast cancer compared with digital mammography by extent of breast density and breast cancer risk. Design, Setting, and Participants Cohort study of 504 427 women aged 40 to 79 years who underwent 1 003 900 screening digital mammography and 375 189 screening DBT examinations from 2011 through 2018 at 44 US Breast Cancer Surveillance Consortium (BCSC) facilities with follow-up for cancer diagnoses through 2019 by linkage to state or regional cancer registries. Exposures Breast Imaging Reporting and Data System (BI-RADS) breast density; BCSC 5-year breast cancer risk. Main Outcomes and Measures Rates per 1000 examinations of interval invasive cancer within 12 months of screening mammography and advanced breast cancer (prognostic pathologic stage II or higher) within 12 months of screening mammography, both estimated with inverse probability weighting. Results Among 504 427 women in the study population, the median age at time of mammography was 58 years (IQR, 50-65 years). Interval invasive cancer rates per 1000 examinations were not significantly different for DBT vs digital mammography (overall, 0.57 vs 0.61, respectively; difference, -0.04; 95% CI, -0.14 to 0.06; P = .43) or among all the 836 250 examinations with BCSC 5-year risk less than 1.67% (low to average-risk) or all the 413 061 examinations with BCSC 5-year risk of 1.67% or higher (high risk) across breast density categories. Advanced cancer rates were not significantly different for DBT vs digital mammography among women at low to average risk or at high risk with almost entirely fatty, scattered fibroglandular densities, or heterogeneously dense breasts. Advanced cancer rates per 1000 examinations were significantly lower for DBT vs digital mammography for the 3.6% of women with extremely dense breasts and at high risk of breast cancer (13 291 examinations in the DBT group and 31 300 in the digital mammography group; 0.27 vs 0.80 per 1000 examinations; difference, -0.53; 95% CI, -0.97 to -0.10) but not for women at low to average risk (10 611 examinations in the DBT group and 37 796 in the digital mammography group; 0.54 vs 0.42 per 1000 examinations; difference, 0.12; 95% CI, -0.09 to 0.32). Conclusions and Relevance Screening with DBT vs digital mammography was not associated with a significant difference in risk of interval invasive cancer and was associated with a significantly lower risk of advanced breast cancer among the 3.6% of women with extremely dense breasts and at high risk of breast cancer. No significant difference was observed in the 96.4% of women with nondense breasts, heterogeneously dense breasts, or with extremely dense breasts not at high risk.

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Abstract: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.

Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial.

Abstract: Two dimensional (2D) full-field digital mammography is the current standard of breast cancer screening. Digital breast tomosynthesis generates pseudo-three dimensional datasets of the breast from which synthesised 2D (s2D) mammograms can be reconstructed. This innovative approach reduces the likelihood of overlapping breast tissues that can conceal features of malignancy. We aimed to compare digital breast tomosynthesis plus s2D mammography with digital screening mammography for the detection of invasive breast cancer.TOSYMA was a randomised, open-label, superiority trial done at 17 screening units in two federal states of Germany. Eligible participants were women aged 50-69 years who had been invited to participate in a population-wide, quality-controlled mammography screening programme. Women were randomly assigned (1:1) to digital breast tomosynthesis plus s2D mammography or digital mammography alone using block randomisation (block size of 32), stratified by site. The primary endpoints were the detection rate of invasive breast cancer and invasive interval cancer rate at 24 months, analysed in the modified full analysis set, which included all randomly assigned participants who underwent either type of screening examination. Ten examinations, corresponding to a second study participation, were excluded. Analyses were done according to the intention-to-treat principle. Interval cancer rates will be reported in the follow-up study. Safety was assessed in the as-treated population, which included all participants who were randomly assigned. This trial is registered with ClinicalTrials.gov, NCT03377036, and is closed to accrual.Between July 5, 2018, and Dec 30, 2020, 99 689 women were randomly assigned to digital breast tomosynthesis plus s2D mammography (n=49 804) or digital mammography (n=49 830). Invasive breast cancers were detected in 354 of 49 715 women with evaluable primary endpoint data in the digital breast tomosynthesis plus s2D group (detection rate 7·1 cases per 1000 women screened) and in 240 of 49 762 women in the digital mammography group (4·8 cases per 1000 women screened; odds ratio 1·48 [95% CI 1·25-1·75]; p<0·0001). Adverse events and device deficiencies were rare (six adverse events in each group; 23 device deficiencies in the digital breast tomosynthesis plus s2D group vs five device deficiencies in the digital mammography group) and no serious adverse events were reported.The results from this study indicate that the detection rate for invasive breast cancer was significantly higher with digital breast tomosynthesis plus s2D mammography than digital mammography alone. Evaluation of interval cancer rates in the follow-up study will further help to investigate incremental long-term benefits of digital breast tomosynthesis screening.Deutsche Forschungsgemeinschaft (German Research Foundation).

Deep Multiple Instance Learning for Automatic Breast Cancer Assessment Using Digital Mammography

Abstract: Breast cancer causes serious public health problems; it is the most common cancer among women worldwide. Screening and early detection of signs of breast cancer increase the chance of survival. Early diagnosis is a crucial task for radiologists and physicians. Therefore, many computer-aided detection and diagnosis (CADx) systems are being developed to ensure the survival of radiologists’ decisions. In this article, we describe a framework to automate assessment of suspicious regions, detected in screening mammography, without having carried out additional examinations, especially unnecessary biopsies in the case where the suspect regions are benign tumors. The setup of the proposed framework is ordered as follows: regions of interest (ROIs) have been segmented using a modified $K$ -means algorithm; the bidimensional empirical mode decomposition (BEMD) algorithm is applied to derive many layers [bidimensional intrinsic mode function (BIMF)] from ROIs. Then, textural features are extracted from the obtained ROIs. First, directly from segmented ROI, second from the ROI and its sublayers (BIMFs + Residue). The features extracted in the second time have been grouped into a bag descriptive of the ROI under consideration. This bag is the input parameter of the classification algorithm based on the support vector machine (which has been confirmed to be beneficial for the classification of breast cancer). The average obtained sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) rates, are, respectively, 98.60%, 98.65%, 98.62%, and 98.23%. Generally, the experimental results in INbreast, digital database of screening mammography (DDSM), and Mammography Image Analysis Society (MIAS) datasets demonstrate the robustness and the efficiency of the developed framework compared to previous works in the literature and have shown a significant advance.

VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography

Abstract: Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. However, most published datasets of mammography are either limited on sample size or digitalized from screen-film mammography (SFM), hindering the development of CADe/x tools which are developed based on full-field digital mammography (FFDM). To overcome this challenge, we introduce VinDr-Mammo, a new benchmark dataset of FFDM for detecting and diagnosing breast cancer and other diseases in mammography. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. It is created for the assessment of Breast Imaging Reporting and Data System (BI-RADS) and density at the breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available on https://physionet.org/ as a new imaging resource to promote advances in developing CADe/x tools for breast cancer screening.

Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification

Abstract: Simple Summary In this study, we propose a novel deep-learning method based on multi-stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models to classify mammographic masses as either benign or malignant. The proposed method alleviates the challenge of obtaining large amounts of labeled mammogram training data by utilizing a large number of cancer cell line microscopic images as an intermediate domain of learning between the natural domain (ImageNet) and medical domain (mammography). Moreover, our method does not utilize patch separation (to segment the region of interest before classification), which renders it computationally simple and fast compared to previous studies. The findings of this study are of crucial importance in the early diagnosis of breast cancer in young women with dense breasts because mammography does not provide reliable diagnosis in such cases. Abstract Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a p-value of 0.0029. Furthermore, our patchless approach performed better than patch- and whole image-based methods, improving test accuracy by 8% (91.41% vs. 99.34%), tested on the INbreast dataset. The proposed method is of significant importance in solving the need for a large training dataset as well as reducing the computational burden in training and implementing the mammography-based deep-learning models for early diagnosis of breast cancer.

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

Abstract: Background: Contrast-enhanced mammography (CEM) and contrast-enhanced magnetic resonance imaging (CE-MRI) are commonly used in the screening of breast cancer. The present systematic review aimed to summarize, critically analyse, and meta-analyse the available evidence regarding the role of CE-MRI and CEM in the early detection, diagnosis, and preoperative assessment of breast cancer. Methods: The search was performed on PubMed, Google Scholar, and Web of Science on 28 July 2021 using the following terms “breast cancer”, “preoperative staging”, “contrast-enhanced mammography”, “contrast-enhanced spectral mammography”, “contrast enhanced digital mammography”, “contrast-enhanced breast magnetic resonance imaging” “CEM”, “CESM”, “CEDM”, and “CE-MRI”. We selected only those papers comparing the clinical efficacy of CEM and CE-MRI. The study quality was assessed using the QUADAS-2 criteria. The pooled sensitivities and specificity of CEM and CE-MRI were computed using a random-effects model directly from the STATA “metaprop” command. The between-study statistical heterogeneity was tested (I2-statistics). Results: Nineteen studies were selected for this systematic review. Fifteen studies (1315 patients) were included in the metanalysis. Both CEM and CE-MRI detect breast lesions with a high sensitivity, without a significant difference in performance (97% and 96%, respectively). Conclusions: Our findings confirm the potential of CEM as a supplemental screening imaging modality, even for intermediate-risk women, including females with dense breasts and a history of breast cancer.

Comparison between two packages for pectoral muscle removal on mammographic images

Abstract: Abstract Background Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. Purpose To compare performance of the two packages on a single database of FFDM images. Methods Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA’s and OpenBreast’s removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal–Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. Results FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA’s Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P < 0.05). No dependence on breast density or laterality has been found (Kruskal–Wallis test P > 0.05). Conclusion: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation.

Cumulative Probability of False-Positive Results After 10 Years of Screening With Digital Breast Tomosynthesis vs Digital Mammography

Abstract: Key Points Question Is there a difference between screening with digital breast tomosynthesis vs digital mammography in the probability of false-positive results after 10 years of screening? Findings In this comparative effectiveness study of 903 495 individuals undergoing 2 969 055 screening examinations, the 10-year cumulative probability of receiving at least 1 false-positive recall was 6.7% lower for tomosynthesis vs digital mammography with annual screening and 2.4% lower for tomosynthesis vs digital mammography with biennial screening, a significant difference. Meaning The findings of this study suggest that digital breast tomosynthesis is associated with a lower cumulative probability of false-positive results compared with digital mammography; biennial vs annual screening was associated with larger reductions in cumulative false-positive risk for both modalities.

Radiation Dose of Contrast-Enhanced Mammography: A Two-Center Prospective Comparison

Abstract: Simple Summary Contrast-enhanced mammography (CEM) is a dual-energy technique where low- and high-energy images are acquired for each mammographic view after contrast agent administration, and are then recombined to enhance potential contrast uptake. As CEM is increasingly used for both screening and diagnostic applications in breast imaging, but its associated radiation dose has been investigated only by single-center studies, we aimed to evaluate the CEM per-patient radiation dose on a large population in a bicentric setting, pooling data from two prospective studies employing the same model of mammography units. The CEM radiation dose showed a 6.2% difference between the two centers, mainly attributable to the study populations’ characteristics and to manufacturing differences between the two systems. The CEM dose was about 30% higher than that of standard digital mammography. Such an increment was close to the dose increase reported for digital breast tomosynthesis, which is already used in both screening and clinical settings. Thus, considering the extensively demonstrated diagnostic gain granted by CEM over these non-contrast-enhanced techniques, radiation dose concerns should not hinder ever-wider clinical implementations of CEM. Abstract The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM mean glandular dose (MGD) was computed for low energy (LE) and high energy (HE) images and their sum was calculated for each view. MGD and related parameters (entrance dose, breast thickness, compression, and density) were compared between the two centers using the Mann–Whitney test. Finally, per-patient MGD was calculated by pooling the two datasets and determining the contribution of LE and HE images. A total of 348 CEM examinations were analyzed (228 from Center 1 and 120 from Center 2). The median total MGD per view was 2.33 mGy (interquartile range 2.19–2.51 mGy) at Center 1 and 2.46 mGy (interquartile range 2.32–2.70 mGy) at Center 2, with a 0.15 mGy median difference (p < 0.001) equal to 6.2%. LE-images contributed between 64% and 77% to the total patient dose in CEM, with the remaining 23–36% being associated with HE images. The mean radiation dose for a two-view bilateral CEM exam was 4.90 mGy, about 30% higher than for digital mammography.

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Abstract: Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

Breast Density and Breast Cancer Screening with Digital Breast Tomosynthesis: A TOSYMA Trial Subanalysis.

Abstract: Background Digital breast tomosynthesis (DBT) plus synthesized mammography (SM) reduces the diagnostic pitfalls of tissue superimposition, which is a limitation of digital mammography (DM). Purpose To compare the invasive breast cancer detection rate (iCDR) of DBT plus SM versus DM screening for different breast density categories. Materials and Methods An exploratory subanalysis of the TOmosynthesis plus SYnthesized MAmmography (TOSYMA) study, a randomized, controlled, multicenter, parallel-group trial recruited within the German mammography screening program from July 2018 to December 2020. Women aged 50-69 years were randomly assigned (1:1) to DBT plus SM or DM screening examination. Breast density categories A-D were visually assessed according to the Breast Imaging Reporting and Data System Atlas. Exploratory analyses were performed of the iCDR in both study arms and stratified by breast density, and odds ratios and 95% CIs were determined. Results A total of 49 762 women allocated to DBT plus SM and 49 796 allocated to DM (median age, 57 years [IQR, 53-62 years]) were included. In the DM arm, the iCDR was 3.6 per 1000 screening examinations in category A (almost entirely fatty) (16 of 4475 screenings), 4.3 in category B (102 of 23 534 screenings), 6.1 in category C (116 of 19 051 screenings), and 2.3 in category D (extremely dense breasts) (six of 2629 screenings). The iCDR in the DBT plus SM arm was 2.7 per 1000 screening examinations in category A (12 of 4439 screenings), 6.9 in category B (154 of 22 328 screenings), 8.3 in category C (156 of 18 772 screenings), and 8.1 in category D (32 of 3940 screenings). The odds ratio for DM versus DBT plus SM in category D was 3.8 (95% CI: 1.5, 11.1). The invasive cancers detected with DBT plus SM were most often grade 2 tumors; in category C, it was 58% (91 of 156 invasive cancers), and in category D, it was 47% (15 of 32 invasive cancers). Conclusion The TOmosynthesis plus SYnthesized MAmmography trial revealed higher invasive cancer detection rates with digital breast tomosynthesis plus synthesized mammography than digital mammography in dense breasts, relatively and absolutely most marked among women with extremely dense breasts. ClinicalTrials.gov registration no.: NCT03377036 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Breast Cancer Detection in Mammograms Using Deep Learning

Abstract: Breast cancer is the most lethal cancer among women. Early-stage diagnosis may reduce the mortality associated with breast cancer subjects. Diagnosis can be made with screening mammography. The main challenge of screening mammography is its high risk of false positives and false negatives. This paper presents the detection of breast cancer in mammograms using the VGG16 model of deep learning approaches. The VGG16 model is trained and tested on 322 images from the MIAS dataset. It performs better as compared to AlexNet, EfficientNet, and GoogleNet models. Classification of mammograms will improve mammograms’ efficient screening, which will be a support system to radiologists.

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research

Abstract: Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images. We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems. The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifications images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one finding. This is the first dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases. Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal findings in images.

Performance evaluation of digital mammography, digital breast tomosynthesis and ultrasound in the detection of breast cancer using pathology as gold standard: an institutional experience

Abstract: Abstract Background Mammography is the primary imaging modality for diagnosing breast cancer in women more than 40 years of age. Digital breast tomosynthesis (DBT), when supplemented with digital mammography (DM), is useful for increasing the sensitivity and improving BIRADS characterization by removing the overlapping effect. Ultrasonography (US), when combined with the above combination, further increases the sensitivity and diagnostic confidence. Since most of the research regarding tomosynthesis has been in screening settings, we wanted to quantify its role in diagnostic mammography. The purpose of this study was to assess the performance of DM alone vs. DM combined with DBT vs. DM plus DBT and ultrasound in diagnosing malignant breast neoplasms with the gold standard being histopathology or cytology. Results A prospective study of 1228 breasts undergoing diagnostic or screening mammograms was undertaken at our institute. Patients underwent 2 views DM, single view DBT and US. BIRADS category was updated after each step. Final categorization was made with all three modalities combined and pathological correlation was done for those cases in which suspicious findings were detected, i.e. 256 cases. Diagnosis based on pathology was done for 256 cases out of which 193 (75.4%) were malignant and the rest 63 (24.6%) were benign. The diagnostic accuracy of DM alone was 81.1%. Sensitivity, Specificity, PPV and NPV were 87.8%, 60%, 81.3% and 61.1%, respectively. With DM + DBT the diagnostic accuracy was 84.8%. Sensitivity, Specificity, PPV and NPV were 92%, 56.5%, 89% and 65%, respectively. The diagnostic accuracy of DM + DBT + US was found to be 85.1% and Sensitivity, Specificity, PPV and NPV were 96.3%, 50.7%, 85.7% and 82%, respectively. Conclusion The combination of DBT to DM led to higher diagnostic accuracy, sensitivity and PPV. The addition of US to DM and DBT further increased the sensitivity and diagnostic accuracy and significantly increased the NPV even in diagnostic mammograms and should be introduced in routine practice for characterizing breast neoplasms.

Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms

Abstract: Objective: Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. Methods: In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. Results: Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). Conclusion: These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses.

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

Abstract: INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient’s health can be improved. OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Co-occurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms. METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification. Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality. RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset. CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods. In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification. Neighbor, Artificial Neural Network, Random Forest and eXtreme Gradient Boosting.

Breast Cancer: Using Deep Transfer Learning Techniques AlexNet Convolutional Neural Network For Breast Tumor Detection in Mammography Images

Abstract: Breast cancer is the most common form of cancer among women worldwide. Mammography has become a valuable tool for detecting breast cancer. We were able to achieve the purpose of our study, which was to create an accurate Convolutional Neural Network (CNN) model that classifies mammography images into normal and abnormal using deep transfer learning and data augmentation approaches, to avoid overfitting issues with images. The Mammographic Image Analysis Society MiniMammographic Database (MiniMIAS) was used to train and test the CNN model. The shortage of abnormal images in the MiniMIAS caused a low accuracy. Because of that, 92 abnormal images were added from the Digital Database for Screening Mammography (DDSM), which leads us to great accuracy. The proposed method starts with a pre-processing step which includes several filters that eliminate any noises, background, enhances the images, and data augmentation for better training. AlexNet was modified and trained after splitting the dataset into 75%, 5%, and 20% as training, validation, and testing sets respectively. The evaluation results were not very satisfying with the MiniMIAS database with 96.87%. On the other hand with balanced data, the obtained results were very satisfying with 99.99%, and better results according to the existing models, which prove that the chosen filters in the pre-processing phase, as well as the chosen pre-trained model AlexNet, is very useful and suitable for breast tumor detection.

Differential detection by breast density for digital breast tomosynthesis versus digital mammography population screening: a systematic review and meta-analysis

Abstract: Abstract Background We examined whether digital breast tomosynthesis (DBT) detects differentially in high- or low-density screens. Methods We searched six databases (2009–2020) for studies comparing DBT and digital mammography (DM), and reporting cancer detection rate (CDR) and/or recall rate by breast density. Meta-analysis was performed to pool incremental CDR and recall rate for DBT (versus DM) for high- and low-density (dichotomised based on BI-RADS) and within-study differences in incremental estimates between high- and low-density. Screening settings (European/US) were compared. Results Pooled within-study difference in incremental CDR for high- versus low-density was 1.0/1000 screens (95% CI: 0.3, 1.6; p = 0.003). Estimates were not significantly different in US (0.6/1000; 95% CI: 0.0, 1.3; p = 0.05) and European (1.9/1000; 95% CI: 0.3, 3.5; p = 0.02) settings ( p for subgroup difference = 0.15). For incremental recall rate, within-study differences between density subgroups differed by setting ( p < 0.001). Pooled incremental recall was less in high- versus low-density screens (−0.9%; 95% CI: −1.4%, −0.4%; p < 0.001) in US screening, and greater (0.8%; 95% CI: 0.3%, 1.3%; p = 0.001) in European screening. Conclusions DBT has differential incremental cancer detection and recall by breast density. Although incremental CDR is greater in high-density, a substantial proportion of additional cancers is likely to be detected in low-density screens. Our findings may assist screening programmes considering DBT for density-tailored screening.

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research

Abstract: Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images. We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems. The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifications images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one finding. This is the first dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases. Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal findings in images.

Assessment of screen‐recalled abnormalities for digital breast tomosynthesis versus digital mammography screening in the BreastScreen Maroondah trial

Abstract: Australia's first population‐based pilot trial comparing digital breast tomosynthesis (DBT) and digital mammography (DM) screening reported detection measures in 2019. This study describes the trial's secondary outcomes pertaining to the assessment process in women screened with DBT or DM, including the type of recalled abnormalities and the procedures performed.