Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Lung cancer (Amsterdam, Netherlands), 2022-01, Vol.163, p.87-95

Ort / Verlag

Ireland: Elsevier B.V

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• 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). 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. 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. 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.