Showing papers on "Mammography published in 2018"

Breast Cancer Screening in Women at Higher-Than-Average Risk: Recommendations From the ACR

Abstract: Early detection decreases breast cancer mortality. The ACR recommends annual mammographic screening beginning at age 40 for women of average risk. Higher-risk women should start mammographic screening earlier and may benefit from supplemental screening modalities. For women with genetics-based increased risk (and their untested first-degree relatives), with a calculated lifetime risk of 20% or more or a history of chest or mantle radiation therapy at a young age, supplemental screening with contrast-enhanced breast MRI is recommended. Breast MRI is also recommended for women with personal histories of breast cancer and dense tissue, or those diagnosed by age 50. Others with histories of breast cancer and those with atypia at biopsy should consider additional surveillance with MRI, especially if other risk factors are present. Ultrasound can be considered for those who qualify for but cannot undergo MRI. All women, especially black women and those of Ashkenazi Jewish descent, should be evaluated for breast cancer risk no later than age 30, so that those at higher risk can be identified and can benefit from supplemental screening.

Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review

Abstract: Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.

Breast cancer diagnosis: Imaging techniques and biochemical markers.

Abstract: Breast cancer is a complex disease which is found as the second cause of cancer-associated death among women. Accumulating of evidence indicated that various factors (i.e., gentical and envirmental factors) could be associated with initiation and progression of breast cancer. Diagnosis of breast cancer patients in early stages is one of important aspects of breast cancer treatment. Among of various diagnosis platforms, imaging techniques are main diagnosis approaches which could provide valuable data on patients with breast cancer. It has been showed that various imaging techniques such as mammography, magnetic resonance imaging (MRI), positron-emission tomography (PET), Computed tomography (CT), and single-photon emission computed tomography (SPECT) could be used for diagnosis and monitoring patients with breast cancer in various stages. Beside, imaging techniques, utilization of biochemical biomarkers such as proteins, DNAs, mRNAs, and microRNAs could be employed as new diagnosis and therapeutic tools for patients with breast cancer. Here, we summarized various imaging techniques and biochemical biomarkers could be utilized as diagnosis of patients with breast cancer. Moreover, we highlighted microRNAs and exosomes as new diagnosis and therapeutic biomarkers for monitoring patients with breast cancer.

A deep learning method for classifying mammographic breast density categories.

Abstract: Purpose Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., “scattered density” and “heterogeneously dense”. The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. Methods In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. Results The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset. Conclusions Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.

An overview of mammographic density and its association with breast cancer.

Abstract: In 2017, breast cancer became the most commonly diagnosed cancer among women in the US. After lung cancer, breast cancer is the leading cause of cancer-related mortality in women. The breast consists of several components, including milk storage glands, milk ducts made of epithelial cells, adipose tissue, and stromal tissue. Mammographic density (MD) is based on the proportion of stromal, epithelial, and adipose tissue. Women with high MD have more stromal and epithelial cells and less fatty adipose tissue, and are more likely to develop breast cancer in their lifetime compared to women with low MD. Because of this correlation, high MD is an independent risk factor for breast cancer. Further, mammographic screening is less effective in detecting suspicious lesions in dense breast tissue, which can lead to late-stage diagnosis. Molecular differences between dense and non-dense breast tissues explain the underlying biological reasons for why women with dense breasts are at a higher risk for developing breast cancer. The goal of this review is to highlight the current molecular understanding of MD, its association with breast cancer risk, the demographics pertaining to MD, and the environmental factors that modulate MD. Finally, we will review the current legislation regarding the disclosure of MD on a traditional screening mammogram and the supplemental screening options available to women with dense breast tissue.

Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model.

Abstract: Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

Breast Cancer Screening Using Tomosynthesis or Mammography: A Meta-analysis of Cancer Detection and Recall.

Abstract: Background Tomosynthesis approximates a 3D mammogram of the breast, reducing parenchymal overlap that masks cancers or creates false "lesions" on 2D mammography, and potentially enabling more accurate detection of breast cancer. We compared breast cancer screening detection and recall in asymptomatic women for tomosynthesis vs 2D mammography. Methods A systematic review and random effects meta-analysis were undertaken. Electronic databases (2009-July 2017) were searched for studies comparing tomosynthesis and 2D mammography in asymptomatic women who attended population breast cancer screening and reporting cancer detection rate (CDR) and recall rate. All statistical tests were two-sided. Results Seventeen studies (1 009 790 participants) were included from 413 citations. The pooled incremental CDR for tomosynthesis was 1.6 cancers per 1000 screens (95% confidence interval [CI] = 1.1 to 2.0, P < .001, I2 = 36.9%). Incremental CDR was statistically significantly higher for European/Scandinavian studies, all using a "paired" design where women had both tests (2.4 per 1000 screens, 95% CI = 1.9 to 2.9, P < .001, I2 = 0.0%) compared with US ("unpaired") studies (1.1 per 1000 screens, 95% CI = 0.8 to 1.5, P < .001, I2 = 0.0%; P < .001 between strata). The recall rate for tomosynthesis was statistically significantly lower than for 2D mammography (pooled absolute reduction = -2.2%, 95% CI = -3.0 to -1.4, P < .001, I2 = 98.2%). Stratified analyses showed a decrease in US studies (pooled difference in recall rate = -2.9%, 95% CI = -3.5 to -2.4, P < .001, I2 = 92.9%) but not European/Scandinavian studies (0.5% increase in recall, 95% CI = -0.1 to 1.2, P = .12, I2 = 93.5%; P < .001 between strata). Results were similar in sensitivity analyses excluding studies with overlapping cohorts. Conclusions Tomosynthesis improves CDR and reduces recall; however, effects are dependent on screening setting, with greater improvement in CDR in European/Scandinavian studies (biennial screening) and reduction in recall in US studies with high baseline recall.

One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study.

Abstract: Summary Background Digital breast tomosynthesis is an advancement of the mammographic technique, with the potential to increase detection of lesions during breast cancer screening. The main aim of the Malmo Breast Tomosynthesis Screening Trial (MBTST) was to investigate the accuracy of one-view digital breast tomosynthesis in population screening compared with standard two-view digital mammography. Methods In this prospective, population-based screening study, of women aged 40–74 years invited to attend national breast cancer screening at Skane University Hospital, Malmo, Sweden, a random sample was asked to participate in the trial (every third woman who was invited to attend regular screening was invited to participate). Participants had to be able to speak English or Swedish and were excluded from the study if they were pregnant. Participants underwent screening with two-view digital mammography (ie, craniocaudal and mediolateral oblique views) followed by one-view digital breast tomosynthesis with reduced compression in the mediolateral oblique view (with a wide tomosynthesis angle of 50°) at one screening visit. Images were read with masked double reading and scoring by two separate reading groups, one for each method, made up of seven radiologists. Any cancer detected with a malignancy probability score of three or higher by any reader in either group was discussed in a consensus meeting of at least two readers, from which the decision of whether or not to recall the woman for further investigation was made. The primary outcome measures were sensitivity and specificity of breast cancer detection. Secondary outcome measures were screening performance measures of cancer detection, recall, and interval cancers (cancers clinically detected between screenings), and positive predictive value for screen recalls and negative predictive value of each method. Outcomes were analysed in the per-protocol population. Follow-up of the participants for at least 2 years allowed for identification of interval cancers. This trial is registered with ClinicalTrials.gov , number NCT01091545 . Findings Between Jan 27, 2010, and Feb 13, 2015, of 21 691 women invited, 14 851 (68%) agreed to participate. Three women withdrew consent during follow-up and were excluded from the analyses. 139 breast cancers were detected in 137 ( Interpretation Breast cancer screening by use of one-view digital breast tomosynthesis with a reduced compression force has higher sensitivity at a slightly lower specificity for breast cancer detection compared with two-view digital mammography and has the potential to reduce the radiation dose and screen-reading burden required by two-view digital breast tomosynthesis with two-view digital mammography. Funding The Swedish Cancer Society, The Swedish Research Council, The Breast Cancer Foundation, The Swedish Medical Society, The Crafoord Foundation, The Gunnar Nilsson Cancer Foundation, The Skane University Hospital Foundation, Governmental funding for clinical research, The South Swedish Health Care Region, The Malmo Hospital Cancer Foundation and The Cancer Foundation at the Department of Oncology, Skane University Hospital.

In Situ Detection of Plasma Exosomal MicroRNA-1246 for Breast Cancer Diagnostics by a Au Nanoflare Probe

Abstract: Breast cancer is the second cause of cancer mortality in women globally. Early detection, treatment, and metastasis monitoring are of great importance to favorable prognosis. Although conventional diagnostic methods, such as breast X-ray mammography and image positioning biopsy, are accurate, they could cause radioactive or invasive damage to patients. Liquid biopsy as a noninvasive method is convenient for repeated sampling in clinical cancer prognostic, metastatic evaluation, and relapse monitoring. MicroRNAs encased in exosomes circulating in biofluids are promising candidate cancer biomarkers because of their cancer-specific expression profiles. Here, we report an in situ detection of microRNA-1246 (miR-1246) in human plasma exosomes as breast cancer biomarker by a nucleic acid functionalized Au nanoflare probe. Needing neither time-consuming and costly isolation of exosomes from the plasma sample nor transfection means, the Au nanoflare probe can directly enter the plasma exosomes to generate fluorescent signal quantitatively by specifically targeting miR-1246. Only 40 μL of plasma is needed to incubate 4 h with the probe, giving signal sensitive enough to distinguish samples of breast cancer to normal control. Using plasma miR-1246 level detected by our assay as a marker, we differentiated 46 breast cancer patients from 28 healthy controls with 100% sensitivity and 92.9% specificity at the best cutoff. This simple, accurate, sensitive, and cost-effective liquid biopsy by the Au nanoflare probe is potent to be developed as a noninvasive breast cancer diagnostic assay for clinical adaption.

Microwave Sensors for Breast Cancer Detection

Abstract: Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.

Digital Breast Tomosynthesis and Synthetic 2D Mammography versus Digital Mammography: Evaluation in a Population-based Screening Program

Abstract: Our study showed the rate of screen-detected breast cancer was substantially higher in women who underwent digital breast tomosynthesis and synthetic mammography screening than in women who underwe...

An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network

Abstract: Computer-aided diagnosis (CAD) offers assistance to radiologists in the interpretation of medical images. A CAD system learns the nature of different tissues and uses this information to diagnose abnormalities. In this paper, we propose a CAD system for breast cancer diagnosis via deep belief network (DBN) that automatically detects breast mass regions and recognizes them as normal, benign, or malignant. In this study, we utilize a standard digital database of mammography to evaluate our proposed DBN-based CAD system for breast cancer diagnosis. We utilize two techniques of ROI extraction: multiple mass regions of interest (ROIs) and whole mass ROIs. In the former technique, we randomly extract four ROIs with a size of 32 × 32 pixels from a detected mass. In the latter technique, the whole detected breast mass is utilized. A total of 347 statistical features are extracted for both techniques to train and test our proposed CAD system. For classification, we utilized linear discriminant analysis, quadratic discriminant analysis, and neural network classifiers as the conventional techniques. Finally, we employed DBN and compared the results. Our results demonstrate that the proposed DBN outperforms the conventional classifiers. The overall accuracies of a DBN are 92.86% and 90.84% for the two ROI techniques, respectively. The presented work shows the feasibility of a DBN-based CAD system for use as in the field of breast cancer diagnosis.

Performance of breast cancer screening using digital breast tomosynthesis: results from the prospective population-based Oslo Tomosynthesis Screening Trial.

Abstract: Digital breast tomosynthesis (DBT) has the potential to overcome limitations of conventional mammography This study investigated the effects of addition of DBT on interval and detected cancers in population-based screening Oslo Tomosynthesis Screening Trial (OTST) was a prospective, independent double-reading trial inviting women 50–69 years biennially, comparing full-field digital mammography (FFDM) plus DBT with FFDM alone Performance indicators and characteristics of screen-detected and interval cancers were compared with two previous FFDM rounds 24,301 consenting women underwent FFDM + DBT screening over a 2-year period Results were compared with 59,877 FFDM examinations during prior rounds Addition of DBT resulted in a non-significant increase in sensitivity (762%, 378/496, vs 808%, 227/281, p = 0151) and a significant increase in specificity (964%, 57229/59381 vs 975%, 23427/24020, p < 001) Number of recalls per screen-detected cancer decreased from 67 (2530/378) to 36 (820/227) with DBT (p < 001) Cancer detection per 1000 women screened increased (63, 378/59877, vs 93, 227/24301, p < 001) Interval cancer rate per 1000 screens for FFDM + DBT remained similar to previous FFDM rounds (21, 51/24301 vs 20, 118/59877, p = 0734) Interval cancers post-DBT were comparable to prior rounds but significantly different in size, grade, and node status from cancers detected only using DBT 396% (19/48) of interval cancers had positive nodes compared with only 39% (2/51) of additional DBT-only-detected cancers DBT-supplemented screening resulted in significant increases in screen-detected cancers and specificity However, no significant change was observed in the rate, size, node status, or grade of interval cancers ClinicalTrialsgov: NCT01248546

Contrast-Enhanced Spectral Mammography in Women With Intermediate Breast Cancer Risk and Dense Breasts.

Abstract: OBJECTIVE. The purpose of this study was to compare the diagnostic performance of contrast-enhanced spectral mammography (CESM) and ultrasound with that of standard digital mammography for breast c...

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Abstract: Purpose: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy. Experimental Design: Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy. Results: Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset. Conclusions: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls. Clin Cancer Res; 1–8. ©2018 AACR.

Contrast Enhanced Spectral Mammography: A Review

Abstract: Contrast-enhanced spectral mammography (CESM) provides low-energy 2D mammographic images comparable to standard digital mammography and a post-contrast recombined image to assess tumor neovascularity similar to magnetic resonance imaging (MRI). The utilization of CESM in the United States is currently low but could increase rapidly given many potential indications for clinical use. This article discusses historical background and literature review of indications and diagnostic accuracy of CESM to date. CESM is a growing technique for breast cancer detection and diagnosis that has levels of sensitivity and specificity on par with contrast-enhanced breast MRI. Because of its similar performance and ease of implementation, CESM is being adopted for multiple indications previously reserved for MRI, such as problem-solving, disease extent in newly diagnosed patients, and evaluating the treatment response of neoadjuvant chemotherapy.

The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women.

Abstract: This study aimed to evaluate automated breast ultrasound (ABUS) compared to hand-held traditional ultrasound (HHUS) in the visualisation and BIRADS characterisation of breast lesions. From January 2016 to January 2017, 1,886 women with breast density category C or D (aged 48.6±10.8 years) were recruited. All participants underwent ABUS and HHUS examination; a subcohort of 1,665 women also underwent a mammography. The overall agreement between HHUS and ABUS was 99.8 %; kappa=0.994, p<0.0001. Two cases were graded as BI-RADS 1 in HHUS, but were graded as BIRADS 4 in ABUS; biopsy revealed a radial scar. Three carcinomas were graded as BI-RADS 2 in mammography but BI-RADS 4 in ABUS; two additional carcinomas were graded as BI-RADS 2 in mammography but BI-RADS 5 in ABUS. Two carcinomas, appearing as a well-circumscribed mass or developing asymmetry in mammography, were graded as BI-RADS 4 in mammography but BI-RADS 5 in ABUS. ABUS could be successfully used in the visualisation and characterisation of breast lesions. ABUS seemed to outperform HHUS in the detection of architectural distortion on the coronal plane and can supplement mammography in the detection of non-calcified carcinomas in women with dense breasts. • The new generation of ABUS yields comparable results to HHUS. • ABUS seems superior to HHUS in detecting architectural distortions. • In dense breasts, supplemental ABUS to mammography detects additional cancers.

Digital Mammography versus Digital Mammography Plus Tomosynthesis for Breast Cancer Screening: The Reggio Emilia Tomosynthesis Randomized Trial.

Abstract: Tomosynthesis plus digital mammography detects 90% more cancers than digital mammography alone, with similar recall rate. This higher detection may have a beneficial impact on cancer prognosis.

Breast ultrasound: recommendations for information to women and referring physicians by the European Society of Breast Imaging.

Abstract: This article summarises the information that should be provided to women and referring physicians about breast ultrasound (US). After explaining the physical principles, technical procedure and safety of US, information is given about its ability to make a correct diagnosis, depending on the setting in which it is applied. The following definite indications for breast US in female subjects are proposed: palpable lump; axillary adenopathy; first diagnostic approach for clinical abnormalities under 40 and in pregnant or lactating women; suspicious abnormalities at mammography or magnetic resonance imaging (MRI); suspicious nipple discharge; recent nipple inversion; skin retraction; breast inflammation; abnormalities in the area of the surgical scar after breast conserving surgery or mastectomy; abnormalities in the presence of breast implants; screening high-risk women, especially when MRI is not performed; loco-regional staging of a known breast cancer, when MRI is not performed; guidance for percutaneous interventions (needle biopsy, pre-surgical localisation, fluid collection drainage); monitoring patients with breast cancer receiving neo-adjuvant therapy, when MRI is not performed. Possible indications such as supplemental screening after mammography for women aged 40–74 with dense breasts are also listed. Moreover, inappropriate indications include screening for breast cancer as a stand-alone alternative to mammography. The structure and organisation of the breast US report and of classification systems such as the BI-RADS and consequent management recommendations are illustrated. Information about additional or new US technologies (colour-Doppler, elastography, and automated whole breast US) is also provided. Finally, five frequently asked questions are answered. • US is an established tool for suspected cancers at all ages and also the method of choice under 40. • For US-visible suspicious lesions, US-guided biopsy is preferred, even for palpable findings. • High-risk women can be screened with US, especially when MRI cannot be performed. • Supplemental US increases cancer detection but also false positives, biopsy rate and follow-up exams. • Breast US is inappropriate as a stand-alone screening method.

Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks

Abstract: Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to perform biopsies, ensuring no missing diagnosis. The convolutional neural network (CNN) has succeeded in a lot of image classification challenges during the recent years. In this paper, we proposed an approach of mammogram and tomosynthesis classification based on CNNs. We had acquired more than 3000 mammograms and tomosynthesis data with approval from an institutional review board at the University of Kentucky. Different models of CNNs were built to classify both the 2-D mammograms and 3-D tomosynthesis, and every classifier was assessed with respect to truth-values generated by histology results from the biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our outcomes demonstrated that CNN-based models we had built and optimized utilizing transfer learning and data augmentation have good potential for automatic breast cancer detection based on the mammograms and tomosynthesis data.

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Abstract: Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.

Automated and real-time segmentation of suspicious breast masses using convolutional neural network

Abstract: In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13-55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm.

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

Abstract: We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients' age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.

Computational mammography using deep neural networks

Abstract: Automatic tissue classification from medical images is an important step in pathology detection and diagnosis. Here, we deal with mammography images and present a novel supervised deep learning-based framework for region classification into semantically coherent tissues. The proposed method uses Convolutional Neural Network (CNN) to learn discriminative features automatically. We overcome the difficulty involved in a medium-size database by training the CNN in an overlapping patch-wise manner. In order to accelerate the pixel-wise automatic class prediction, we use convolutional layers instead of the classical fully connected layers. This approach results in significantly faster computation, while preserving the classification accuracy. The proposed method was tested on annotated mammography images and demonstrates promising image segmentation and tissue classification results.