AIMC Topic: Urinary Bladder Neoplasms
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Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.
Nature communications
Jun 1, 2024
Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful de...
Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer.
Journal of cancer research and clinical oncology
May 20, 2024
BACKGROUND: Bladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research ai...
Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods.
Lab on a chip
May 14, 2024
The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening meth...
Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.
Medical image analysis
May 8, 2024
Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor...
A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.
Scandinavian journal of urology
May 2, 2024
OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in ...
A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.
International journal of surgery (London, England)
May 1, 2024
BACKGROUND: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and foll...
Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.
Genetics research
Apr 30, 2024
Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosi...
Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.
Abdominal radiology (New York)
Apr 23, 2024
PURPOSE: To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical ...
Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer.
Scientific reports
Apr 23, 2024
Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is involved in the progression of BC. This study aimed to developed a novel diagnostic model based on mito...
Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple Z-Plane Scanning.
Acta cytologica
Apr 22, 2024
INTRODUCTION: Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by pro...
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