AIMC Topic: Kidney Neoplasms
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Navigating advanced renal cell carcinoma in the era of artificial intelligence.
Cancer imaging : the official publication of the International Cancer Imaging Society
Feb 18, 2025
BACKGROUND: Research has helped to better understand renal cell carcinoma and enhance management of patients with locally advanced and metastatic disease. More recently, artificial intelligence has emerged as a powerful tool in cancer research, parti...
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...
Radiomics
Male
Middle Aged
Deep Learning
Aged, 80 and over
Adult
Sensitivity and Specificity
Diagnosis, Differential
Radiographic Image Interpretation, Computer-Assisted
Kidney Neoplasms
Carcinoma, Renal Cell
Humans
Retrospective Studies
Tomography, X-Ray Computed
Female
Reproducibility of Results
Aged
Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses.
Nature communications
Feb 7, 2025
Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 132...
A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial Nephrectomy.
Urology
Feb 4, 2025
OBJECTIVE: To test if our artificial intelligence (AI)-postoperative glomerular filtration rate (GFR) prediction is as accurate as a validated clinical model. The American Urologic Association recommends estimating postoperative GFR in patients with ...
Quality of Information on Wilms Tumor From Artificial Intelligence Chatbots: What Are Your Patients and Their Families Reading?
Urology
Feb 4, 2025
OBJECTIVE: To assess the ability of AI chatbots to deliver quality and understandable information on Wilms tumors to patients and their families.
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...
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