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Radiomics and Artificial Intelligence: Renal Cell Carcinoma
Alex G Raman1, David Fisher2, Felix Yap3
1Department of Radiology, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA 90033, USA; Western University of Health Sciences, 309 East Second Street, Pomona, CA 91766-1854, USA.
Accurate kidney cancer diagnosis and prognosis are crucial. Advanced imaging analysis using radiomics and artificial intelligence shows significant potential for personalized renal cell carcinoma treatment.
Area of Science:
- Medical imaging analysis
- Oncology
- Artificial intelligence in medicine
Background:
- Clinical need for improved kidney cancer diagnosis and prognostication.
- Current limitations in traditional imaging interpretation.
- Emerging role of computational methods in oncology.
Purpose of the Study:
- To highlight the potential of radiomics and deep learning in kidney cancer.
- To discuss the application of AI in renal cell carcinoma diagnosis and treatment.
- To explore AI's role in personalized medicine for kidney cancer.
Main Methods:
- Application of radiomics to medical imaging.
- Utilizing deep learning algorithms for image analysis.
- Correlation of imaging features with clinical data and biomarkers.
Main Results:
- Radiomics and deep learning show promise in tumor segmentation, classification, staging, and grading.
- These methods aid in assessing preoperative scores.
- Potential for correlating imaging findings with tumor biomarkers.
Conclusions:
- AI and radiomics offer advanced tools for kidney cancer management.
- Personalized medicine approaches for renal cell carcinoma are advancing.
- Imaging-based AI is key to future diagnostic and prognostic accuracy.
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