Amjad Rehman¹, Muhammad Mujahid¹, Robertas Damasevicius^2,*, Faten S Alamri³, Tanzila Saba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

JINXIA CHEN^1,2,3,#, SULI DAI^1,2,#, GENG ZHANG^4,5, SISI WEI^1,2, XUETAO ZHAO³, YANG ZHENG^1,2, YAOJIE WANG^1,2, XIAOHAN WANG^1,2, YUNJIANG LIU^4,5,*, LIANMEI ZHAO^1,2,*

Oncology Research, Vol.32, No.11, pp. 1791-1802, 2024, DOI:10.32604/or.2024.049348 - 16 October 2024

Abstract Background: Triple-negative breast cancer (TNBC) is a heterogeneous, recurring cancer characterized by a high rate of metastasis, poor prognosis, and lack of efficient therapies. KBU2046, a small molecule inhibitor, can inhibit cell motility in malignant tumors, including breast cancer. However, the specific targets and the corresponding mechanism of its function remain unclear. Methods: In this study, we employed (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H tetrazolium) (MTS) assay and transwell assay to investigate the impact of KBU2046 on the proliferation and migration of TNBC cells in vitro. RNA-Seq was used to explore the targets of KBU2046 that inhibit the motility of TNBC.… More > Graphic Abstract

Feyza Altunbey Özbay¹, Erdal Özbay², Farhad Soleimanian Gharehchopogh^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1067-1110, 2024, DOI:10.32604/cmes.2024.054334 - 27 September 2024

Abstract Artificial rabbits optimization (ARO) is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature. However, for solving optimization problems, the ARO algorithm shows slow convergence speed and can fall into local minima. To overcome these drawbacks, this paper proposes chaotic opposition-based learning ARO (COARO), an improved version of the ARO algorithm that incorporates opposition-based learning (OBL) and chaotic local search (CLS) techniques. By adding OBL to ARO, the convergence speed of the algorithm increases and it explores the search space better. Chaotic maps in CLS… More > Graphic Abstract

Oncology Research Editorial Office

Oncology Research, Vol.32, No.10, pp. 1679-1680, 2024, DOI:10.32604/or.2024.056894 - 18 September 2024

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.10, pp. 1675-1676, 2024, DOI:10.32604/or.2024.056889 - 18 September 2024

Abstract This article has no abstract. More >

Youqi Jiang^1,#, Bing Wu^2,#, Jiahui Chen³, Ruyi Jin³, Guangshan Jin³, Minhao Zhang⁴, Qin Zhou^4,*, Aiji Jiang^2,*

Psycho-Oncologie, Vol.18, No.3, pp. 191-200, 2024, DOI:10.32604/po.2024.053942 - 12 September 2024

Abstract Objective: This study evaluated the effect of resilience on depression among patients with breast cancer from individual and familial perspectives by exploring the mediating and moderating effects of family resilience between individual resilience and depression. Methods: A questionnaire survey was conducted among 337 patients with breast cancer who were admitted to the Oncology Department of Jiangsu Province Hospital. The survey included demographic information, the Connor–Davidson Resilience Scale (CD-RISC), the Family Resilience Assessment Scale (FRAS), and the Chinese version of the Patient Health Questionnaire-9 (PHQ-9) for Depression. The relationship among individual resilience, family resilience, and depression… More >

Huimin Zheng, Minghui Wang^*, Miao Ye

Psycho-Oncologie, Vol.18, No.3, pp. 169-179, 2024, DOI:10.32604/po.2024.052703 - 12 September 2024

Abstract Background: Although fear of cancer recurrence (FCR) is the most important factor affecting the life quality of young breast cancer patients, and it may be affected by the patient’s personality, marital relationship and communication, there is a lack of research on the relationship between adult attachment, self-disclosure and FCR in patients. This study investigated the current situation of FCR in young breast cancer patients, its correlation with adult attachment and self-disclosure and its influencing factors, in order to predict the impact of adult attachment and self-disclosure of patients to spouse on FCR. Methods: A survey… More >

Nevena Stojadinović¹, Goran Mihajlović¹, Marko Spasić¹, Milena Mladenović¹, Darko Hinić^2,*

Psycho-Oncologie, Vol.18, No.3, pp. 159-168, 2024, DOI:10.32604/po.2024.050122 - 12 September 2024

Abstract Breast cancer is considered one of the most frequent causes of morbidity and death in women. Individuals’ response to information regarding health threats and illness can influence the adjustment of the treatment to existing conditions including the issues of non-completion of treatment or non-attendance at medical appointments. The study aimed to examine the relationship between illness perception, body image dissatisfaction and (mal)adaptive coping styles in breast cancer patients. A sample of 197 patients with diagnosed breast cancer hospitalized at the Center for Oncology and Radiology, Kragujevac, Serbia, was surveyed. The instruments included sociodemographic questionnaire, a… More >

Dennies Tsietso^1,*, Abid Yahya¹, Ravi Samikannu¹, Basit Qureshi², Muhammad Babar^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4749-4765, 2024, DOI:10.32604/cmc.2024.052793 - 12 September 2024

Abstract Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide. Various Computer-Aided Diagnosis (CAD) tools, based on breast thermograms, have been developed for early detection of this disease. However, accurately segmenting the Region of Interest (ROI) from thermograms remains challenging. This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottom boundary using a second-degree polynomial. The proposed method demonstrated high efficacy, achieving an impressive Jaccard coefficient of 86% and a Dice… More >