Federico Greco^1,2,*, Marco Cataldo³, Valerio D’Andrea^2,4, Luca Pugliese⁵, Andrea Panunzio⁶, Alessandro Tafuri⁶, Bruno Beomonte Zobel^2,4, Carlo Augusto Mallio^2,4

Canadian Journal of Urology, Vol.32, No.5, pp. 433-443, 2025, DOI:10.32604/cju.2025.068390 - 30 October 2025

Abstract Background: Radiogenomics offers a non-invasive approach to correlate imaging features with tumor molecular profiles. This study aims to identify computed tomography (CT) imaging characteristics associated with positive NIPA-like domain containing 4 (NIPAL4) expression in clear cell renal cell carcinoma (ccRCC) and to develop a radiogenomic predictive model to support personalized risk stratification. Methods: A retrospective analysis was conducted on 241 ccRCC patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases. Clinical, pathological, and CT features were compared between NIPAL4-positive and NIPAL4-negative groups. A penalized logistic regression model was built to… More >

Tiren Huang, Chong Luo, Xu Li^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5847-5865, 2025, DOI:10.32604/cmc.2025.067986 - 23 October 2025

Abstract Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection, treatment planning and therapeutic evaluation. However, the pancreas’s small size, irregular morphology, and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task. To address these challenges, we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images. First, a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network, enhancing pancreatic feature extraction and improving localization accuracy.… More >

Yanwen Zhu^1,#, Chen Zhao^2,#, Yishuo Xu², Zheyang Wu³, Akiko Maehara⁴, Liang Wang¹, Dirui Zhang², Ming Zeng², Rui Lv⁵, Xiaoya Guo⁶, Mengde Huang¹, Minglong Chen⁷, Gary S. Mintz⁴, Dalin Tang^1,3,*, Haibo Jia², Bo Yu^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1249-1270, 2025, DOI:10.32604/cmes.2025.067039 - 31 August 2025

Abstract Image-based computational models have been used for vulnerable plaque progression and rupture predictions, and good results have been reported. However, mechanisms and predictions for plaque erosion are under-investigated. Patient-specific fluid-structure interaction (FSI) models based on optical coherence tomography (OCT) follow-up data from patients with plaque erosion and who received conservative antithrombotic treatment (using medication, no stenting) to identify risk factors that could be used to predict the treatment outcome. OCT and angiography data were obtained from 10 patients who received conservative antithrombotic treatment. Five participants had worse outcomes (WOG, stenosis severity ≥ 70% at one-year… More > Graphic Abstract

Aysenur Elmali¹, Birhan Demirhan², Caglayan Selenge Beduk Esen³, Ozan Cem Guler⁴, Pervin Hurmuz⁵, Cem Onal^1,4,*

Canadian Journal of Urology, Vol.32, No.4, pp. 243-254, 2025, DOI:10.32604/cju.2025.066700 - 29 August 2025

Abstract Background: Intermediate-risk prostate cancer (IR-PC) represents a heterogeneous group requiring nuanced treatment approaches, and recent advancements in radiotherapy (RT), androgen deprivation therapy (ADT), and prostate-specific membrane antigen positron emission tomography (PSMA-PET/CT) imaging have prompted growing interest in personalized, risk-adapted management strategies. This study by the Turkish Society for Radiation Oncology aims to examine radiation oncologists’ practices in managing IR-PC, focusing on RT and imaging modalities to identify trends for personalized treatments. Methods: A cross-sectional survey was conducted among Turkish radiation oncologists treating at least 50 prostate cancer (PC) cases annually. The 22-item questionnaire covered IR-PC… More >

Qingyang Zhou¹, Guofeng Lu², Yunfan Ye^3,*, Zhiping Cai¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2411-2427, 2025, DOI:10.32604/cmc.2025.066810 - 03 July 2025

Abstract Computed Tomography (CT) reconstruction is essential in medical imaging and other engineering fields. However, blurring of the projection during CT imaging can lead to artifacts in the reconstructed images. Projection blur combines factors such as larger ray sources, scattering and imaging system vibration. To address the problem, we propose DeblurTomo, a novel self-supervised learning-based deblurring and reconstruction algorithm that efficiently reconstructs sharp CT images from blurry input without needing external data and blur measurement. Specifically, we constructed a coordinate-based implicit neural representation reconstruction network, which can map the coordinates to the attenuation coefficient in the… More >

Ovidijus Grigas, Robertas Damaševičius^*, Rytis Maskeliūnas

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1805-1838, 2025, DOI:10.32604/cmc.2025.064354 - 09 June 2025

Abstract Brain imaging is important in detecting Mild Cognitive Impairment (MCI) and related dementias. Magnetic Resonance Imaging (MRI) provides structural insights, while Positron Emission Tomography (PET) evaluates metabolic activity, aiding in the identification of dementia-related pathologies. This study integrates multiple data modalities—T1-weighted MRI, Pittsburgh Compound B (PiB) PET scans, cognitive assessments such as Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Functional Activities Questionnaire (FAQ), blood pressure parameters, and demographic data—to improve MCI detection. The proposed improved Convolutional Mixer architecture, incorporating B-cos modules, multi-head self-attention, and a custom classifier, achieves a classification accuracy of 96.3% More >

Anand Deva Durai Chelladurai¹, Theena Jemima Jebaseeli², Omar Alqahtani¹, Prasanalakshmi Balaji^1,*, Jeniffer John Simon Christopher³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1101-1122, 2025, DOI:10.32604/cmc.2025.062278 - 09 June 2025

Abstract Optical Coherence Tomography (OCT) provides cross-sectional and three-dimensional reconstructions of the target tissue, allowing precise imaging and quantitative analysis of individual retinal layers. These images, based on optical inhomogeneities, reveal intricate cellular structures and are vital for tasks like retinal segmentation. The proposed study uses OCT images to identify significant differences in peripapillary retinal nerve fiber layer thickness. Incorporating spectral-domain analysis of OCT images significantly enhances the evaluation of Purtcher Retinopathy. To streamline this process, the study introduces a Context Encoding Deep Neural Network (CEDNN), which eliminates the time-consuming manual segmentation process while improving the… More >

Yueshun Chen^1,2,*, Yupeng Zhou¹, Cao Yin³

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 575-592, 2025, DOI:10.32604/sdhm.2024.057005 - 03 April 2025

Abstract Computed tomography (CT) can inspect the internal structure of concrete with high resolution, but improving the accuracy of measurements remains a key challenge due to the reliance on complex image processing and significant manual intervention. This study aims to optimize CT scanning parameters to enhance the accuracy of measuring crack widths and rebar volumes in reinforced concrete. Nine sets of specimens, each with varying rebar diameters and concrete cover thicknesses, were scanned before and after corrosion using an Optima CT scanner, followed by three-dimensional reconstructions using Avizo software. The effects of threshold values and “Erosion” More >

Jahida Subhedar^1,2, Anurag Mahajan^1,*, Shabana Urooj³, Neeraj Kumar Shukla^4,5

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2831-2847, 2025, DOI:10.32604/cmc.2025.059350 - 17 February 2025

Abstract Retinal Optical Coherence Tomography (OCT) images, a non-invasive imaging technique, have become a standard retinal disease detection tool. Due to disease, there are morphological and textural changes in the layers of the retina. Classifying OCT images is challenging, as the morphological manifestations of different diseases may be similar. The OCT images capture the reflectivity characteristics of the retinal tissues. Retinal diseases change the reflectivity property of retinal tissues, resulting in texture variations in OCT images. We propose a hybrid approach to OCT image classification in which the Convolution Neural Network (CNN) model is trained using… More >

Rana Mohamed^1,2,*, Mostafa Elgendy¹, Mohamed Taha¹

Computer Systems Science and Engineering, Vol.49, pp. 213-238, 2025, DOI:10.32604/csse.2024.059491 - 10 January 2025

Abstract Globally, liver cancer ranks as the sixth most frequent malignancy cancer. The importance of early detection is undeniable, as liver cancer is the fifth most common disease in men and the ninth most common cancer in women. Recent advances in imaging, biomarker discovery, and genetic profiling have greatly enhanced the ability to diagnose liver cancer. Early identification is vital since liver cancer is often asymptomatic, making diagnosis difficult. Imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasonography can be used to identify liver cancer once a sample of liver tissue is… More >