Robust Radiomics: a review of guidelines for radiomics in medical imaging

Michele Avanzo^1*Paolo Soda²Marco Bertolini³Andrea Bettinelli⁴Tiziana Rancati⁵Joseph Stancanello⁶Osvaldo Rampado⁷Giovanni Pirrone¹Annalisa Drigo¹

• ¹Medical Physics Department, Centro di Riferimento Oncologico IRCCS, Aviano, Italy
• ²Universita Campus Bio-Medico di Roma, Rome, Italy
• ³Azienda Unita Sanitaria Locale - IRCCS Tecnologie Avanzate e Modelli Assistenziali in Oncologia di Reggio Emilia, Reggio Emilia, Italy
• ⁴Istituto Oncologico Veneto IRCCS, Padua, Italy
• ⁵Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
• ⁶Elekta AB, Stockholm, Sweden
• ⁷Azienda Ospedaliero Universitaria Citta della Salute e della Scienza di Torino, Turin, Italy

Introduction: Radiomics aims to develop image-based biomarkers by combining quantitative analysis of medical images with artificial intelligence (AI through a robust. reproducible pipeline. Scientific societies, task groups, and consortia have published several guidelines to help researchers design robust radiomics studies. This review summarizes existing guidelines, recommendations, and regulations for designing radiomics studies that can lead to clinically adoptable biomarkers. Methods: Relevant articles were identified through a PubMed systematic review using 'radiomics' and 'guideline' as keywords. Of 314 retrieved papers, after screening 99 articles were deemed relevant for extracting recommendations on developing image-based biomarkers. Additional guidelines were searched by the authors. Results: We can synthesize the systematic review in the following high consensus recommendations divided into five major areas: a) Study Design: Carefully define the study rationale, objectives, and outcomes, ensuring the dataset is of adequate size and quality; b) Data Workflow: Use standardized protocols for image acquisition, reconstruction, preprocessing, and feature extraction—following IBSI guidelines where applicable; c) Model Development and Validation: Follow best practices for model development, including prevention of data leakage, dimensionality reduction, strategies to enhance model interpretability, and establish biological plausibility; d) Transparency and Reproducibility: Publish results with sufficient methodological details to ensure rigor and generalizability and promote open science by sharing codes and data; e) Quality and Complianc: Evaluate study compliance with relevant guidelines and regulations using appropriate quality metrics. Conclusion: Radiomics promises to offer clinically useful imaging biomarkers and can represent a significant step in personalized medicine. In the present systematic review we identified five key guidelines and regulations developed in recent years specifically for radiomics or AI can guide the research community in designing and conducting radiomic studies that result in a biomarker suitable for clinical practice..