Jean-Claude Ferrandez^1,*, Pierre-Henri Ganchou², Serge Theys³, Maria Torres-Lacomba⁴, Daniel Serin¹

Oncologie, Vol.24, No.1, pp. 25-33, 2022, DOI:10.32604/oncologie.2022.018110

Abstract Upper limb lympedema is a frequent consequence of breast cancer treatment. The International Society of Lymphology recommends physical therapy for lymphedema management. This treatment includes a combination of manual lymphatic drainage and high-compression bandaging. Actually, the effectiveness of manual lymphatic drainage remains an area of controversy, especially because of the many different techniques called “manual lymphatic drainage” since 1930. The purpose of this review was to emphasise the efficacy of these different techniques according to physiological data and evidence-based practice. To improve the manual lymphatic drainage efficiency, the pressure, sometimes important, should be tailored to each edema and the techniques… More >

Romany F. Mansour^1,*, Maha M. Althobaiti²

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3945-3959, 2022, DOI:10.32604/cmc.2022.026515

Abstract Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies–machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others–to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier… More >

V. Ulagamuthalvi¹, G. Kulanthaivel^2,*, A. Balasundaram³, Arun Kumar Sivaraman⁴

Computer Systems Science and Engineering, Vol.43, No.1, pp. 275-289, 2022, DOI:10.32604/csse.2022.023737

Abstract One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters… More >

JUNXIA LIU¹, KE PANG², FEI HE^2,*

BIOCELL, Vol.46, No.7, pp. 1661-1673, 2022, DOI:10.32604/biocell.2022.018023

Abstract Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide. Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients. In the study, we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes. Subsequently, the prognostic immune-related gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis. Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance. And we… More >

Sharifa Khalid Alduraibi^*

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1321-1334, 2022, DOI:10.32604/iasc.2022.025021

Abstract This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions. First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase the classification accuracy at the… More >

Majdy Mohamed Eltayeb Eltahir^1,*, Tarig Mohammed Ahmed^2,3

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1257-1272, 2022, DOI:10.32604/csse.2022.022942

Abstract In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing life expectancy, increasing urbanization and embracing Western lifestyles, the high prevalence of this cancer is noted in the developed world. This paper aims to develop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors to make the right decision in diagnosing breast cancer patients. The proposed model is based on three datasets to develop three sub-models. Each sub-model works independently. The final… More >

NING LI^1,#, YANG CHEN^2,#, YONGJIE YANG^3,4, SHUHAN LYU¹, YUE PAN^1,5,*

BIOCELL, Vol.46, No.6, pp. 1473-1482, 2022, DOI:10.32604/biocell.2022.018278

Abstract Breast cancer is a highly aggressive cancer in females. Metastasis is a major obstacle to the efficient and successful treatment of breast cancer. Cetyltrimethylammonium bromide (CTAB) has anti-tumor effects on a variety of tumors. We showed that CTAB inhibits the metastasis of breast cancer to the lungs both in vitro and in vivo. Epithelial-mesenchymal transition (EMT) is thought to be one of the major processes mediating breast cancer metastasis. We found that CTAB suppressed EMT and regulated the levels of the classical EMT markers E-cadherin, N-cadherin, vimentin, Snail and Twist1. Moreover, as a candidate anti-tumor agent, CTAB showed primary safety… More >

Asmaa E. E. Ali^*, Mofreh Mohamed Salem, Mahmoud Badway, Ali I. EL Desouky

Journal on Artificial Intelligence, Vol.3, No.3, pp. 81-96, 2021, DOI:10.32604/jai.2021.022433

Abstract Breast cancer (BrC) is now the world’s leading cause of death for women. Early detection and effective treatment of this disease are the only rescues to reduce BrC mortality. The prediction of BrC diseases is very difficult because it is not an individual disease but a mixture of various diseases. Many researchers have used different techniques such as classification, Machine Learning (ML), and Deep Learning (DL) of the prediction of the breast tumor into Benign and Malignant. However, still there is a scope to introduce appropriate techniques for developing and implementing a more effective diagnosis system. This paper proposes a… More >

José Escorcia-Gutierrez^1,*, Romany F. Mansour², Kelvin Beleño³, Javier Jiménez-Cabas⁴, Meglys Pérez¹, Natasha Madera¹, Kevin Velasquez¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4221-4235, 2022, DOI:10.32604/cmc.2022.022322

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… More >

LU YANG^1,#, YUN LIU^1,#, BOKE ZHANG², MENGSI YU³, FEN HUANG¹, YANG WEN¹, JIANGZHENG ZENG¹, YANDA LU¹, CHANGCHENG YANG¹

BIOCELL, Vol.46, No.5, pp. 1215-1243, 2022, DOI:10.32604/biocell.2022.018221

Abstract The aim of this study was to reveal genes associated with breast cancer metastasis, to investigate their intrinsic relationship with immune cell infiltration in the tumor microenvironment, and to screen for prognostic biomarkers. Gene expression data of breast cancer patients and their metastases were downloaded from the GEO, TCGA database. R language package was used to screen for differentially expressed genes, enrichment analysis of genes, PPI network construction, and also to elucidate key genes for diagnostic and prognostic survival. Spearman’s r correlation was used to analyze the correlation between key genes and infiltrating immune cells. We screened 25 hub genes,… More >