Jingcheng Lyu^1,2,#, Ruiyu Yue^1,2,#, Ye Tian^1,2,*, Boyu Yang^1,2,*

Canadian Journal of Urology, Vol.33, No.2, pp. 361-375, 2026, DOI:10.32604/cju.2026.074814 - 20 April 2026

Abstract Objectives: Patients with a multi-parameter magnetic resonance imaging (mpMRI) prostate imaging report and data system (PI-RADS) score ≤ 3, but with clinically significant prostate cancer (CSPCa) detected by biopsy, are termed MRI-Invisible prostate cancer (MRI(-)PCa). This study aims to explore risk factors for MRI(-)PCa and identify immunohistochemical indicators with predictive significance. Methods: A retrospective analysis was conducted on 376 patients with PI-RADS score ≤ 3 who underwent 24-needle systematic prostate biopsy at Beijing Friendship Hospital, Capital Medical University (January 2015 to October 2025). Clinical data, imaging data, and Angiogenic factor with G and FHA domain… More >

Şeref Barbaros Arik^1,2,*, İnanç Güvenç^1,2

Canadian Journal of Urology, Vol.32, No.6, pp. 569-578, 2025, DOI:10.32604/cju.2025.068094 - 30 December 2025

Abstract Objectives: Differentiating benign from malignant testicular lesions is essential to avoid unnecessary surgery and ensure timely intervention. While conventional ultrasound remains the first-line imaging method, elastography and MRI provide additional functional and structural information. This study assesses the diagnostic utility of testicular elastography and magnetic resonance imaging (MRI) in differentiating benign and malignant testicular lesions. Methods: Patients with sonographically detected testicular masses were retrospectively evaluated using elastography, scrotal MRI, and tumor markers. Quantitative and qualitative imaging findings, lesion size, and laboratory values were recorded. Statistical analyses included Fisher’s exact test, logistic regression, Receiver operating characteristic… More >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

Jingcheng Lyu^1,2,#, Ruiyu Yue^1,2,#, Boyu Yang^1,2, Xuanhao Li^1,2, Jian Song^1,2,*

Canadian Journal of Urology, Vol.32, No.5, pp. 445-456, 2025, DOI:10.32604/cju.2025.068853 - 30 October 2025

Abstract Objectives: This study aimed to develop an artificial intelligence (AI)-based image recognition system using the nnU-Net adaptive neural network to assist clinicians in detecting magnetic resonance imaging (MRI)-invisible prostate cancer. The motivation stems from the diagnostic challenges, especially when MRI findings are inconclusive (Prostate Imaging Reporting and Data System [PI-RADS] score ≤ 3). Methods: We retrospectively included 150 patients who underwent systematic prostate biopsy at Beijing Friendship Hospital between January 2013 and January 2023. All were pathologically confirmed to have clinically significant prostate cancer, despite negative findings on preoperative MRI. A total of 1475 MRI… More >

Khalil Ibrahim Lairedj¹, Zouaoui Chama¹, Amina Bagdaoui¹, Samia Larguech², Younes Menni^3,4,*, Nidhal Becheikh⁵, Lioua Kolsi^6,*, Badr M. Alshammari⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2419-2443, 2025, DOI:10.32604/cmes.2025.069396 - 31 August 2025

Abstract Brain tumor segmentation from Magnetic Resonance Imaging (MRI) supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans, making it a crucial yet challenging task. Supervised models such as 3D U-Net perform well in this domain, but their accuracy significantly improves with appropriate preprocessing. This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model (GGMM) to T1 contrast-enhanced MRI scans from the BraTS 2020 dataset. The Expectation-Maximization (EM) algorithm is employed to estimate parameters for four tissue classes, generating a More >

Rachana Poongodan¹, Dayanand Lal Narayan², Deepika Gadakatte Lokeshwarappa³, Hirald Dwaraka Praveena⁴, Dae-Ki Kang^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5751-5772, 2025, DOI:10.32604/cmc.2025.066314 - 30 July 2025

Abstract In recent decades, brain tumors have emerged as a serious neurological disorder that often leads to death. Hence, Brain Tumor Segmentation (BTS) is significant to enable the visualization, classification, and delineation of tumor regions in Magnetic Resonance Imaging (MRI). However, BTS remains a challenging task because of noise, non-uniform object texture, diverse image content and clustered objects. To address these challenges, a novel model is implemented in this research. The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN, which effectively captures 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 >

Khalid Jamil¹, Wahab Khan¹, Bilal Khan², Sarwar Shah Khan^2,*

Digital Engineering and Digital Twin, Vol.3, pp. 1-16, 2025, DOI:10.32604/dedt.2025.058943 - 28 February 2025

Abstract Brain tumors are one of the deadliest cancers, partly because they’re often difficult to detect early or with precision. Standard Magnetic Resonance Imaging (MRI) imaging, though essential, has limitations, it can miss subtle or early-stage tumors, which delays diagnosis and affects patient outcomes. This study aims to tackle these challenges by exploring how machine learning (ML) can improve the accuracy of brain tumor identification from MRI scans. Motivated by the potential for artificial intillegence (AI) to boost diagnostic accuracy where traditional methods fall short, we tested several ML models, with a focus on the K-Nearest More >

Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Noam Bar-Yaakov^1,2, Ziv Savin^1,2, Ibrahim Fahoum^2,3, Sophie Barnes^2,4, Yuval Bar-Yosef^1,2, Ofer Yossepowitch^1,2, Gal Keren-Paz^1,2, Roy Mano^1,2

Canadian Journal of Urology, Vol.31, No.1, pp. 11793-11801, 2024

Abstract Introduction: Prostate cancer screening with PSA is associated with low specificity; furthermore, little is known about the optimal timing of biopsy. We aimed to evaluate whether a risk classification system combining PSA density (PSAD) and mpMRI can predict clinically significant cancer and determine biopsy timing.
Materials and methods: We reviewed the medical records of 256 men with a PI-RADS ≥ 3 lesion on mpMRI who underwent transperineal targeted and systematic biopsies of the prostate between 2017-2019. Patients were stratified into three risk groups based on PSAD and mpMRI findings.
The study endpoint was clinically significant prostate cancer (CSPC).… More >