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1000
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Abstract/Summary
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<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Objective</jats:title>
<jats:p>To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (<jats:italic>n </jats:italic>= 23) or soft-tissue (<jats:italic>n</jats:italic> = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage.</jats:p>
</jats:sec><jats:sec>
<jats:title>Critical relevance statement</jats:title>
<jats:p>An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage.</jats:p>
</jats:sec><jats:sec>
<jats:title>Key points</jats:title>
<jats:p>• 2021–2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed.</jats:p>
<jats:p>• Feature reproducibility was assessed in more than half (59%) of the studies.</jats:p>
<jats:p>• Model clinical validation was performed in 69% of the studies.</jats:p>
<jats:p>• Internal (44%) and/or external (29%) test datasets were employed for clinical validation.</jats:p>
</jats:sec><jats:sec>
<jats:title>Graphical Abstract</jats:title>
</jats:sec>
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1000
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Hinweis
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DeepGreen-ID: 01b5ff14a63741139c4439edc305a471 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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