Software-assisted structured reporting and semi-automated TNM classification for NSCLC staging in a multicenter proof of concept study

Heimer, Maurice M.; Dikhtyar, Yevgeniy; Hoppe, Boj F.; Herr, Felix L.; Stüber, Anna Theresa; Burkard, Tanja; Zöller, Emma; Fabritius, Matthias P.; Unterrainer, Lena; Adams, Lisa; Thurner, Annette; Kaufmann, David; Trzaska, Timo; Kopp, Markus; Hamer, Okka; Maurer, Katharina; Ristow, Inka; May, Matthias S.; Tufman, Amanda; Spiro, Judith; Brendel, Matthias; Ingrisch, Michael; Ricke, Jens; 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: 258. ISSN 1869-4101

Abstract

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.
doi.org/10.1016/j.chest.2024.05.026

• Dokumententyp: Artikel (Klinikum der LMU)
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198