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Abstract/Summary
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<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Objectives</jats:title>
<jats:p>In this multi-center study, we proposed a structured reporting (SR) framework for non-small cell lung cancer (NSCLC) and developed a software-assisted tool to automatically translate image-based findings and annotations into TNM classifications. The aim of this study was to validate the software-assisted SR tool for NSCLC, assess its potential clinical impact in a proof-of-concept study, and evaluate current reporting standards in participating institutions.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>A framework for SR and staging of NSCLC was developed in a multi-center collaboration. SR annotations and descriptions were used to generate semi-automated TNM classification. The SR and TNM classification tools were evaluated by nine radiologists on <jats:italic>n</jats:italic> = 20 representative [18F]FDG PET/CT studies and compared to the free text reporting (FTR) strategy. Results were compared to a multidisciplinary team reference using a generalized linear mixed model (GLMM). Additionally, participants were surveyed on their experience with SR and TNM classification.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>Overall, GLMM analysis revealed that readers using SR were 1.707 (CI: 1.137–2.585) times more likely to correctly classify TNM status compared to FTR strategy (<jats:italic>p</jats:italic> = 0.01) resulting in increased overall TNM correctness in 71.9% (128/178) of cases compared to 62.8% (113/180) FTR. The primary source of variation in classification accuracy was explained by case complexity. Participants rated the potential impact of SR and semi-automated TNM classification as positive across all categories with improved scores after template validation.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>This multi-center study yielded an effective software-assisted SR framework for NSCLC. The SR and semi-automated classification tool improved TNM classification and were perceived as valuable.</jats:p>
</jats:sec><jats:sec>
<jats:title>Critical relevance statement</jats:title>
<jats:p>Software-assisted SR provides robust input for semi-automated rule-based TNM classification in non-small-cell lung carcinoma (NSCLC), improves TNM correctness compared to FTR, and was perceived as valuable by radiology physicians.</jats:p>
</jats:sec><jats:sec>
<jats:title>Key Points</jats:title>
<jats:p><jats:list list-type='bullet'>
<jats:list-item>
<jats:p>SR and TNM classification are underutilized across participating centers for NSCLC staging.</jats:p>
</jats:list-item>
<jats:list-item>
<jats:p>Software-assisted SR has emerged as a promising strategy for oncologic assessment.</jats:p>
</jats:list-item>
<jats:list-item>
<jats:p>Software-assisted SR facilitates semi-automated TNM classification with improved staging accuracy compared to free-text reports in NSCLC.</jats:p>
</jats:list-item>
</jats:list></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: 06802d02260b4457a9b4d208a2ef27ed ; 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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