Heimer, Maurice M. and Dikhtyar, Yevgeniy and Hoppe, Boj F. and Herr, Felix L. and Stueber, Anna Theresa and Burkard, Tanja and Zoeller, Emma and Fabritius, Matthias P. and Unterrainer, Lena and Adams, Lisa and Thurner, Annette and Kaufmann, David and Trzaska, Timo and Kopp, Markus and Hamer, Okka and Maurer, Katharina and Ristow, Inka and May, Matthias S. and Tufman, Amanda and Spiro, Judith and Brendel, Matthias and Ingrisch, Michael and Ricke, Jens and Cyran, Clemens C. (2024) Software-assisted structured reporting and semi-automated TNM classification for NSCLC staging in a multicenter proof of concept study. INSIGHTS INTO IMAGING, 15 (1): 258. ISSN 1869-4101
Full text not available from this repository. (Request a copy)Abstract
ObjectivesIn 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.MethodsA 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.ResultsOverall, 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.ConclusionThis 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 statementSoftware-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 PointsSR 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CLINICAL STAGE; RELIABILITY; Lung; Non-small-cell lung carcinoma; PET-CT; TNM classification |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Röntgendiagnostik |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 04 Dec 2025 05:31 |
| Last Modified: | 04 Dec 2025 05:31 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65434 |
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