AIMC Topic: Carcinoma, Renal Cell
Clear Filters Showing 21 to 30 of 204 articles
Integrating radiomics and gene expression by mapping on the image with improved DeepInsight for clear cell renal cell carcinoma.
Cancer genetics
Feb 14, 2025
BACKGROUND: Radiomics analysis extracts high-dimensional features from medical images, which are used to predict outcomes in machine learning (ML). Recently, deep-learning methods have become applicable to image data converted from nonimage samples.
Integration of radiomic and deep features to reliably differentiate benign renal lesions from renal cell carcinoma.
European journal of radiology
Feb 7, 2025
PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial for optimized management, particularly for small renal lesions (≤4 cm in diameter). This study aimed to integrate clinical data, radiomic features, a...
Aged
Aged, 80 and over
Adult
Sensitivity and Specificity
Retrospective Studies
Tomography, X-Ray Computed
Female
Reproducibility of Results
Diagnosis, Differential
Radiographic Image Interpretation, Computer-Assisted
Kidney Neoplasms
Carcinoma, Renal Cell
Radiomics
Male
Middle Aged
Humans
Deep Learning
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC cancer
Jan 29, 2025
BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.
BMC cancer
Jan 27, 2025
OBJECTIVES: The detection of renal cell carcinoma (RCC) tumors in the earlier stages is of great importance for more effective treatment. Encouraged by the key role of imaging in the management of RCC, we conducted a systematic review and meta-analys...
Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma.
Life sciences
Jan 12, 2025
AIMS: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics anal...
Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.
Scientific reports
Jan 11, 2025
The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and pro...
Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.
Scientific reports
Jan 11, 2025
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of ...
Interpretable CT Radiomics-based Machine Learning Model for Preoperative Prediction of Ki-67 Expression in Clear Cell Renal Cell Carcinoma.
Academic radiology
Jan 9, 2025
RATIONALE AND OBJECTIVES: To develop and externally validate interpretable CT radiomics-based machine learning (ML) models for preoperative Ki-67 expression prediction in clear cell renal cell carcinoma (ccRCC).
Machine learning models predict the progression of long-term renal insufficiency in patients with renal cancer after radical nephrectomy.
BMC nephrology
Dec 18, 2024
BACKGROUND: Chronic Kidney Disease (CKD) is a common severe complication after radical nephrectomy in patients with renal cancer. The timely and accurate prediction of the long-term progression of renal function post-surgery is crucial for early inte...
Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.
International urology and nephrology
Dec 13, 2024
OBJECTIVE: To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of renal cell carcinoma (RCC) by developing a convolutional neural network (CNN) model and fusing it with c...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823