Fei Wang¹, Jihyun Ma², Nan Zhao³, Chi Lin^3,*, Haixing Jiang^1,*

Oncologie, Vol.24, No.4, pp. 865-895, 2022, DOI:10.32604/oncologie.2022.025447

Abstract Objective: The characteristics of clinical features and prognoses among patients with different racial backgrounds have not been clearly studied. We thus investigated the clinical characteristics and overall survival (OS) differences among Asian, White, and Black patients with pancreatic neuroendocrine tumors (pNETs). Materials and Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried to identify patients with pNETs between 1983 and 2015. We performed univariable (UVA) and multivariable logistic regression (MVA) to assess the association between variables and race category. A Kaplan-Meier (KM) plot was used to calculate the OS rates. The Cox proportional hazard regression was used to… More >

XIANGLAI JIANG^1,2,3,4,#, JIN HE^2,#, YONGFENG WANG^2,3,4,5,#, JIAHUI LIU⁵, XIANGYANG LI⁵, XIANGUI HE⁵, HUI CAI^2,3,4,*

BIOCELL, Vol.47, No.2, pp. 351-366, 2023, DOI:10.32604/biocell.2023.025157

Abstract B and T-lymphocyte attenuator (BTLA) plays an immunosuppressive role by inhibiting T- and B-cell functions. BTLA is associated with a variety of diseases, especially cancer immunity. However, the function of BTLA in various cancers and its clinical prognostic value have still not been comprehensively analyzed. This study aimed to identify the relationship between BTLA and cancer from the perspectives of differences in BTLA expression, its clinical value, immune infiltration, and the correlation with immune-related genes in various cancers. Data regarding mRNA expression, miRNA expression, lncRNA expression, and clinical data of patients of 33 existing cancers were collected from the TCGA… More >

G. Nallasivan^1,*, V. Ramachandran², Roobaea Alroobaea³, Jasem Almotiri⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1655-1677, 2023, DOI:10.32604/csse.2023.030697

Abstract An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system… More >

Ibrahim Mahmoud El-Henawy¹, Mostafa Elbaz², Zainab H. Ali^3,*, Noha Sakr⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3489-3502, 2023, DOI:10.32604/cmc.2023.033356

Abstract Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the… More >

Mostafa Elbaz^1,2,*, Haitham Elwahsh¹, Ibrahim Mahmoud El-Henawy²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2927-2944, 2023, DOI:10.32604/cmc.2023.033111

Abstract Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in… More >

Dongqing Wang^1,*, Heying Chen¹, Yi Hu^2,*

Oncologie, Vol.24, No.3, pp. 441-449, 2022, DOI:10.32604/oncologie.2022.024898

Abstract Immunotherapy is currently recognized as one of the most promising anticancer strategies. In the tumor microenvironment, tumor-associated macrophages are mainly M2-type macrophages with tumor-promoting effects. Therefore, the reprogramming of tumor-associated macrophages from M2 to M1 type is a potential strategy for cancer therapy. We have previously shown the anticancer effects of implantable allogeneic M1 macrophages in mice. Here, we further engineered autologous mouse bone marrow cells into M1 macrophages and then embedded them into a sodium alginate gel to prepare an implantable immunotherapeutic agent (M1@Gel). We demonstrate that M1@Gel repolarizes M2 macrophages to M1 type and activates the immune responses… More >

Yu Sun, Wei Wang, Chenghai Zhao

Oncology Research, Vol.28, No.6, pp. 661-674, 2020, DOI:10.3727/096504020X16014648664459

Abstract Wnt molecules play crucial roles in development and adult homeostasis through their receptors Frizzled proteins (Fzds). Fzds mediate canonical b-catenin pathway and various noncanonical b-catenin-independent pathways. Aberrant Fzd signaling is involved in many diseases including cancer. Wnt/b-catenin is a well-established oncogenic pathway involved in almost every aspect of tumor development. However, Fzd-mediated noncanonical Wnt pathways function as both tumor promoters and tumor suppressors depending on cellular context. Fzd-targeted therapies have proven to be effective on cultured tumor cells, tumor cell xenografts, mouse tumor models, and patient-derived xenografts (PDX). Moreover, Fzd-targeted therapies synergize with chemotherapy in preclinical models. However, the occurrence… More >

Prasanalakshmi Balaji^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1-14, 2023, DOI:10.32604/iasc.2023.026341

Abstract Medical image assessment is based on segmentation at its fundamental stage. Deep neural networks have been more popular for segmentation work in recent years. However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable. For this reason, a novel optimized deep learning approach is proposed for medical image segmentation. Optimization plays an important role in terms of resources used, accuracy, and the time taken. The noise in the raw medical image are processed using Quasi-Continuous Wavelet Transform (QCWT). Then,… More >

Hiba Alzoubi¹, Alameen Alsabbah², Rosario Caltabiano³, Giuseppe Broggi^3,*

Oncologie, Vol.24, No.1, pp. 51-63, 2022, DOI:10.32604/oncologie.2022.020890

Abstract The 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) improved our understanding of the brain neoplasm biology. In more details, differences between diffuse gliomas that primarily occur in adults and those that primarily occur in children have been identified by the terms “adult-type” and “pediatric-type” diffuse gliomas. More importantly, both diagnostic and grading criteria for adult-type diffuse astrocytomas have been modified, by adopting novel molecular markers: diffuse astrocytomas, IDH-mutant have been grouped into a single entity and graded as CNS WHO grades 2, 3, or 4, with the assignment of Grade 4 in the… More >

S. Priyadarsini^1,*, Carlos Andrés Tavera Romero², M. Mrunalini³, Ganga Rama Koteswara Rao⁴, Sudhakar Sengan⁵

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1517-1530, 2022, DOI:10.32604/iasc.2022.024786

Abstract A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed. Diagnosis of liver diseases based on observation using liver CT images is essential for surgery and treatment planning. Identifying the progression of cancerous regions and Classification into Benign Tumors and Malignant Tumors are essential for treating liver diseases. The manual process is time-consuming and leads to intra and inter-observer variability. Hence, an automatic method based on enhanced region growing is proposed for the Classification of Liver Tumors (LT). To enhance the Liver Region… More >