Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

Autor: Liu BC; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China., Ma HY; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China., Huang J; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China., Luo YW; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China., Zhang WB; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China., Deng WW; Clinical & Technical Support, Philips Healthcare, Shanghai, People's Republic of China., Liao YT; Clinical & Technical Support, Philips Healthcare, Shanghai, People's Republic of China., Xie CM; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China. xiechm@sysucc.org.cn., Li Q; State Key Laboratory of Oncology in South China, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China. liqiong@sysucc.org.cn.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2024 Jun; Vol. 34 (6), pp. 4176-4186. Date of Electronic Publication: 2023 Nov 17.
DOI: 10.1007/s00330-023-10440-6
Abstrakt: Objectives: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma.
Methods: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models.
Results: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model.
Conclusions: DLCT has added value for STAS prediction in lung adenocarcinoma.
Clinical Relevance Statement: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future.
Key Points: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.
(© 2023. The Author(s), under exclusive licence to European Society of Radiology.)
Databáze: MEDLINE
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