CT-based radiomic phenotypes of lung adenocarcinoma: a preliminary comparative analysis with targeted next-generation sequencing - Liu, Xiaowen; Xu, Ting; Wang, Shuxing; Chen, Yaxi; Jiang, Changsi; Xu, Wuyan; Gong, Jingshan (Frontiers in Medicine, 2023)
Objectives: This study aimed to explore the relationship between computed tomography (CT)-based radiomics phenotypes and genomic profiles, including expression of PD-L1 and the 10 major genes, such as EGFR, TP53, and KRAS, in patients with lung adenocarcinoma (LUAD). Methods: In total, 288 consecutive patients with pathologically confirmed LUAD were enrolled in this retrospective study. Radiomic features were extracted from preoperative CT images, and targeted genomic data were profiled through next-generation sequencing. PD-L1 expression was assessed by immunohistochemistry staining (chi-square test or Fisher’s exact test for categorical data and the Kruskal-Wallis test for continuous data). A total of 1013 radiomic features were obtained from each patient’s CT images. Consensus clustering was used to cluster patients on the basis of radiomic features. Results: The 288 patients were classified according to consensus clustering into four radiomics phenotypes: Cluster 1 (n=11) involving mainly large solid masses with a maximum diameter of 5.1 ± 2.0 cm; Clusters 2 and 3 involving mainly part-solid and solid masses with maximum diameters of 2.1 ± 1.4 cm and 2.1 ± 0.9 cm, respectively; and Cluster 4 involving mostly small ground-glass opacity lesions, with a maximum diameter of 1.0 ± 0.9 cm. Differences in maximum diameter, PD-L1 expression and TP53, EGFR, BRAF, ROS1 and ERBB2 mutation among the four clusters were statistically significant. Regarding targeted therapy and immunotherapy, EGFR mutations were highest in Cluster 2 (73.1%); PD-L1 expression was highest in Cluster 1 (45.5%). Conclusions: Our findings provide evidence that CT-based radiomics phenotypes could noninvasively identify LUADs with different molecular characteristics, showing the potential to provide personalized treatment decision-making support in LUAD patients.