Radiomics in Oncology: A Practical Guide.

Area of Science:

• Medical imaging analysis
• Computational oncology
• Precision medicine

Background:

• Radiomics extracts mineable data from medical images for oncology applications.
• It aims to enhance diagnosis, prognostication, and clinical decision support for personalized medicine.

Purpose of the Study:

• To provide a practical, step-by-step guide for implementing radiomic workflows.
• To cover the entire process from planning and conceptualization to manuscript writing.

Main Methods:

• The radiomic workflow involves tumor segmentation, image preprocessing, feature extraction, model development, and validation.
• Multidisciplinary collaboration between radiologists and data/imaging scientists is crucial.
• Techniques include classification and time-to-event analysis for clinical event prediction.

Main Results:

• Radiomic analysis can improve diagnostic accuracy (e.g., benign vs. malignant).
• Feature selection and robust validation (cross-validation, hold-out datasets) are key to reduce overfitting and ensure generalizability.
• Software applications and AI checklists can aid implementation.

Conclusions:

• Successful radiomic workflow implementation requires careful planning and execution.
• Radiologists must understand potential pitfalls for reliable conclusions in radiomic studies.
• Radiomics offers a powerful approach to advancing precision oncology.