Preoperative CT-based peritumoral and tumoral radiomic features prediction for tumor spread through air spaces in clinical stage I lung adenocarcinoma. (January 2022)

• Record Type: Journal Article
• Authors: Liao, Guoqing; Huang, Luyu; Wu, Shaowei; Zhang, Peirong; Xie, Daipeng; Yao, Lintong; Zhang, Zhengjie; Yao, Su; Shanshan, Lyu; Wang, Siyun; Wang, Guangyi; Wing-Chi Chan, Lawrence; Zhou, Haiyu
• Is Part Of: Lung cancer. Volume 163(2022)
• Page Start: 87
• Page End: 95
• Publication Date: 2022-01
• Subjects: AIC Akaike information criterion -- AUC Area under the receiver operating characteristics curve -- CI Confidence interval -- DCA Decision curve analysis -- STAS Spread through air spaces -- GRS Optimal Gross Radiomic Signature -- TRS Tumor Radiomic Signature -- PRS Peritumor Radiomic Signature -- CRS Clinical and Radiomic Signature; Nomograms -- Adenocarcinoma of Lung -- Algorithms -- Tomography -- X-Ray Computed; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2021.11.017
• Languages: English
• ISSNs: 0169-5002

Abstract

Abstract: Objectives: This study aims to develop and evaluate preoperative CT-based peritumoral and tumoral radiomic features to predict tumor spread through air space (STAS) status in clinical stage I lung adenocarcinoma (LUAD). Materials and methods: From June 2018 to December 2019, a retrospective diagnostic investigation was done. Patients with pathologically confirmed STAS status (N = 256) were eventually enrolled. The development cohort consisted of 191 patients (74.6%) chosen randomly in a 7:3 ratio, whereas the validation group consisted of 65 patients (25.4%). The performance of models was assessed using receiver operating characteristic analysis, accuracy, sensitivity, specificity, negative predictive values, and positive predictive values. Results: The STAS positive status was found in 85 (33.2%) of the 256 patients (female: 53.2%; median [IQR] age: 62.0, [53.0–79.0] years), while the STAS negative status was found in 171 patients (66.8%) (female:50.6%; median [IQR] age: 62.0, [53.0–87.0] years). The combined TRS and PRS-15 mm model had an AUC of 0.854 (95% CI, 0.799–0.909) in the development cohort and 0.870 (95% CI, 0.781–0.958) in the validation cohort, indicating that the tumor radiomic signature (TRS) model and different peritumoral radiomic signature (PRS) models were used to build the optimal gross radiomic signature (GRS) model. The radiomic nomogram achieves superior discriminatory performance than GRS and clinical and radiological signatures (CRS), with an AUC of 0.871 (95% CI, 0.820–0.922) in the development cohort and AUC of 0.869 (95% CI, 0.776–0.961) in the validation cohort. Based on the Akaike information criterion (AIC) and decision curve analysis (DCA), the radiomic nomogram provided greater clinical predictive capacity than clinical or any radiomic signatures alone. Conclusion: In conclusion, we discovered that peritumoral characteristics were substantially related to STAS status. This study revealed the unit of radiomic signature and clinical signatures may have a better performance in STAS status.