Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 2025

Abstract Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >

Theano Makridou¹, Elena Vlastou², Vasilios Kouloulias³, Efstathios P. Efstathopoulos⁴, Kalliopi Platoni^4,*

Oncology Research, Vol.33, No.12, pp. 3611-3632, 2025, DOI:10.32604/or.2025.069101 - 27 November 2025

Abstract Nanomedicine has evolved significantly over the last decades and expanded its applications in pediatric oncology, which represents a special domain with unique patients and distinct requirements. Τhe need for early cancer diagnosis and more effective and targeted therapies aiming to increase the pediatric patients’ survival rates and minimize the treatment-related side effects to survivors is profound. Nanoparticles (NPs) come as a beacon of hope to provide sensitive cancer diagnostic tools and assist contrast agents’ transport to the malignant tumors. Besides, NPs could be designed to deliver targeted drugs and genes to tumors, minimizing the medicine-related… More >

Ji-eun Yu¹, Eunji Ahn², Hanbit Jin², Dongsu Kim^2,*

International Journal of Mental Health Promotion, Vol.27, No.9, pp. 1353-1367, 2025, DOI:10.32604/ijmhp.2025.067407 - 30 September 2025

Abstract Background: Patients with cancer are confronted not only with physical changes and pain but also with significant psychological challenges, including distress, anxiety, and depression, as a consequence of their diagnosis and treatment. This study aimed to identify the factors influencing anxiety or depression in patients with cancer, examine the relationship between the duration since cancer diagnosis and psychological state, and explore the association between these factors and the use of Korean medicine (KM). Methods: This study utilized data from the 2018 Korea Health Panel spanning 2008 to 2018. The analysis focused on adult participants (aged… More >

Saranya Velmurugan¹, Dapkupar Wankhar², Vijayalakshmi Paramasivan³, Gowtham Kumar Subbaraj^1,*

BIOCELL, Vol.49, No.8, pp. 1363-1390, 2025, DOI:10.32604/biocell.2025.065891 - 29 August 2025

Abstract Cancer rates are increasing globally, making it more urgent than ever to enhance research and treatment strategies. This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis, knowledge, and therapy. A complete literature search was undertaken using PubMed, Elsevier, Google Scholar, ScienceDirect, Embase, and NCBI. This review examined the articles published from 2010 to 2025. Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods, like next-generation sequencing and single-cell analysis, have transformed our ability to study tumor complexity and genetic mutations, paving the… More >

Xiao Jian Tan^1,2,3,*, Wai Loon Cheor², Ee Meng Cheng^4,5, Chee Chin Lim^3,4, Khairul Shakir Ab Rahman⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2015-2060, 2025, DOI:10.32604/cmc.2025.063407 - 03 July 2025

Abstract This study systematically reviews the applications of generative artificial intelligence (GAI) in breast cancer research, focusing on its role in diagnosis and therapeutic development. While GAI has gained significant attention across various domains, its utility in breast cancer research has yet to be comprehensively reviewed. This study aims to fill that gap by synthesizing existing research into a unified document. A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in the retrieval of 3827 articles, of which 31 were deemed eligible for analysis. The included studies were… More >

Mostafa Ahmed Arafa^1,2, Karim Hamda Farhat^1,*, Nesma Lotfy³, Farrukh Kamel Khan¹, Alaa Mokhtar⁴, Abdulaziz Mohammed Althunayan¹, Waleed Al-Taweel⁴, Sultan Saud Al-Khateeb⁴, Sami Azhari^1,5, Danny Munther Rabah^1,4

Canadian Journal of Urology, Vol.32, No.3, pp. 173-180, 2025, DOI:10.32604/cju.2025.066016 - 27 June 2025

Abstract Background: Transrectal (TR) and transperineal (TP) biopsies are commonly used methods for diagnosing prostate cancer. However, their comparative effectiveness in conjunction with machine learning (ML) techniques remains underexplored. This study aimed to evaluate the predictive accuracy of ML algorithms in detecting prostate cancer using data derived from TR and TP biopsies. Methods: The clinical records of patients who underwent prostate biopsy at King Saud University Medical City and King Faisal Specialist Hospital and Research Centerin Riyadh, Saudi Arabia, between 2018 and 2025 were analyzed. Data were used to train and test ML models, including eXtreme… More >

CHUCHU ZHANG^1,#, YING LIU^2,#, ZEHUI CHEN¹, YI LIU³, QIYUAN MAO⁴, GE ZHANG⁵, HONGSHENG LIN⁴, JIABIN ZHENG^6,*, HAIYAN LI^1,*

Oncology Research, Vol.33, No.6, pp. 1437-1458, 2025, DOI:10.32604/or.2025.059290 - 29 May 2025

Abstract Objectives: Decades of clinical and fundamental research advancements in oncology have led to significant breakthroughs such as early screening, targeted therapies, and immunotherapy, contributing to reduced mortality rates in cancer patients. Despite these achievements, cancer continues to be a major public health challenge. This study employs bibliometric techniques to visually analyze the English literature on cancer prevention, screening, diagnosis, treatment, and rehabilitation. Methods: We systematically reviewed publications from 01 March 2014, to 01 March 2024, indexed in the Web of Science core collection. Tools such as VOSviewer Version 1.6.20 is characterized by its core idea… More > Graphic Abstract

Majdi Rawashdeh^1,2,*, Muath A. Obaidat³, Meryem Abouali⁴, Dhia Eddine Salhi⁵, Kutub Thakur⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1129-1155, 2025, DOI:10.32604/cmes.2025.063765 - 11 April 2025

Abstract Cancer is a formidable and multifaceted disease driven by genetic aberrations and metabolic disruptions. Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer, which is also the top cause of death worldwide. The malignancy has a terrible 5-year survival rate of 19%. Early diagnosis is critical for improving treatment outcomes and survival rates. The study aims to create a computer-aided diagnosis (CAD) that accurately diagnoses lung disease by classifying histopathological images. It uses a publicly accessible dataset that includes 15,000 images of benign, malignant, and squamous cell carcinomas in the lung.… More >

S. Ramacharan^1,*, Martin Margala¹, Amjan Shaik², Prasun Chakrabarti³, Tulika Chakrabarti⁴

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3731-3760, 2024, DOI:10.32604/cmc.2024.058488 - 19 December 2024

Abstract Breast cancer remains a significant global health concern, with early detection being crucial for effective treatment and improved survival rates. This study introduces HERA-Net (Hybrid Extraction and Recognition Architecture), an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging modalities. The HERA-Net model integrates powerful deep learning architectures, including VGG19, U-Net, GRU (Gated Recurrent Units), and ResNet-50, to capture multi-dimensional features that support robust image segmentation, feature extraction, and temporal analysis. For thermographic imaging, a comprehensive dataset of 3534 infrared (IR) images from the… 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 >