AIMC Topic: Kidney Neoplasms
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Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets.
BMC medical imaging
May 30, 2025
BACKGROUND: To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention.
Multiomics in Renal Cell Carcinoma: Current Landscape and Future Directions for Precision Medicine.
Current urology reports
May 26, 2025
PURPOSE OF REVIEW: Renal cell carcinoma (RCC) is a prevalent and increasingly diagnosed malignancy associated with high mortality and recurrence rates. Traditional diagnostic and therapeutic approaches have limitations due to the disease's molecular ...
Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.
European journal of radiology
Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Boosting Machine Learning Algorithms
Neoplasm Grading
Radiographic Image Interpretation, Computer-Assisted
Kidney Neoplasms
Carcinoma, Renal Cell
Reproducibility of Results
Aged
Aged, 80 and over
Adult
Male
Middle Aged
Contrast Media
Sensitivity and Specificity
Radiomics
Humans
Machine Learning
Tomography, X-Ray Computed
Female
Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.
JCO clinical cancer informatics
Apr 25, 2025
PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody ...
Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.
Nature communications
Apr 15, 2025
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...
Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).
Exploring the Incremental Value of Aorta Enhancement Normalization Method in Evaluating Renal Cell Carcinoma Histological Subtypes: A Multi-center Large Cohort Study.
Academic radiology
Mar 28, 2025
RATIONALE AND OBJECTIVES: The classification of renal cell carcinoma (RCC) histological subtypes plays a crucial role in clinical diagnosis. However, traditional image normalization methods often struggle with discrepancies arising from differences i...
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)
Mar 28, 2025
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...
Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.
Scientific reports
Mar 28, 2025
The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the i...
Foundation Model and Radiomics-Based Quantitative Characterization of Perirenal Fat in Renal Cell Carcinoma Surgery.
Academic radiology
Mar 24, 2025
RATIONALE AND OBJECTIVES: To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma.
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