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

• Published in: Insights into imaging Vol. 15; no. 1; pp. 258 - 10
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 28.10.2024; Springer Nature B.V; SpringerOpen
• Subjects: Annotations; Automation; Classification; Diagnostic Radiology; Evaluation; Imaging; Internal Medicine; Interventional Radiology; Lung; Medicine; Medicine & Public Health; Neuroradiology; Non-small-cell lung carcinoma; Original; Original Article; PET-CT; Radiology; Software; Statistical models; TNM classification; Ultrasound; PET-CT; TNM classification; Lung; Non-small-cell lung carcinoma
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-024-01836-z

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. Graphical Abstract