A practical 10-step recipe for conducting radiomic studies.

Area of Science:

• Medical Imaging
• Computational Pathology
• Radiology

Background:

• Radiomics transforms medical images into quantitative data for disease diagnosis and treatment planning.
• It plays a crucial role in precision medicine by uncovering subtle imaging patterns.
• Current adoption is hindered by a lack of standardization, reproducibility issues, and clinical implementation challenges.

Purpose of the Study:

• To propose a practical 10-step framework for conducting effective radiomic studies.
• To address key challenges in standardization, reproducibility, and clinical implementation of radiomics.
• To guide researchers in aligning radiomic studies with clinical needs and promoting best practices.

Main Methods:

• A structured 10-step approach is detailed, covering study design to clinical translation.
• Steps include identifying clinical needs, establishing databases, quality assurance, segmentation, feature extraction, selection, data integration, model construction, performance evaluation, and clinical workflow integration.
• Emphasis is placed on research rationale, robust methodologies, and advanced computational techniques.

Main Results:

• The proposed framework provides a clear roadmap for researchers entering the radiomics field.
• It emphasizes critical aspects such as data quality, feature selection, and machine learning integration.
• The guide aims to enhance the reliability and reproducibility of radiomic studies.

Conclusions:

• The 10-step recipe offers a structured approach to overcome radiomics implementation barriers.
• This framework promotes standardization and reproducibility, facilitating the transition of radiomics into clinical practice.
• Adherence to this guide can lead to more personalized and effective patient care through precision imaging.