Electro-impedance mammograms for automatic breast cancer screening: First insights on Mexican patients

2021 ◽  
pp. 1-13
Author(s):  
Rosario Lissiet Romero Coripuna ◽  
Delia Irazú Hernández Farías ◽  
Blanca Olivia Murillo Ortiz ◽  
Teodoro Córdova Fraga
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Data Augmentation ◽  
Screening Tools ◽  
Health Concern ◽  
Important Health ◽  
Invasive Method ◽  
First Time ◽  
Breast Tissues ◽  
Mexican Patients

Breast cancer is a very important health concern around the world. Early detection of such a disease increases the chances of survival. Among the available screening tools, there is the Electro-Impedance Mammography (EIM), which is a novel and less invasive method that captures the potential difference stored in breast tissues under the assumption that electrical properties among normal and pathologically altered tissues are different. In this paper, we address breast cancer detection as a multi-class problem aiming to determine the corresponding label in terms of the Breast Imaging Electrical Impedance classification system, the standard used by physicians for interpreting an EIM mammogram. For experimental purposes, for the first time in the literature, we took advantage of a dataset comprising EIM of Mexican patients. Aiming to establish a baseline for this task, traditional supervised learning methods were used together with two different feature extraction techniques: raw pixel data and transfer learning. Besides, data augmentation was exploited for compensating data imbalance. Different experimental settings were evaluated reaching classification rates over 0.85 in F-score. KNN emerges as a very promising classifier for addressing this task. The obtained results allow us to validate the usefulness of traditional methods for classifying electro-impedance mammograms.

scholarly journals Oncogenic UBE3C promotes breast cancer progression by activating Wnt/β-catenin signaling

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chen Hang ◽  
Shanojie Zhao ◽  
Tiejun Wang ◽  
Yan Zhang
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Nuclear Accumulation ◽  
Migration And Invasion ◽  
Ubiquitin Protein Ligase ◽  
Novel Target ◽  
First Time ◽  
Breast Tissues

Abstract Background Breast cancer (BrCa) is the most common female malignancy worldwide and has the highest morbidity among all cancers in females. Unfortunately, the mechanisms of BrCa growth and metastasis, which lead to a poor prognosis in BrCa patients, have not been well characterized. Methods Immunohistochemistry (IHC) was performed on a BrCa tissue microarray (TMA) containing 80 samples to evaluate ubiquitin protein ligase E3C (UBE3C) expression. In addition, a series of cellular experiments were conducted to reveal the role of UBE3C in BrCa. Results In this research, we identified UBE3C as an oncogenic factor in BrCa growth and metastasis for the first time. UBE3C expression was upregulated in BrCa tissues compared with adjacent breast tissues. BrCa patients with high nuclear UBE3C expression in tumors showed remarkably worse overall survival (OS) than those with low nuclear expression. Knockdown of UBE3C expression in MCF-7 and MDA-MB-453 BrCa cells inhibited cell proliferation, migration and invasion in vitro, while overexpression of UBE3C in these cells exerted the opposite effects. Moreover, UBE3C promoted β-catenin nuclear accumulation, leading to the activation of the Wnt/β-catenin signaling pathway in BrCa cells. Conclusion Collectively, these results imply that UBE3C plays crucial roles in BrCa development and progression and that UBE3C may be a novel target for the prevention and treatment of BrCa.

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

scholarly journals Study of role of digital breast tomosynthesis over digital mammography in the assessment of BIRADS 3 breast lesions

2019 ◽  
Vol 50 (1) ◽  
Author(s):  
Engy A. Ali ◽  
Lamiaa Adel
Keyword(s):  
Breast Cancer ◽  
Digital Mammography ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Standard Of Care ◽  
Patients Âgés ◽  
Ductal Hyperplasia ◽  
Important Health ◽  
Important Health Issue ◽  
A Prospective Study

Abstract Background Breast cancer is the most common malignancy in women and thus, screening has become an important health issue. Although mammography remains the standard of care for breast cancer screening and diagnosis (with biopsy), tomosynthesis (3D DBT) allows the separation of overlapping structures seen on 2D mammography and thus enables better depiction of masses or asymmetries. Results A prospective study for mammographic cases referred to our radiology unit included 60 lesions detected in 59 patients that were performed during the period from January 2016 to September 2017. Patients’ ages ranged from 26 to 72 years with mean age 51 ± 12 SD. Sixty percent of breast imaging-reporting and data system (BIRADS) 3 lesions detected by 2D digital mammography (36/60) changed their category after 3D DBT, 40% (24/60) digital mammography noticed lesions did not change their BIRADS after 3D DBT. Twenty-nine BIRADS 3 lesions out of the 60 were downgraded to BIRADS 1and 2, while 7 BIRADS 3 lesions out of the 60 were upgraded to BIRADS 4 and 5 which were all biopsied. Six out of the 7 lesions were pathologically proven ducal carcinoma and 1 out of 7 pathologically proven to be atypical ductal hyperplasia. Conclusion 3D DBT significantly reduced the need for additional mammographic views and frequent follow-up studies as it gave better characterization for all BIRADS 3 lesions.

Comparison of Call-Back Rates between Digital Mammography and Digital Breast Tomosynthesis

2019 ◽  
Vol 85 (8) ◽  
pp. 855-857
Author(s):  
Anthony M. Scott ◽  
Madison G. Lashley ◽  
Nicholas B. Drury ◽  
Paul S. Dale
Keyword(s):  
Breast Cancer ◽  
Ultrasound Imaging ◽  
Digital Mammography ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Data System ◽  
Screening Tools ◽  
Radiographic Imaging ◽  
Breast Cancers ◽  
Breast Tomosynthesis

The effect of mammographic screening on the natural history and evolution of breast cancer treatment cannot be overstated; however, despite intensive and resource consuming screening, advanced breast cancer is still diagnosed frequently. The development of three-dimensional mammography or digital breast tomosynthesis (DBT) has already demonstrated greater sensitivity in the diagnosis of breast pathology and effectiveness in identifying early breast cancers. In addition to being a more sensitive screening tool, other studies indicate DBT has a lower call-back rate when compared with traditional DM. This study compares call-back rates between these two screening tools. A single institution, retrospective review was conducted of almost 20,000 patient records who underwent digital mammography or DBTin the years 2016 to 2018. These charts were analyzed for documentation of imaging type, Breast Imaging Reporting and Data System 0 status, call-back status, and type of further imaging that was required. Charts for 19,863 patients were reviewed, 17,899 digital mammography examinations were conducted compared with 11,331 DBT examinations resulting in 1,066 and 689 Breast Imaging Reporting and Data System 0 studies, respectively. Of the DM call-backs, 82.08 per cent were recommended for additional radiographic imaging and 17.82 per cent for ultrasound imaging. In the DBT group, only 39.77 per cent of callbacks were recommended for additional radiographic imaging and 60.09 per cent for ultrasound imaging. Our data suggest that DBT results in less call-back for additional mammographic images as compared with digital mammography. DBT may offer benefits over DM, including less imaging before biopsy, less time before biopsy, quicker diagnosis, and improved patient satisfaction.

Optical Spectroscopy

1988 ◽  
Vol 29 (5) ◽  
pp. 497-503 ◽  
Author(s):  
R. L. Egan ◽  
P. D. Dolan
Keyword(s):  
Breast Cancer ◽  
Optical Spectroscopy ◽  
Breast Imaging ◽  
Spectroscopic Data ◽  
Non Invasive ◽  
Dense Breasts ◽  
Sensitivity Range ◽  
Imaging Approach ◽  
High Assurance ◽  
Breast Tissues

Non-invasive optical spectroscopy consistently delineates compositional and physiologic properties of breast tissues serving as a premammography risk marker for cancer or yielding a high assurance of no such risk. We believe this new non-imaging approach depends on biochemistry of tissues rather than on the macroscopic physical properties involved with most breast imaging modalities. After establishing the procedure as inexpensive, physician independent, simple, requiring only a few minutes and appealing to women, it was carried out in two institutions on 1739 women referred for routine mammography. Of 166 breast biopsies on these women 77 were cancer by histology. An automated computerized analysis of the spectroscopic data yielded a sensitivity of 87 per cent, a specificity of 74 per cent and a negative predictive value of 99 per cent. Optical spectroscopy shows promise in identifying women at a higher risk for developing cancer, cases of non-infiltration carcinomas where dense breasts limit mammographic detection, and even clustered calcifications not associated with a mass. The relative risk of breast cancer was 16.5 times as great with a positive spectroscopic value at a sensitivity range of 87 per cent. Placement of 87 per cent of all breast cancer cases in a subset of 28.7 per cent of all women will yield a population of women in whom mammography will be approximately four times as efficient.

scholarly journals Self-Reported Frailty Concerns in Older Breast Cancer Survivors During the COVID-19 Pandemic

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 960-960
Author(s):  
Adele Crouch ◽  
Diane Von Ah
Keyword(s):  
Breast Cancer ◽  
Cancer Survivors ◽  
Breast Cancer Survivors ◽  
Cross Sectional Study ◽  
Health Concern ◽  
Cross Sectional ◽  
Tilburg Frailty Indicator ◽  
The Social ◽  
Important Health

Abstract Frailty among older adults is common, especially those who have undergone breast cancer treatment; however, we do not know how frailty among this group presented during the COVID-19 pandemic. The purpose of this descriptive, cross-sectional study was to examine self-reported frailty among older breast cancer survivors (BCS) during the pandemic. This IRB-approved study recruited BCS who were at least 1-year post-treatment and 60 years of age or older, via online advertisements (e.g., Dr. Susan Love Foundation). BCS completed demographic and Tilburg Frailty Indicator (TFI) RedCap questionnaires from 11/2020 to 05/2021. The TFI, is a 15-item measure with 3 sub-scales with published cut points indicating frailty: total (5), physical (3), psychological (2), and social (2). Descriptive statistics were used. Older BCS (n=203) who were on average 65.5 (SD=4.7) years of age, white (93.6%; n=190) and had stage II breast cancer at diagnosis (39.9%; n=81) participated. The average total (M=5.4, SD=2.5) and physical (M=3.2, SD=1.5) frailty scores were above the threshold for frailty. Overall, 58.6% (n=119) and 63.1% (n=128) scored at or above the threshold on the total and physical sub-scales, respectively. In addition,78.8% (n=160) responded that they ‘missed having people around’ on the social frailty sub-scale. Research has shown that higher TFI scores (more frailty) are associated with increased healthcare utilization, poorer quality of life, and even mortality. Thus, frailty among older BCS is an important health concern within the context of the pandemic. Further research is needed to understand the lasting effects of self-reported frailty for BCS including COVID-19 survivors.

Breast cancer associated with pregnancy: a clinical case

GYNECOLOGY ◽  
2018 ◽  
Vol 20 (1) ◽  
pp. 102-108
Author(s):  
Yu E Dobrokhotova ◽  
S E Arakelov ◽  
S Zh Danelyan ◽  
E I Borovkova ◽  
A E Zykov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Radiation Therapy ◽  
Clinical Case ◽  
First Trimester ◽  
First Year ◽  
Total Mastectomy ◽  
First Time

Associated with pregnancy is breast cancer, which was first detected during pregnancy, during the first year after childbirth or at any time against lactation. Diagnosis of the disease in the first trimester is an indication for abortion. The detection of the disease after 20 weeks and the desire of the woman to maintain pregnancy is the basis for conducting a total mastectomy followed by polychemotherapy with doxorubicin with cyclophosphamide or with fluorouracil. Radiation therapy during pregnancy is not applied. The timing and method of delivery are determined individually and depend on the stage of the process and the period of pregnancy, when it was identified. A clinical case of a patient with edematous-infiltrative form of breast cancer of the IV stage, diagnosed for the first time in 22 weeks of pregnancy, is presented.

scholarly journals Breast Cancer Cell Re-Dissemination from Lung Metastases—A Mechanism for Enhancing Metastatic Burden

2021 ◽  
Vol 10 (11) ◽  
pp. 2340
Author(s):  
Lucia Borriello ◽  
John Condeelis ◽  
David Entenberg ◽  
Maja H. Oktay
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Metastatic Disease ◽  
Breast Cancer Cells ◽  
Lung Metastases ◽  
Primary Tumors ◽  
Metastatic Burden ◽  
First Time ◽  
Do So

Although metastatic disease is the primary cause of mortality in cancer patients, the mechanisms leading to overwhelming metastatic burden are still incompletely understood. Metastases are the endpoint of a series of multi-step events involving cancer cell intravasation, dissemination to distant organs, and outgrowth to metastatic colonies. Here we show, for the first-time, that breast cancer cells do not solely disseminate to distant organs from primary tumors and metastatic nodules in the lymph nodes, but also do so from lung metastases. Thus, our findings indicate that metastatic dissemination could continue even after the removal of the primary tumor. Provided that the re-disseminated cancer cells initiate growth upon arrival to distant sites, cancer cell re-dissemination from metastatic foci could be one of the crucial mechanisms leading to overt metastases and patient demise. Therefore, the development of new therapeutic strategies to block cancer cell re-dissemination would be crucial to improving survival of patients with metastatic disease.

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