Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology

Koçak, Burak; Chepelev, Leonid L.; Chu, Linda C.; Cuocolo, Renato; Kelly, Brendan; Seeböck, Philipp; Thian, Yee Liang; Hamersvelt, Robbert W. van; Wang, A.; Williams, S. M.; Witowski, Jan; Zhang, Zhongyi; Santos, Daniel Pinto dos

doi:10.1007/s00330-023-09768-w

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…In addition, we provide supplement 3 , a mapping table between the consensus definition and analyzed literature terms to support the translation between terms used in different publications. We included the radiomics standardization guidelines ARISE [ 10 ], CLEAR [ 9 ], IBSI [ 8 ], and RQS [ 7 ] in this mapping and observed that only the phase “Feature extraction” is represented in all four guidelines. On average 3 of these 4 guidelines are mapping to aspects of the seven defined consensus phases, but no aspect is covered by all of the guidelines.…”

Section: Resultsmentioning

confidence: 99%

“…Most analyzed papers include aspects of the defined consensus phases “Modelling” (96%, 23 publications) and “Feature extraction” (92%, 22 publications). Looking only at well-known radiomics standardization guidelines (ARISE [ 10 ], CLEAR [ 9 ], IBSI [ 8 ], and RQS [ 7 ]) the phase coverage overlap improves (all phases are covered to some extent by at least 3 guidelines; “Feature extraction” is covered by all). But even in this focused set of analyzed literature, the lack of definition overlap becomes evident as none of the consensus aspects is covered by all 4 guidelines.…”

Section: Discussionmentioning

confidence: 99%

“…9 TranslaTUM -Central Institute for Translational Cancer Research, TU Munich, Munich, Germany. 10 Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany. 11 Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…The lack of translation of radiomics research into practical clinical applications can be attributed to various factors and still exists, despite existing initiatives such as the image biomarker standardization initiative (IBSI) [ 8 ], Radiomics Quality Score (RQS) [ 7 ], Quantitative Imaging Biomarker Alliance (QIBA; https://www.rsna.org/research/quantitative-imaging-biomarkers-alliance ), CheckList for Evaluation of Radiomics Research (CLEAR) [ 9 ], Assessment of Radiomics research (ARISE) [ 10 ], and guideline framing. Some studies, like CLEAR and ARISE, provide essential checklists aimed at ensuring thorough and reproducible reporting in radiomics research.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics

Floca,

Bohn,

Haux

et al. 2024

Insights Imaging

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Objectives Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. Materials and methods The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. Results Workflow definitions from 22 publications (published 2012–2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts “agree” or “strongly agree”). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts’ perspective the ten most important challenges in radiomics workflows. Conclusion To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. Critical relevance statement Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. Key Points Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community. Graphical Abstract

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Supplementary Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics

Floca,

Bohn,

Haux

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

show abstract

“…We opted not to include European Radiology , another journal of a similar nature representing Europe. This decision was made to prevent potential skewing of results, as it had a significantly higher publication volume and was known to publish AI studies more prominently [ 38 ]. Acknowledging the vastness of AI literature, focusing on these two specific journals may offer a more practical approach and provide pilot data—even though a comprehensive analysis was not feasible.…”

Section: Methodsmentioning

confidence: 99%

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Jhang,

Park,

Sul

et al. 2024

Korean J Radiol

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Objective This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. Materials and Methods We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1–10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. Results Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%–93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements ( P ≤ 0.036). Conclusion There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.

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Reproducible Radiomics Features from Multi‐MRI‐Scanner Test–Retest‐Study: Influence on Performance and Generalizability of Models

Wennmann,

Rotkopf,

Bauer

et al. 2024

Magnetic Resonance Imaging

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BackgroundRadiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large‐scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test–retest experiments, which might help to overcome the problem of limited generalizability to external data.PurposeTo evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi‐MRI‐scanner test–retest‐study, on the performance and generalizability of radiomics models.Study TypeRetrospective.PopulationPatients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2–8).Field Strength/Sequence1.5T and 3.0T; T1‐weighted turbo spin echo.AssessmentThe task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2–8).Statistical TestsPearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z‐transformation, Wilcoxon signed‐rank test, Wilcoxon rank‐sum test; significance level P < 0.05.ResultsWhen using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10).ConclusionUsing only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model.Level of Evidence3.Technical EfficacyStage 2.

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Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology

Cited by 12 publications

References 24 publications

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

Reproducible Radiomics Features from Multi‐MRI‐Scanner Test–Retest‐Study: Influence on Performance and Generalizability of Models

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