Maurice M Heimer, Yevgeniy Dikhtyar, Boj F Hoppe, Felix L Herr, Anna Theresa Stüber, Tanja Burkard, Emma Zöller, Matthias P Fabritius, Lena Unterrainer, Lisa Adams, Annette Thurner, David Kaufmann, Timo Trzaska, Markus Kopp, Okka Hamer, Katharina Maurer, Inka Ristow, Matthias S May, Amanda Tufman, Judith Spiro, Matthias Brendel, Michael Ingrisch, Jens Ricke, Clemens C Cyran
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Additionally, participants were surveyed on their experience with SR and TNM classification.</p><p><strong>Results: </strong>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 (p = 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.</p><p><strong>Conclusion: </strong>This multi-center study yielded an effective software-assisted SR framework for NSCLC. 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引用次数: 0
摘要
研究目的在这项多中心研究中，我们提出了非小细胞肺癌（NSCLC）的结构化报告（SR）框架，并开发了一种软件辅助工具，用于将基于图像的检查结果和注释自动转化为 TNM 分类。本研究的目的是验证软件辅助的非小细胞肺癌SR工具，在概念验证研究中评估其潜在的临床影响，并评估参与研究机构的现行报告标准：方法：多中心合作开发了 NSCLC SR 和分期框架。SR注释和描述用于生成半自动TNM分类。九位放射科医生对 n = 20 项有代表性的 [18F]FDG PET/CT 研究对 SR 和 TNM 分类工具进行了评估，并与自由文本报告 (FTR) 策略进行了比较。使用广义线性混合模型 (GLMM) 将结果与多学科团队参考进行比较。此外，还调查了参与者在SR和TNM分类方面的经验：总体而言，GLMM 分析显示，与 FTR 策略相比，使用 SR 的读者正确分类 TNM 状态的可能性要高出 1.707（CI：1.137-2.585）倍（p = 0.01），因此与 62.8%（113/180）的 FTR 相比，71.9%（128/178）的病例总体 TNM 正确率有所提高。病例复杂性是造成分类准确性差异的主要原因。参与者对SR和半自动TNM分类的潜在影响的评价在所有类别中都是积极的，并在模板验证后提高了分数：这项多中心研究为 NSCLC 提供了一个有效的软件辅助 SR 框架。SR和半自动分类工具改善了TNM分类，并被认为是有价值的：软件辅助 SR 为非小细胞肺癌（NSCLC）基于规则的 TNM 半自动分类提供了可靠的输入，与 FTR 相比提高了 TNM 的正确性，并被放射科医生认为是有价值的：要点：在参与研究的中心中，SR 和 TNM 分级在 NSCLC 分期中的应用不足。软件辅助 SR 已成为一种有前途的肿瘤评估策略。与自由文本报告相比，软件辅助SR有助于半自动TNM分类，提高了NSCLC分期的准确性。
Software-assisted structured reporting and semi-automated TNM classification for NSCLC staging in a multicenter proof of concept study.
Objectives: 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.
Methods: 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 n = 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.
Results: 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 (p = 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.
Conclusion: 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.
Critical relevance statement: 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.
Key points: SR and TNM classification are underutilized across participating centers for NSCLC staging. Software-assisted SR has emerged as a promising strategy for oncologic assessment. Software-assisted SR facilitates semi-automated TNM classification with improved staging accuracy compared to free-text reports in NSCLC.
期刊介绍：
Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere!
I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe.
Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy.
A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field.
I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly.
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The journal went open access in 2012, which means that all articles published since then are freely available online.
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