Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jan;20(1):52-64.
doi: 10.1016/j.jtho.2024.09.1431. Epub 2024 Sep 19.

Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma

Hong-Ji Li  1 , Zhen-Bin Qiu  1 , Meng-Min Wang  2 , Chao Zhang  1 , Hui-Zhao Hong  2 , Rui Fu  2 , Li-Shan Peng  2 , Chen Huang  2 , Qian Cui  3 , Jia-Tao Zhang  2 , Jing-Yun Ren  4 , Lei Jiang  4 , Yi-Long Wu  2 , Wen-Zhao Zhong  5
Affiliations
Free article

Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma

Hong-Ji Li et al. J Thorac Oncol. 2025 Jan.
Free article

Abstract

Introduction: An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomics-based features and molecular characteristics of lung nodules.

Methods: Consensus clustering was applied to the radiomic features of 1212 patients to establish stable clustering. Clusters were illustrated using clinicopathological and next-generation sequencing. A classifier was constructed to further investigate the molecular characteristics in patients with paired computed tomography and RNA sequencing data.

Results: Patients were clustered into four clusters. Cluster 1 was associated with a low consolidation-to-tumor ratio, preinvasion, grade I disease, and good prognosis. Clusters 2 and 3 reported increasing malignancy with a higher consolidation-to-tumor ratio, higher pathologic grade, and poor prognosis. Cluster 2 possessed more spread through air spaces and cluster 3 reported a higher proportion of pleural invasion. Cluster 4 had similar clinicopathological features as cluster 1 except but a proportion of grade II disease. RNA sequencing indicated that cluster 1 represented nodules with indolent growth and good differentiation, whereas cluster 4 reported progression in cell development but still had low proliferative activity. Nodules with high proliferation were classified into clusters 2 and 3. In addition, the radiomics classifier distinguished cluster 2 as nodules harboring an activated immune environment, whereas cluster 3 represented nodules with a suppressive immune environment. Furthermore, signatures associated with the prognosis of early-stage LUAD were validated in external datasets.

Conclusions: Radiomics features can manifest molecular events driving the progression of LUAD. Our study provides molecular insight into radiomics features and assists in the diagnosis and treatment of early-stage LUAD.

Keywords: Genomics; Lung adenocarcinoma; NSCLC; Radiomics; Transcriptomics.

PubMed Disclaimer

Conflict of interest statement

Disclosure Dr. Zhong declares honoraria from AstraZeneca, Bristol-Myers Squibb, Merck Sharp & Dohme, Roche, and Innovent, outside the submitted work. Dr. Wu declares advisory services for AstraZeneca, Boehringer Ingelheim, Novartis, and Takeda; speaker fees from AstraZeneca, Beigene, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Merck Sharp & Dohme, Pfizer, Roche, Sanofi; and grants from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Hengrui, and Roche outside the submitted work. The remaining authors declare no conflict of interest.