Abstract | Background: Methods: We retrospectively analyzed pretreatment CT images and clinical information from a cohort of lung adenocarcinomas. We entered the top-ranked features into a support vector machine (SVM) classifier to establish a radiomics signature that predicted EGFR mutation status. Furthermore, we identified the best response-related features based on EGFR mutant-related features in first-line TKI therapy patients. Then we test and validate the predictive effect of the best response-related features for progression-free survival (PFS). Results: Six hundred ninety-two patients were enrolled in building radiomics signatures. The 13 top-ranked features were input into an SVM classifier to establish the radiomics signature of the training cohort (n = 514), and the predictive score of the radiomics signature was assessed on an independent validation group with 178 patients and obtained an area under the curve (AUC) of 74.13%, an F1 score of 68.29%, a specificity of 79.55%, an accuracy of 70.79%, and a sensitivity of 62.22%. More importantly, the skewness-Low (≤0.882) or 10th percentile-Low group (≤21.132) had a superior partial response (PR) rate than the skewness-High or 10th percentile-High group (p < 0.01). Higher skewness (hazard ratio (HR) = 1.722, p = 0.001) was also found to be significantly associated with worse PFS. Conclusions: The radiomics signature can be used to predict EGFR mutation status. Skewness may contribute to the stratification of disease progression in lung cancer patients treated with first-line TKIs.
|
Authors | Meilin Jiang, Pei Yang, Jing Li, Wenying Peng, Xingxiang Pu, Bolin Chen, Jia Li, Jingyi Wang, Lin Wu |
Journal | Frontiers in oncology
(Front Oncol)
Vol. 12
Pg. 985284
( 2022)
ISSN: 2234-943X [Print] Switzerland |
PMID | 36052262
(Publication Type: Journal Article)
|
Copyright | Copyright © 2022 Jiang, Yang, Li, Peng, Pu, Chen, Li, Wang and Wu. |