Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.

Autor:	Suh YJ; Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. rongzu@yuhs.ac., Han K; Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea., Kwon Y; Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea., Kim H; Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Korea., Lee S; Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea., Hwang SH; Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea., Kim MH; Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea., Shin HJ; Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea., Lee CY; Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea., Shim HS; Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Jazyk:	angličtina
Zdroj:	Yonsei medical journal [Yonsei Med J] 2024 Mar; Vol. 65 (3), pp. 163-173.
DOI:	10.3349/ymj.2023.0368
Abstrakt:	Purpose: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. Materials and Methods: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. Results: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p =0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model ( p =0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. Conclusion: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas. Competing Interests: The authors have no potential conflicts of interest to disclose. (© Copyright: Yonsei University College of Medicine 2024.)
Databáze:	MEDLINE
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