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  • Open Access


    Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

    Ishu Anand1, Himani Negi1, Deepika Kumar1, Mamta Mittal2, Tai-hoon Kim3,*, 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 >

  • Open Access


    Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

    Shahan Yamin Siddiqui1,2, Iftikhar Naseer3, Muhammad Adnan Khan4, Muhammad Faheem Mushtaq5, Rizwan Ali Naqvi6,*, 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 >

  • Open Access


    Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

    Mavra Mehmood1, Ember Ayub1, Fahad Ahmad1,6,*, 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 >

  • Open Access


    Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

    Diyar Qader Zeebaree1, Adnan Mohsin Abdulazeez2, Dilovan Asaad Zebari3,*, 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 >

  • Open Access


    Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

    Ihsan Jasim Hussein1, M. A. Burhanuddin2, Mazin Abed Mohammed3,*, 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 >

  • Open Access


    Chaperone-mediated autophagy targeting chimeras (CMATAC) for the degradation of ERα in breast cancer


    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 >

  • Open Access


    RETRACTED: LncRNA TUG1 Targets miR-222-3p to Take Part in Proliferation and Invasion of Breast Cancer Cells

    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 >

  • Open Access


    Double Heterozygosity in the BRCA1/2 Genes in a Turkish Patient with Bilateral Breast Cancer: A Case Report

    Neslihan Duzkale1,*, 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 >

  • Open Access


    Prognostic and Predictive Significance of Eukaryotic Elongation Factor 1D (eEF1D) in Breast Cancer: A Potential Marker of Response to Endocrine Therapy


    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 >

  • Open Access


    Breast Cancer: Delays of Access to Diagnosis and Treatment Retrospective Study–Batna, Algeria August 2015–February 2016
    Cancer du Sein: Délais d’Accès au Diagnostic et aux Traitements Etude Rétrospective–Batna, Algérie Août 2015–Février 2016

    Fadhila Mansour1,2,*, Abdelhak Lakehal2,3, Lahcène Nezzal2,3

    Oncologie, Vol.22, No.3, pp. 117-128, 2020, DOI:10.32604/oncologie.2020.014979

    Abstract The objective of this study was to quantify the delays in access to diagnosis and treatment of women with breast cancer who are managed at the anticancer center of Batna, Algeria. This was a descriptive retrospective study conducted during the period August 2015 to February 2016. In order to trace the history of the journey from the first signs of cancer to the first treatments, a questionnaire was filled in from the medical files and completed by an interview with the patients. A total of 267 patients were included in the study. The median time to management was 4.5 months.… More >

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