Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Area of Science:

• Medical Imaging Analysis
• Quantitative Data Analytics
• Computational Medicine

Background:

• Medical image processing traditionally requires multidisciplinary collaboration.
• Radiomics leverages quantitative data analytics for medical image feature extraction.
• Radiomics offers scalability, efficiency, and precision in medical data analysis.

Purpose of the Study:

• To review selected learning methods significant for radiomics.
• To explore the potential of advanced algorithms in radiomics.
• To highlight the role of deep learning in quantitative medical imaging.

Main Methods:

• Review of statistical and machine learning algorithms in radiomics.
• Focus on deep learning as a leading inference approach.
• Consideration of hybrid learning for integrative approaches.

Main Results:

• Radiomics relies on sophisticated algorithms for optimization.
• Deep learning is a prominent method in radiomics.
• Hybrid learning approaches can improve classification and prediction accuracy.

Conclusions:

• Radiomics significantly supports diagnostic, prognostic, and therapeutic decisions.
• Advanced machine learning, particularly deep learning, is crucial for radiomics.
• Further exploration of learning methods can unlock greater potential in medical image analysis.