S. Sathiya Devi¹, S. Vidivelli^2,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3049-3066, 2022, DOI:10.32604/cmc.2022.019917 - 27 September 2021

Abstract Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and… More >

Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi^*

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1273-1286, 2022, DOI:10.32604/iasc.2022.020662 - 22 September 2021

Abstract In this paper we propose a computerized breast cancer detection and breast masses classification system utilizing mammograms. The motivation of the proposed method is to detect breast cancer tumors in early stages with more accuracy and less negative false cases. Our proposed method utilizes clustering of different features by segmenting the breast mammogram and then extracts deep features using the presented Convolution Neural Network (CNN). The extracted features are then combined with subjective features such as shape, texture and density. The combined features are then utilized by the Extreme Learning Machine Clustering (ELMC) algorithm to… More >

G. Jayandhi^1,*, J.S. Leena Jasmine², S. Mary Joans²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 491-503, 2022, DOI:10.32604/csse.2022.016376 - 09 September 2021

Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces… More >

Naglaa F. Soliman^1,2, Naglaa S. Ali², Mahmoud I. Aly^2,3, Abeer D. Algarni^1,*, Walid El-Shafai⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1315-1334, 2022, DOI:10.32604/cmc.2022.017001 - 07 September 2021

Abstract Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main… More >

MOTAWA E. EL-HOUSEINI¹, MOSTAFA S. ARAFAT², AHMED M. EL-HUSSEINY³, ISLAM M. KASEM², MAHMOUD M. KAMEL⁴, AHMED H. EL-HABASHY⁵, MEDHAT M. KHAFAGY⁶, ENAS M. RADWAN⁴, MAHA H. HELAL⁷, MONA S. ABDELLATEIF^1,*

Oncology Research, Vol.29, No.5, pp. 319-329, 2021, DOI:10.32604/or.2022.025249 - 10 October 2022

Abstract Immunotherapy becomes a promising line of treatment for breast cancer (BC) however, its success rate is still limited. Methods: The study was designed to optimize the condition for producing an effective dendritic cell (DCs) based immunotherapy by using DCs and T lymphocytes together with tumor-infiltrating lymphocytes (TILs) and tumor-infiltrating DCs (TIDCs), treated with anti-PD1 and anti-CTLA4 monoclonal antibodies. This mixture of immune cells was co-cultured with autologous breast cancer cells (BCCs) isolated from 26 BC females. Results: There was a significant upregulation of CD86 and CD83 on DCs (P = 0.001 and 0.017, respectively), similarly upregulation of… More >

LIMIN ZHOU¹, LIANBO ZHANG², XIN GUAN³, YI DONG¹, TAO LIU^1,*

Oncology Research, Vol.29, No.4, pp. 251-262, 2021, DOI:10.32604/or.2022.03582 - 31 August 2022

Abstract Long noncoding RNA PPP1R14B antisense RNA 1 (PPP1R14B-AS1) has emerged as a critical modulator of liver cancer and lung adenocarcinoma progression. However, the functional importance and biological relevance of PPP1R14B-AS1 in breast cancer remain unclear. Therefore, this study was designed to detect PPP1R14B-AS1 levels in breast cancer cells using qRT–PCR and elucidate the influence of PPP1R14B-AS1 on aggressive phenotypes. Furthermore, molecular events mediating the action of PPP1R14B-AS1 were characterized in detail. Functional experiments addressed the impacts of PPP1R14B-AS1 knockdown on breast cancer cells. In this study, PPP1R14B-AS1 was found to be overexpressed in breast cancer,… More >

YI DONG¹, LIANBO ZHANG², XIN GUAN³, TAO LIU¹, LIMIN ZHOU^1,*

Oncology Research, Vol.29, No.4, pp. 291-303, 2021, DOI:10.32604/or.2022.025172 - 31 August 2022

Abstract Increasing numbers of long noncoding RNAs (lncRNAs) are implicated in breast cancer oncogenicity. However, the contribution of LINC02568 toward breast cancer progression remains unclear and requires further investigation. Herein, we evaluated LINC02568 expression in breast cancer and clarified its effect on disease malignancy. We also investigated the mechanisms underlying the pro-oncogenic role of LINC02568. Consequently, LINC02568 was upregulated in breast cancer samples, with a notable association with worse overall survival. Functionally, depleted LINC02568 suppressed cell proliferation, colony formation, and metastasis, whereas LINC02568 overexpression exerted the opposite effects. Our mechanistic investigations suggested that LINC02568 was physically More >

TAO LIU¹, LIMIN ZHOU¹, LIANBO ZHANG², XIN GUAN³, YI DONG^1,*

Oncology Research, Vol.29, No.3, pp. 189-200, 2021, DOI:10.32604/or.2022.03573 - 01 August 2022

Abstract Many studies have illustrated the significance of long noncoding RNAs in oncogenesis and promotion of breast cancer (BC). However, the biological roles of CCDC183 antisense RNA 1 (CCDC183-AS1) in BC have rarely been characterized. Thus, we explored whether CCDC183-AS1 is involved in the malignancy of BC and elucidated the possible underlying mechanisms. Our data confirmed elevated CCDC183-AS1 expression in BC, which was associated with poor clinical outcomes. Functionally, knocking down CCDC183-AS1 hampered cell proliferation, colony formation, migration, and invasion in BC. Additionally, the absence of CCDC183-AS1 restrained tumor growth in vivo. Mechanistically, CCDC183-AS1 executed as a 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 - 25 January 2022

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… More >

Yuqin Xie, Shuang Deng, Qian Deng, Jiudong Xu

Oncologie, Vol.23, No.4, pp. 609-609, 2021, DOI:10.32604/oncologie.2021.020379 - 31 December 2021

Abstract This article has no abstract. More >