AIMC Topic: Urinary Bladder Neoplasms
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An accurate and trustworthy deep learning approach for bladder tumor segmentation with uncertainty estimation.
Computer methods and programs in biomedicine
Feb 10, 2025
BACKGROUND AND OBJECTIVE: Although deep learning-based intelligent diagnosis of bladder cancer has achieved excellent performance, the reliability of neural network predicted results may not be evaluated. This study aims to explore a trustworthy AI-b...
Discrepancies between physician-assessed and patient-reported complications after cystectomy - a prospective analysis.
World journal of urology
Feb 10, 2025
PURPOSE: Despite the high incidence of perioperative complications following cystectomy, there is a lack of evidence regarding patients' perceptions. Moreover, discrepancies between established complication grading systems and the patient's perspecti...
"Three-in-one" Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jan 15, 2025
Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of urine. As a critical component of routine urinalysis, urine protein testing measures the levels and ...
Deep learning-based lymph node metastasis status predicts prognosis from muscle-invasive bladder cancer histopathology.
World journal of urology
Jan 10, 2025
PURPOSE: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients...
Noninvasive identification of HER2 status by integrating multiparametric MRI-based radiomics model with the vesical imaging-reporting and data system (VI-RADS) score in bladder urothelial carcinoma.
Abdominal radiology (New York)
Jan 9, 2025
PURPOSE: HER2 expression is crucial for the application of HER2-targeted antibody-drug conjugates. This study aims to construct a predictive model by integrating multiparametric magnetic resonance imaging (mpMRI) based multimodal radiomics and the Ve...
A novel artificial intelligence segmentation model for early diagnosis of bladder tumors.
Abdominal radiology (New York)
Dec 30, 2024
OBJECTIVE: Despite cystoscopy plays an important role in bladder tumors diagnosis, it often falls short in flat cancerous tissue and minuscule satellite lesions. It can easily lead to a missed diagnosis by the urologist, which can lead to a swift tum...
Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.
Cells
Dec 26, 2024
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by ...
Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer.
Journal of translational medicine
Dec 23, 2024
OBJECTIVE: The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagn...
Reproducibility of Results
Tumor Microenvironment
Cell Line, Tumor
Single-Cell Analysis
Urinary Bladder Neoplasms
Humans
Computational Biology
Gene Expression Regulation, Neoplastic
Cell Movement
Immunotherapy
Neural Networks, Computer
Apoptosis
Multiomics
Prognosis
Cell Proliferation
Proteasome Endopeptidase Complex
Treatment Outcome
Spatially-resolved analyses of muscle invasive bladder cancer microenvironment unveil a distinct fibroblast cluster associated with prognosis.
Frontiers in immunology
Dec 20, 2024
BACKGROUND: Muscle-invasive bladder cancer (MIBC) is a prevalent cancer characterized by molecular and clinical heterogeneity. Assessing the spatial heterogeneity of the MIBC microenvironment is crucial to understand its clinical significance.
A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.
Abdominal radiology (New York)
Dec 18, 2024
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
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