scholarly journals A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6116
Author(s):  
Muhammad Firoz Mridha ◽  
Md. Abdul Hamid ◽  
Muhammad Mostafa Monowar ◽  
Ashfia Jannat Keya ◽  
Abu Quwsar Ohi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Performance Metrics ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Imaging Modalities ◽  
Research Directions ◽  
Comprehensive Survey ◽  
Processing Techniques

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

Simple and fast isolation of circulating exosomes with chitosan modified shuttle flow microchip towards breast cancer diagnosis

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Wenwen Chen ◽  
Rongkai Cao ◽  
Wentao Su ◽  
xu zhang ◽  
Yuhai Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Treatment ◽  
Cancer Diagnosis ◽  
Biological Samples ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Early Stage Cancer ◽  
Tumor Prognosis ◽  
Huge Challenge

Tumor-derived exosomes have been recognized as promising biomarkers for early-stage cancer diagnosis, tumor prognosis monitoring and individual medical treatment. However, separating exosomes from trace biological samples is a huge challenge...

Density-Assessment for Breast Cancer Diagnosis Using Deep Learning on Mammographic Image: A Brief Study

2021 ◽  
pp. 313-320
Author(s):  
Shaila Chugh ◽  
Sachin Goyal ◽  
Anjana Pandey ◽  
Sunil Joshi ◽  
Mukesh Azad
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Mammographic Image ◽  
Density Assessment

scholarly journals A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lian Zou ◽  
Shaode Yu ◽  
Tiebao Meng ◽  
Zhicheng Zhang ◽  
Xiaokun Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Learning Tools ◽  
Pros And Cons ◽  
Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

scholarly journals Non-Invasive Biomarkers for Early Detection of Breast Cancer

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2767
Author(s):  
Jiawei Li ◽  
Xin Guan ◽  
Zhimin Fan ◽  
Lai-Ming Ching ◽  
Yan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
False Negative ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Biomarkers ◽  
Circulating Micrornas ◽  
Non Invasive ◽  
Common Cancer ◽  
Volatile Organic

Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of “omics” strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Mammography use in breast cancer survivors: An administrative claims study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6531-6531 ◽  
Author(s):  
Kathryn Jean Ruddy ◽  
Lindsey R. Sangaralingham ◽  
Heather B. Neuman ◽  
Caprice Christian Greenberg ◽  
Rachel A. Freedman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Survivors ◽  
Cancer Diagnosis ◽  
Breast Cancer Survivors ◽  
Breast Tissue ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
First Year ◽  
Administrative Claims ◽  
Mammography Use

6531 Background: Annual mammography is recommended to screen residual breast tissue for new cancers and recurrent disease after treatment for early stage breast cancer. This study aimed to assess mammography rates over time in breast cancer survivors. Methods: We used administrative claims data from a large U.S. commercial insurance database, OptumLabs, to retrospectively identify privately- and Medicare Advantage-insured women with operable breast cancer who had residual breast tissue after definitive breast surgery between 2006 and 2015. We required coverage for at least 13 months following surgery. For each subsequent 13-month time period, we only included women without a loss of coverage, bilateral mastectomy, metastatic breast cancer diagnosis, or non-breast cancer diagnosis. We calculated the proportion of patients who had a mammogram during each 13-month period following breast surgery. We used multivariable logistic regression to test for factors associated with mammography in the first 13 months. Results: The cohort included 26,011 women followed for a median of 2.9 years (IQR 1.9-4.6) after surgery; 63.1% were less than 65 years of age, and 74.4% were white. In their first year of follow-up, 86% underwent mammography, but by year 7, this decreased to 73%. Fewer than 1% underwent MRI instead of mammography. In multivariable analysis, mammograms were less likely during the first year after surgery among women aged < 50 years (odds ratio [OR], 0.7; 95% confidence interval [CI], 0.6 to 0.8), African Americans (OR, 0.7; 95% CI, 0.7 to 0.8), patients who underwent mastectomy (OR, 0.7; 95% CI, 0.6 to 0.7), and patients residing in the Western part of the country (OR, 0.9; 95% CI, 0.7 to 0.9). Those with 1-2 comorbidities were more likely (OR, 1.1; 95% CI 1.1-1.2) than those with none to have a mammogram during that period. Mammography use did not differ significantly by year of diagnosis (2006-2015). Conclusions: Even in an insured cohort, a substantial proportion of breast cancer survivors do not undergo annual surveillance mammography. Mammography use falls as the time from the early stage breast cancer diagnosis increases. Understanding factors associated with lack of mammographic screening may help improve survivorship care.

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