An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Quality of radiomics research: comprehensive analysis of 1574 unique publications from 89 reviews

PURPOSE

This study aims to comprehensively evaluate the quality of radiomics research by examining unique papers from reviews using the radiomics quality score (RQS).

METHODS

A literature search was conducted in PubMed (last search date: April 14, 2024). Systematic or non-systematic reviews using the RQS to evaluate radiomic studies were potentially included. Exclusion was applied at two levels: first, at the review level, and second, at the study level (i.e., for the individual articles previously evaluated within the reviews). Score-wise and item-wise analyses were performed, along with trend, multivariable, and subgroup analyses based on baseline study characteristics and validation methods.

RESULTS

A total of 1574 unique papers (published online between 1999 and 2023) from 89 reviews were included in the final analysis. The median RQS percentage was 31% with an IQR of 25% (25th-75th percentiles, 14-39%). A positive correlation between median RQS percentage and publication year (2014-2023) was found, with Kendall's tau coefficient of 0.908 (p < 0.001), suggesting an improvement in quality over time. The quality of radiomics publications significantly varied according to different subfields of radiology (p < 0.001). Around one-third of the publications (32%) lacked a separate validation set. Papers with internal validation (54%) dominated those with external validation (14%). Higher-quality validation practices were significantly associated with better RQS percentage scores, independent of the validation's effect on the final score. Item-wise analysis revealed significant shortcomings in several areas.

CONCLUSION

Radiomics research quality is low but improving according to RQS. Significant variation exists across radiology subfields. Critical areas were identified for targeted improvement.

CLINICAL RELEVANCE STATEMENT

Our study shows that the quality of radiomics research is generally low but improving over time, with item-wise analysis highlighting critical areas needing improvement. It also reveals that the quality of radiomics research differs across subfields and validation methods.

KEY POINTS

Overall quality of radiomics research remains low and highly variable, although a significant positive trend suggests an improvement in quality over time. Considerable variations exist in the quality of radiomics publications across different subfields of radiology and validation types. The item-wise analysis highlights several critical areas requiring attention, emphasizing the need for targeted improvements.

ChatGPT as an effective tool for quality evaluation of radiomics research

OBJECTIVES

This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.

METHODS

Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.

RESULTS

In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840-0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756-0.919) and 0.914 (95% CI: 0.855-0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9-3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.

CONCLUSION

ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.

KEY POINTS

Question Is ChatGPT effective and reliable in evaluating radiomics research quality based on RQS? Findings ChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment. Clinical relevance ChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.

Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes

OBJECTIVES

To investigate how studies determine the sample size when developing radiomics prediction models for binary outcomes, and whether the sample size meets the estimates obtained by using established criteria.

METHODS

We identified radiomics studies that were published from 01 January 2023 to 31 December 2023 in seven leading peer-reviewed radiological journals. We reviewed the sample size justification methods, and actual sample size used. We calculated and compared the actual sample size used to the estimates obtained by using three established criteria proposed by Riley et al. We investigated which characteristics factors were associated with the sufficient sample size that meets the estimates obtained by using established criteria proposed by Riley et al. RESULTS: We included 116 studies. Eleven out of one hundred sixteen studies justified the sample size, in which 6/11 performed a priori sample size calculation. The median (first and third quartile, Q1, Q3) of the total sample size is 223 (130, 463), and those of sample size for training are 150 (90, 288). The median (Q1, Q3) difference between total sample size and minimum sample size according to established criteria are -100 (-216, 183), and those differences between total sample size and a more restrictive approach based on established criteria are -268 (-427, -157). The presence of external testing and the specialty of the topic were associated with sufficient sample size.

CONCLUSION

Radiomics studies are often designed without sample size justification, whose sample size may be too small to avoid overfitting. Sample size justification is encouraged when developing a radiomics model.

KEY POINTS

Question Sample size justification is critical to help minimize overfitting in developing a radiomics model, but is overlooked and underpowered in radiomics research. Findings Few of the radiomics models justified, calculated, or reported their sample size, and most of them did not meet the recent formal sample size criteria. Clinical relevance Radiomics models are often designed without sample size justification. Consequently, many models are too small to avoid overfitting. It should be encouraged to justify, perform, and report the considerations on sample size when developing radiomics models.

Large language models in methodological quality evaluation of radiomics research based on METRICS: ChatGPT vs NotebookLM vs radiologist

OBJECTIVES

This study aimed to evaluate the effectiveness of large language models (LLM) in assessing the methodological quality of radiomics research, using METhodological RadiomICs Score (METRICS) tool.

METHODS

This study included open access radiomic research articles published in 2024 across various journals and a preprint repository, all under the Creative Commons Attribution License. Each study was independently evaluated using METRICS by two LLMs, ChatGPT-4 and NotebookLM, and a consensus assessment performed by two radiologists with expertise in radiomics research.

RESULTS

A total of 48 open access articles were included in this study. ChatGPT-4, NotebookLM, and human readers achieved median scores of 79.5 %, 61.6 %, and 69.0 %, respectively, with a statistically significant difference across these evaluations (p < 0.05). Pairwise comparisons indicated no statistically significant difference for NotebookLM vs human experts (p > 0.05), in contrast to other pairs (p < 0.05). Intraclass correlation coefficient (ICC) for ChatGPT-4 and human experts was 0.563 (95 % CI: 0.050---0.795), corresponding to poor to good agreement. The ICC for ChatGPT-4 and NotebookLM and for human experts and NotebookLM were 0.391 (95 % CI: -0.031---0.665) and 0.555 (95 % CI: 0.326---0.723), respectively, indicating poor to moderate agreement. LLMs completed the tasks in a significantly shorter time (p < 0.05). In item-wise reliability analysis, ChatGPT-4 generally demonstrated higher consistency than NotebookLM.

CONCLUSION

LLMs hold promise for automatically evaluating the quality of radiomics research using METRICS, a new tool that is relatively more complex yet comprehensive compared to its counterparts. However, substantial improvements are needed for full alignment with human experts.

Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation

PURPOSE

PET/CT imaging data contains a wealth of quantitative information that can provide valuable contributions to characterising tumours. A growing body of work focuses on the use of deep-learning (DL) techniques for denoising PET data. These models are clinically evaluated prior to use, however, quantitative image assessment provides potential for further evaluation. This work uses radiomic features to compare two manufacturer deep-learning (DL) image enhancement algorithms, one of which has been commercialised, against 'gold-standard' image reconstruction techniques in phantom data and a sarcoma patient data set (N=20).

METHODS

All studies in the retrospective sarcoma clinical [18 F]FDG dataset were acquired on either a GE Discovery 690 or 710 PET/CT scanner with volumes segmented by an experienced nuclear medicine radiologist. The modular heterogeneous imaging phantom used in this work was filled with [18 F]FDG, and five repeat acquisitions of the phantom were acquired on a GE Discovery 710 PET/CT scanner. The DL-enhanced images were compared to 'gold-standard' images the algorithms were trained to emulate and input images. The difference between image sets was tested for significance in 93 international biomarker standardisation initiative (IBSI) standardised radiomic features.

RESULTS

Comparing DL-enhanced images to the 'gold-standard', 4.0% and 9.7% radiomic features measured significantly different (pcritical < 0.0005) in the phantom and patient data respectively (averaged over the two DL algorithms). Larger differences were observed comparing DL-enhanced images to algorithm input images with 29.8% and 43.0% of radiomic features measuring significantly different in the phantom and patient data respectively (averaged over the two DL algorithms).

CONCLUSION

DL-enhanced images were found to be similar to images generated using the 'gold-standard' target image reconstruction method with more than 80% of radiomic features not significantly different in all comparisons across unseen phantom and sarcoma patient data. This result offers insight into the performance of the DL algorithms, and demonstrate potential applications for DL algorithms in harmonisation for radiomics and for radiomic features in quantitative evaluation of DL algorithms.

Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII

OBJECTIVES

To investigate the intra- and inter-rater reliability of the total methodological radiomics score (METRICS) and its items through a multi-reader analysis.

MATERIALS AND METHODS

A total of 12 raters with different backgrounds and experience levels were recruited for the study. Based on their level of expertise, raters were randomly assigned to the following groups: two inter-rater reliability groups, and two intra-rater reliability groups, where each group included one group with and one group without a preliminary training session on the use of METRICS. Inter-rater reliability groups assessed all 34 papers, while intra-rater reliability groups completed the assessment of 17 papers twice within 21 days each time, and a "wash out" period of 60 days in between.

RESULTS

Inter-rater reliability was poor to moderate between raters of group 1 (without training; ICC = 0.393; 95% CI = 0.115-0.630; p = 0.002), and between raters of group 2 (with training; ICC = 0.433; 95% CI = 0.127-0.671; p = 0.002). The intra-rater analysis was excellent for raters 9 and 12, good to excellent for raters 8 and 10, moderate to excellent for rater 7, and poor to good for rater 11.

CONCLUSION

The intra-rater reliability of the METRICS score was relatively good, while the inter-rater reliability was relatively low. This highlights the need for further efforts to achieve a common understanding of METRICS items, as well as resources consisting of explanations, elaborations, and examples to improve reproducibility and enhance their usability and robustness.

KEY POINTS

Questions Guidelines and scoring tools are necessary to improve the quality of radiomics research; however, the application of these tools is challenging for less experienced raters. Findings Intra-rater reliability was high across all raters regardless of experience level or previous training, and inter-rater reliability was generally poor to moderate across raters. Clinical relevance Guidelines and scoring tools are necessary for proper reporting in radiomics research and for closing the gap between research and clinical implementation. There is a need for further resources offering explanations, elaborations, and examples to enhance the usability and robustness of these guidelines.

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study

OBJECTIVES

To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.

METHODS

A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).

RESULTS

The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.

CONCLUSION

The radiomics features were non-reproducible with significant variability in values among different CT techniques.

CLINICAL RELEVANCE STATEMENT

Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.

KEY POINTS

CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors

OBJECTIVES

To assess the impact of acquisition and reconstruction factors on the robustness of radiomics within photon-counting detector CT (PCD-CT).

METHODS

A phantom with twenty-eight texture materials was scanned with different acquisition and reconstruction factors including reposition, scan mode (standard vs high-pitch), tube voltage (120 kVp vs 140 kVp), slice thickness (1.0 mm vs 0.4 mm), radiation dose level (0.5 mGy, 1.0 mGy, 3.0 mGy, 5.0 mGy, vs 10.0 mGy), quantum iterative reconstruction level (0/4, 2/4, vs 4/4), and reconstruction kernel (Qr40, Qr44, vs Qr48). Thirteen sets of virtual monochromatic images at 70-keV were reconstructed. The regions of interest were drawn with rigid registrations. Ninety-three radiomics features were extracted from each material. The reproducibility of radiomics features was evaluated using the intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The variability of radiomics features was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).

RESULTS

The percentage of features with ICC > 0.90 and CCC > 0.90 were high when repositioned (88.2% and 88.2%) and tube voltage was changed (87.1% and 87.1%), but none of the features with ICC > 0.90 and CCC > 0.90 when high-pitch scan and different slice thickness were used. The percentage of features with CV < 10% and QCD < 10% were high when repositioned (47.3% and 68.8%) and tube voltage was changed (64.2% and 71.0%), but that with CV < 10% and QCD < 10% were low between standard and high-pitch scans (16.1% and 26.9%) and slice thickness (19.4% and 29.0%).

CONCLUSIONS

The PCD-CT radiomics was robust to tube voltage, radiation dose, reconstruction strength level, and kernel, but brittle to high-pitch scan and slice thickness.

KEY POINTS

Question The stability of radiomics features against acquisition and reconstruction factors within PCD-CT should be fully determined before academic research and clinical application. Findings The radiomics features are robust against tube voltage, radiation dose, reconstruction strength level, and kernel within PCD-CT but brittle to high-pitch scan and slice thickness. Clinical relevance The high-pitch scan and slice thickness that influence voxel size should be set with careful attention within PCD-CT, to allow a higher robustness of radiomics features before the implementation of radiomics analysis in clinical routine.

Enhancing Reporting Quality Using the Preferred Reporting Items for Systematic Review and Meta-Analysis 2020 in Systematic Reviews of Emergency Medicine Journals: A Cross-Sectional Study

BACKGROUND

Systematic reviews (SRs) with thorough reporting and rigorous methodology lead to less biased outcomes. The Priority Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was developed to enhance SRs and meta-analysis reporting. While it was updated to PRISMA 2020, the impact on emergency medicine remains unexplored. Therefore, we aimed to investigate whether using PRISMA 2020 improves the reporting quality of SRs in the emergency medicine field.

METHODS

This study is a cross-sectional meta-epidemiological analysis of SRs published in emergency medicine journals between 2021 and 2023. We selected SRs with pairwise meta-analyses of health interventions included in MEDLINE. We evaluated adherence to PRISMA 2020 items with and without the use of the PRISMA 2020 statement.

RESULTS

A total of 695 articles were analyzed, ultimately including 31 that used PRISMA 2020 and 100 that did not. Adherence rates to PRISMA 2020 items were higher in papers using PRISMA 2020 (925/1270, 72.8%, odds ratio: 1.24, 95% confidence interval: 1.08-1.43) than in papers not using it (2758/4034, 68.4%). No SRs met all of the PRISMA 2020 criteria. Adherence to PRISMA 2020 for Abstracts was slightly higher in the group that used the PRISMA 2020 (182/372, 48.9%, odds ratio: 1.17, 95% confidence interval: 0.92-1.47), compared to those that had not (541/1200, 45.1%). Adherence was highest in the introduction and lowest in the methods section. Agreement between the first author and other reviewers' ratings averaged 89.6% (4815/5371).

CONCLUSION

Implementing PRISMA 2020 significantly improved the reporting quality of SRs in emergency medicine-related journals. Declaring the use of PRISMA 2020 is insufficient, and researchers must strictly adhere to each item.

REGISTRATION

The study protocol was registered in the Open Science Framework on September 22, 2023 (https://doi.org/10.17605/OSF.IO/DQ5W6).

Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies

Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)-a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation.

Self-reported checklists and quality scoring tools in radiomics: a meta-research

OBJECTIVE

To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature.

METHODS

Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers.

RESULTS

The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of - 7.2 (standard deviation, 6.8).

CONCLUSION

Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable.

CLINICAL RELEVANCE STATEMENT

Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward.

KEY POINTS

• In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Just give the contrast? Appraisal of guidelines on intravenous iodinated contrast media use in patients with kidney disease

OBJECTIVE

To appraise the quality of guidelines on intravenous iodinated contrast media (ICM) use in patients with kidney disease, and to compare the recommendations among them.

METHODS

We searched four literature databases, eight guideline libraries, and ten homepages of radiological societies to identify English and Chinese guidelines on intravenous ICM use in patients with kidney disease published between January 2018 and June 2023. The quality of the guidelines was assessed with the Scientific, Transparent, and Applicable Rankings (STAR) tool.

RESULTS

Ten guidelines were included, with a median STAR score of 46.0 (range 28.5-61.5). The guidelines performed well in "Recommendations" domain (31/40, 78%), while poor in "Registry" (0/20, 0%) and "Protocol" domains (0/20, 0%). Nine guidelines recommended estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2 as the cutoff for referring patients to discuss the risk-benefit balance of ICM administration. Three guidelines further suggested that patients with an eGFR < 45 mL/min/1.73 m2 and high-risk factors also need referring. Variable recommendations were seen in the acceptable time interval between renal function test and ICM administration, and that between scan and repeated scan. Nine guidelines recommended to use iso-osmolar or low-osmolar ICM, while no consensus has been reached for the dosing of ICM. Nine guidelines supported hydration after ICM use, but their protocols varied. Drugs or blood purification therapy were not recommended as preventative means.

CONCLUSION

Guidelines on intravenous ICM use in patients with kidney disease have heterogeneous quality. The scientific societies may consider joint statements on controversial recommendations for variable timing and protocols.

CRITICAL RELEVANCE STATEMENT

The heterogeneous quality of guidelines, and their controversial recommendations, leave gaps in workflow timing, dosing, and post-administration hydration protocols of contrast-enhanced CT scans for patients with kidney diseases, calling for more evidence to establish a safer and more practicable workflow.

KEY POINTS

• Guidelines concerning iodinated contrast media use in kidney disease patients vary. • Controversy remains in workflow timing, contrast dosing, and post-administration hydration protocols. • Investigations are encouraged to establish a safer iodinated contrast media use workflow.