Prasanalakshmi Balaji^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1-14, 2023, DOI:10.32604/iasc.2023.026341 - 06 June 2022

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 More >

ASPASIA MANTA¹, SPYRIDON KAZANAS², STEFANOS KARAMAROUDIS³, HELEN GOGAS², DIMITRIOS C. ZIOGAS^2,*

Oncology Research, Vol.30, No.5, pp. 211-219, 2022, DOI:10.32604/or.2022.026913 - 03 February 2023

Abstract Epigenetic mechanisms, such as DNA methylation and histone modifications (e.g., acetylation and deacetylation), are strongly implicated in the carcinogenesis of various malignancies. During transcription, the expression and functionality of coding gene products are altered following the histone acetylation and deacetylation. These processes are regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively. HDAC inhibitors (HDACis) have been developed as promising therapeutic agents, to limit exposure to traditional and toxic chemotherapies and offer more alternatives for some specific malignant diseases with limited options. Mechanistically, these agents affect many intracellular pathways, including cell cycle arrest, apoptosis… More >

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 - 31 December 2022

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… More >

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

Oncologie, Vol.24, No.3, pp. 441-449, 2022, DOI:10.32604/oncologie.2022.024898 - 19 September 2022

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 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 - 31 March 2022

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 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 - 24 March 2022

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).… More >

Yasmeen Al-Saeed^1,2, Wael A. Gab-Allah¹, Hassan Soliman¹, Maysoon F. Abulkhair³, Wafaa M. Shalash⁴, Mohammed Elmogy^1,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638 - 14 January 2022

Abstract One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main More >

Fuat Turk^1,*, Murat Luy², Necaattin Barışçı³, Fikret Yalçınkaya⁴

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 349-363, 2022, DOI:10.32604/iasc.2022.023710 - 05 January 2022

Abstract Cases of kidney cancer have shown a rapid increase in recent years. Advanced technology has allowed bettering the existing treatment methods. Research on the subject is still continuing. Medical segmentation is also of increasing importance. In particular, deep learning-based studies are of great importance for accurate segmentation. Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate. Kidney tumors, specifically, are a type of cancer with a higher incidence in older people. As age progresses, the importance of having diagnostic tests increases. In some cases, patients with kidney More >

T. Jeslin^1,*, J. Arul Linsely²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 171-182, 2022, DOI:10.32604/cmc.2022.020255 - 03 November 2021

Abstract Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.… More >

Aihong Yu¹, Zhe Liu^1,*, Victor S. Sheng², Yuqing Song¹, Xuesheng Liu³, Chongya Ma⁴, Wenqiang Wang¹, Cong Ma¹

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 589-599, 2021, DOI:10.32604/iasc.2021.019513 - 11 August 2021

Abstract Liver cancer is one of frequent causes of death from malignancy in the world. Owing to the outstanding advantages of computer-aided diagnosis and deep learning, fully automatic segmentation of computed tomography (CT) images turned into a research hotspot over the years. The liver has quite low contrast with the surrounding tissues, together with its lesion areas are thoroughly complex. To deal with these problems, we proposed effective methods for enhancing features and processed public datasets from Liver Tumor Segmentation Challenge (LITS) for the verification. In this experiment, data pre-processing based on the image enhancement and… More >