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  • Open Access

    REVIEW

    Anticancer Activity of Novel NF-kB Inhibitor DHMEQ by Intraperitoneal Administration

    Kazuo Umezawa*, Andrzej Breborowicz, Shamil Gantsev

    Oncology Research, Vol.28, No.5, pp. 541-550, 2020, DOI:10.3727/096504020X15929100013698

    Abstract There have been great advances in the therapy of cancer and leukemia. However, there are still many neoplastic diseases that are difficult to treat. For example, it is often difficult to find effective therapies for aggressive cancer and leukemia. An NF- B inhibitor named dehydroxymethylepoxyquinomicin (DHMEQ) was discovered in 2000. This compound was designed based on the structure of epoxyquinomicin isolated from a microorganism. It was shown to be a specific inhibitor that directly binds to and inactivates NF- B components. Until now, DHMEQ has been used by many scientists in the world to suppress… More >

  • Open Access

    VIEW POINT IN FRENCH

    Manual Lymphatic Drainage for Upper Limb Lymphedema: A Clinician’s Perspective for its Adaptation
    Adapter le Drainage Lymphatique Manuel pour le Lymphœdème du Membre Supérieur: Point de vue de Cliniciens

    Jean-Claude Ferrandez1,*, 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… More >

  • Open Access

    REVIEW IN FRENCH

    Hairy Cell Leukemia and HCL-Like Disorders: Diagnosis and Treatment
    Leucémie à Tricholeucocytes et Autres Proliférations à Cellules Chevelues: Diagnostic et Traitement

    Elsa Maitre, Xavier Troussard*

    Oncologie, Vol.24, No.1, pp. 3-24, 2022, DOI:10.32604/oncologie.2022.021490

    Abstract Hairy cell leukemia (LT) accounts for 2% of all leukemias. The diagnosis is based on the presence in the blood and/ or the marrow of hairy cells expressing CD103, CD123, CD11c and CD25. The BRAFV600E mutation, a molecular marker of the disease, is present in more than 80% of cases. LT should be distinguished from other chronic B-cell lymphoproliferative disorders, including the variant form of hairy cell leukemia (HCL-V) and diffuse splenic red pulp lymphoma (DSRPL). Progress has recently been made in the management of patients. The purine analogues (PNAs) in monotherapy, deoxycoformycin (DCF) or 2-chloro-deoxyadenosine… More >

  • Open Access

    ARTICLE

    Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection

    R. Kavitha1,*, N. Viswanathan2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1305-1320, 2022, DOI:10.32604/iasc.2022.022816

    Abstract Bone marrow cancer is considered to be the most complex and dangerous disease which results due to an uncontrolled growth of white blood cells called leukocytes. Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) are considered to be important categories of bone cancers, which induces a larger number of cancer cells in the bone marrow, results in preventing the production of healthy blood cells. The advent of Artificial Intelligence, especially machine and deep learning, has expanded humanity’s capacity to analyze and detect these increasingly complex diseases. But, accurate detection of cancer cells and reducing the… More >

  • Open Access

    ARTICLE

    Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

    G. Sriram1, T. R. Ganesh Babu2, R. Praveena2,*, J. V. Anand3

    Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 29-40, 2022, DOI:10.32604/mcb.2022.016966

    Abstract Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an… More >

  • Open Access

    ARTICLE

    A Retrospective Analysis of 94 Patients with Hemophagocytic Lymphohistiocytosis of Unknown Etiology from a Single Center

    Xiaodan He, Jingshi Wang, Zhao Wang*

    Oncologie, Vol.23, No.4, pp. 559-567, 2021, DOI:10.32604/oncologie.2021.018647

    Abstract Despite extensive work-ups, some patients have been diagnosed with hemophagocytic lymphohistiocytosis (HLH) of unknown etiology. For HLH of unknown etiology, to investigate the clinical features and the factors that may affect the prognosis, we retrospectively reviewed the medical records of 94 patients hospitalized from January 2014 to December 2019. Survival times were evaluated until April 2020. For the 94 patients, the underlying causes of their diseases remained unclear at the end of the follow-up period, and the 1-, 3-, and 6-month survival rates, and the overall survival (OS) rates were 86.2%, 78.7%, 73.4%, and 70.2%,… More >

  • Open Access

    ARTICLE

    An Effective Feature Generation and Selection Approach for Lymph Disease Recognition

    Sunil Kr. Jha1,*, Zulfiqar Ahmad2

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 567-594, 2021, DOI:10.32604/cmes.2021.016817

    Abstract Health care data mining is noteworthy in disease diagnosis and recognition procedures. There exist several potentials to further improve the performance of machine learning based-classification methods in healthcare data analysis. The selection of a substantial subset of features is one of the feasible approaches to achieve improved recognition results of classification methods in disease diagnosis prediction. In the present study, a novel combined approach of feature generation using latent semantic analysis (LSA) and selection using ranker search (RAS) has been proposed to improve the performance of classification methods in lymph disease diagnosis prediction. The performance… More >

  • Open Access

    ARTICLE

    Clinical Significance of PD-L1 Expression and CD8-Positive Tumor-Infiltrating Lymphocytes in Patients with Cavitary Lung Adenocarcinoma

    Jiangyong Liu1,#,*, Mingming Gu2,#, Yang Xue1, Qiong Wang3, Yong Ren3, Wencai Huang1,*

    Oncologie, Vol.23, No.3, pp. 439-452, 2021, DOI:10.32604/oncologie.2021.017220

    Abstract Cavitary lung cancer is a rare type of lung cancer. Generally, the relationship between cavitary lung adenocarcinoma (LUAD) and specific immune checkpoints remains unknown. In this study, we aimed to detect the expression of programmed cell death ligand-1(PD-L1) and the density of CD8-positive (CD8+) tumor-infiltrating lymphocytes (TILs) to evaluate their clinicopathological significance in the case of patients with cavitary LUAD. This study included 65 patients with cavitary LUAD. Patient specimens were obtained from surgery. The expression of PD-L1 protein and CD8+ TIL status was detected by traditional immunohistochemistry and multiplex quantitative immunofluorescence technology. The correlation of… More >

  • Open Access

    ARTICLE

    Classification and Diagnosis of Lymphoma’s Histopathological Images Using Transfer Learning

    Schahrazad Soltane*, Sameer Alsharif , Salwa M.Serag Eldin

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 629-644, 2022, DOI:10.32604/csse.2022.019333

    Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and… More >

  • Open Access

    ARTICLE

    Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

    K. Muthumayil1, S. Manikandan2, S. Srinivasan3, José Escorcia-Gutierrez4,*, Margarita Gamarra5, Romany F. Mansour6

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2031-2044, 2021, DOI:10.32604/cmc.2021.017116

    Abstract White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we… More >

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