Radiomics in Medical Imaging: Methods, Applications, and Challenges

Area of Science:

• Medical Imaging
• Radiomics
• Quantitative Analysis

Background:

• Radiomics offers quantitative medical image analysis for predictive modeling.
• Despite progress, radiomics faces challenges in feature stability, reproducibility, and clinical translation.
• Current reviews often overlook how interdependent design choices impact radiomics robustness.

Purpose of the Study:

• To provide an end-to-end analysis of radiomics pipelines.
• To examine how methodological decisions influence feature stability, model reliability, and translational validity.
• To identify challenges and future directions for radiomics standardization and clinical deployment.

Main Methods:

• Review of radiomic feature extraction, selection, and dimensionality reduction.
• Analysis of classical machine learning and deep learning-based modeling approaches.
• Emphasis on validation protocols, data leakage prevention, and statistical reliability.

Main Results:

• Methodological decisions across the radiomics pipeline significantly impact feature stability and model reliability.
• Evaluation rigor in clinical applications is crucial for translational validity.
• Open challenges include standardization, domain shift, and clinical deployment.

Conclusions:

• Addressing interdependent design choices in radiomics pipelines is key to enhancing robustness and generalizability.
• Future directions include hybrid models, multimodal fusion, federated learning, and standardized benchmarking.
• Improving radiomics reliability is essential for successful clinical translation.