Evidence map›Paper›PMID 41602202›Full record

ReviewFrontiers in radiology2025

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

Michele Avanzo, Paolo Soda, Marco Bertolini, Andrea Bettinelli, Tiziana Rancati, Joseph Stancanello, Osvaldo Rampado, Giovanni Pirrone, Annalisa Drigo

Abstract readReview
In one paragraph

Review in Frontiers in radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Michele AvanzoMedical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy.
Paolo SodaDepartment of Engineering, University Campus Biomedico of Rome, Rome, Italy.
Marco BertoliniMedical Physics Department, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia: Reggio Emilia, Emilia-Romagna, Italy.
Andrea BettinelliMedical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy.
Tiziana RancatiData Science Unit, Department of Epidemiology and Data Science, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
Joseph StancanelloElekta SA, Boulogne-Billancourt, France.
Osvaldo RampadoMedical Physics Unit, A.O.U. Città Della Salute e Della Scienza di Torino, Torino, Italy.
Giovanni PirroneMedical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy.
Annalisa DrigoMedical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 compliance: 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, that can guide the research community in designing and conducting radiomic studies that result in an imaging biomarker suitable for clinical practice.

Indexed as

artificial intelligenceguidelines & recommendationsmachine learningmedical imagingradiomics

Identifiers

PMID41602202
PMCPMC12833238

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.