Heyam H. Al-Baity^*, Nourah Al-Mutlaq

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3089-3106, 2021, DOI:10.32604/cmc.2021.015291

Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest… More >

Ishu Anand¹, Himani Negi¹, Deepika Kumar¹, Mamta Mittal², Tai-hoon Kim^3,*, Sudipta Roy⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229

Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.… More >

Shahan Yamin Siddiqui^1,2, Iftikhar Naseer³, Muhammad Adnan Khan⁴, Muhammad Faheem Mushtaq⁵, Rizwan Ali Naqvi^6,*, Dildar Hussain⁷, Amir Haider⁸

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1033-1049, 2021, DOI:10.32604/cmc.2021.013952

Abstract Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis (CAD) systems. This study explores… More >

Mavra Mehmood¹, Ember Ayub¹, Fahad Ahmad^1,6,*, Madallah Alruwaili², Ziyad A. Alrowaili³, Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem⁴, Tahir Alyas⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774

Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

Diyar Qader Zeebaree¹, Adnan Mohsin Abdulazeez², Dilovan Asaad Zebari^3,*, Habibollah Haron⁴, Haza Nuzly Abdull Hamed⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314

Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level… More >

Ihsan Jasim Hussein¹, M. A. Burhanuddin², Mazin Abed Mohammed^3,*, Mohamed Elhoseny⁴, Begonya Garcia-Zapirain⁵, Marwah Suliman Maashi⁶, Mashael S. Maashi⁷

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691

Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer… More >

JUN ZHANG, YEHONG HUANG, WENZHUO LIU, LULU LI, LIMING CHEN^*

BIOCELL, Vol.44, No.4, pp. 591-595, 2020, DOI:10.32604/biocell.2020.011642

Abstract Estrogen receptor alpha (ERα/ESR1) is overexpressed in over half of all breast cancers and is considered a valuable therapeutic target in ERα positive breast cancer. Here, we designed a membrane-permeant Chaperonemediated Autophagy Targeting Chimeras (CMATAC) peptide to knockdown endogenous ERα protein through chaperone-mediated autophagy. The peptide contains a cell membrane-penetrating peptide (TAT) that allows the peptide to by-pass the plasma membrane, an αI peptide as a protein-binding peptide (PBD) that binds specifically to ERα, and CMA-targeting peptide (CTM) that targeting chaperone-mediated autophagy. We validated that ERα targeting peptide was able to target and degrade ERα to reduce the viability of… More >

Yuqin Xie¹, Shuang Deng¹, Qian Deng², Jiudong Xu^*

Oncologie, Vol.22, No.3, pp. 179-188, 2020, DOI:10.32604/Oncologie.2020.012544

Abstract This study aimed to explore LncRNA TUG1 targeted miR-222-3p in the proliferation and invasion of breast cancer (BC) cells. Seventy-six BC patients admitted to our hospital and 62 health check-ups at the same time were selected as the research objects. Among them, the former was seen as the observation group (OG), and the latter was considered as the control group (CG). The clinical significance of LncRNA TUG1 and miR-222-3p in BC was detected. Human BC cell MCF7 and normal human breast epithelial cell MCF-10A were purchased. After cells were transfected with LncRNA TUG1 and miR-222-3p, their proliferation, invasion, and apoptosis… More >

Neslihan Duzkale^1,*, Nilnur Eyerci²

Oncologie, Vol.22, No.3, pp. 161-166, 2020, DOI:10.32604/oncologie.2020.014116

Abstract BRCA1 and BRCA2 tumor suppressor genes are responsible for a quarter of hereditary breast cancers. Double heterozygous (DH) pathogenic variant carrier status in these genes is an extremely rare condition, especially in non-Askenazi individuals. We report a woman patient with bilateral breast cancer that carries DH disease-causing variants in BRCA1/2 genes. The 45-year-old patient who was followed up with the diagnosis of metachronous bilateral breast cancer was diagnosed with cancer at the age of 39 and 43, respectively. BRCA1/2 genes of the patient were evaluated using Next-Generation Sequencing. In the patient, the c.2800C>T (p.Gln934Ter) pathogenic variant in BRCA1 and the… More >

Burcu BİTERGE SÜT^1,*, Ayşe İKİNCİ KELE޲

Oncologie, Vol.22, No.3, pp. 147-154, 2020, DOI:10.32604/oncologie.2020.014449

Abstract Components of the protein synthesis machinery are subjected to alterations in cancer cells. eEF1D gene, which lies within the frequently amplified 8q24 locus, is one of the subunits of the human eukaryotic elongation factor complex. This study aimed to evaluate the prognostic and predictive significance of eEF1D in breast cancer using in silico analysis tools. For this purpose, we analyzed genomic alterations of the eEF1D gene using TCGA datasets via cBioPortal. Histopathological analysis was performed on patient tissue images obtained from cBioPortal and the Human Protein Atlas. Survival analysis was carried out using the KM Plotter and the prediction of… More >