AIMC Topic: Carcinoma, Renal Cell

Clear Filters Showing 41 to 50 of 236 articles

Artificial intelligence-based multimodal prediction for nuclear grading status and prognosis of clear cell renal cell carcinoma: a multicenter cohort study.

International journal of surgery (London, England)
BACKGROUND: The assessment of the International Society of Urological Pathology (ISUP) nuclear grade is crucial for the management and treatment of clear cell renal cell carcinoma (ccRCC). This study aimed to explore the value of using integrated mul...

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.

Nature communications
Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RN...

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) could increase evidence-based treatment by generating additional treat...

Machine learning-derived prognostic signature integrating programmed cell death and mitochondrial function in renal clear cell carcinoma: identification of PIF1 as a novel target.

Cancer immunology, immunotherapy : CII
BACKGROUND: The pathogenesis and progression of renal cell carcinoma (RCC) involve complex programmed cell death (PCD) processes. As the powerhouse of the cell, mitochondria can influence cell death mechanisms. However, the prognostic significance of...

Navigating advanced renal cell carcinoma in the era of artificial intelligence.

Cancer imaging : the official publication of the International Cancer Imaging Society
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
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
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...

Fully automated segmentation and classification of renal tumors on CT scans via machine learning.

BMC cancer
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.