Related Experiment Video
Updated: Jul 13, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Correlation analysis between growth heterogeneity and genetic mutations in resected subsolid lung adenocarcinoma
Shulei Cui1, Linlin Qi1, Fenglan Li1
1Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Persistent pulmonary subsolid nodules (SSNs) are pathologically classified as invasive adenocarcinoma (IA) or precancerous glandular lesions. Pulmonary SSNs demonstrate significant heterogeneity in their growth patterns. Some SSNs remain stable during long-term follow-up, some grow slowly, and some grow rapidly. However, the molecular mechanisms that trigger or regulate the growth of SSNs remain incompletely understood. This study aimed to investigate the correlation between growth pattern of pulmonary SSNs and common gene mutations and help explore key genes that trigger or regulate SSN growth based on long-term computed tomography (CT) follow-up.
Methods:
We retrospectively included SSNs that underwent ≥3 years of CT follow-up or showed growth within 3 years between November 2010 and December 2024, and underwent pathological diagnosis and genetic testing. Enrolled SSNs were divided into growth and non-growth groups. According to the growth rate, the growth group was further divided into rapidly growing [volume doubling time (VDT) ≤800 days] and slowly growing (VDT >800 days) subgroups. Gene mutations including epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene homolog (KRAS), and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) were identified via routine genetic testing. The Mann-Whitney U test, the Chi-squared test, or Fisher's exact test, and multiple logistic regression analysis were used to analyze the correlations among genetic, clinical, and radiological data across different growth patterns, nodule subtypes, and pathological subtypes.
Results:
A total of 164 SSNs [median diameter, 10.0 mm; interquartile range (IQR), 7.5-13.0 mm] from 159 patients (median age, 57.0 years; IQR, 50.0-62.0 years) were included. The majority of patients were non-smokers (82.3%). A significantly higher prevalence of IA in the growing group than in the non-growing group (82.8% vs. 13.3%; P<0.001). Age (P=0.03), initial nodule type (P=0.01), pathological subtypes (P<0.001), and EGFR mutations (P<0.001) were independent risk factors for the SSN growth. However, there were no significant differences in the mutation rates of EGFR, KRAS, or BRAF_V600E between the rapidly and slowly growing groups when stratified by VDT (EGFR: 75.4% vs. 70.8%, P=0.55; KRAS: 5.8% vs. 9.2%, P=0.67; BRAF_V600E: 0% vs. 0%, not applicable). EGFR mutation rates differed significantly across nodule subtypes (P=0.001). Specifically, the incidence of EGFR mutations was significantly higher in IA than in non-invasive lesions (P=0.001).
Conclusions:
Growing SSNs during identified on CT follow-up were significantly correlated with IA and EGFR mutations. EGFR may serve as a pivotal gene in driving the growth of SSNs; however, it is not a key gene in regulating their growth rate. This finding may help promote personalized management for SSN patients and uncover potential biomarkers and therapeutic targets with value.
More Related Videos
11:31Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer
Published on: May 20, 2016
07:59Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023