TY - JOUR
T1 - Preoperative CT-based peritumoral and tumoral radiomic features prediction for tumor spread through air spaces in clinical stage I lung adenocarcinoma
AU - Liao, Guoqing
AU - Huang, Luyu
AU - Wu, Shaowei
AU - Zhang, Peirong
AU - Xie, Daipeng
AU - Yao, Lintong
AU - Zhang, Zhengjie
AU - Yao, Su
AU - Shanshan, Lyu
AU - Wang, Siyun
AU - Wang, Guangyi
AU - Wing-Chi Chan, Lawrence
AU - Zhou, Haiyu
N1 - Funding Information:
This study was funded by the Guangdong Province Medical Scientific Research Foundation [grant number B2018148], Science and Technology Program of Guangzhou [grant number 201903010028, 2017B030314026], Guangdong Provincial People’s Hospital Intermural Program [grant number KJ012019447], National Natural Science Foundation of China in 2020 [grant number KY012020523], Mandatory project of Guangdong Medical Science and Technology Research Fund [grant number C2020107], Health and Medical Research Funds [grant number HMRF 02131026, HMRF 16172561].
Publisher Copyright:
© 2021 The Authors
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Adenocarcinoma of Lung
KW - Algorithms
KW - Nomograms
KW - Tomography
KW - X-Ray Computed
UR - http://www.scopus.com/inward/record.url?scp=85121458797&partnerID=8YFLogxK
U2 - 10.1016/j.lungcan.2021.11.017
DO - 10.1016/j.lungcan.2021.11.017
M3 - Journal article
C2 - 34942493
AN - SCOPUS:85121458797
SN - 0169-5002
VL - 163
SP - 87
EP - 95
JO - Lung Cancer
JF - Lung Cancer
ER -