Correlation analysis between growth heterogeneity and genetic mutations in resected subsolid lung adenocarcinoma based on long-term computed tomography (CT) follow-up

Introduction

Lung cancer persists as the most prevalent and lethal malignancy globally, and it currently ranks first among all malignant tumors in China (1). Early detection, early diagnosis, and treatment are the most effective strategies for improving the survival outcomes of lung cancer patients. With the increasing prevalence of early lung cancer screening programs, the detection rate of subsolid nodules (SSNs) on computed tomography (CT) scans has increased significantly (2,3). Radiologically, SSNs are classified into two subtypes based on the presence of solid components: pure ground-glass nodule (pGGN) and part-solid nodule (PSN) (4). Pathologically, persistent SSNs exhibit a characteristic stepwise progression, ranging from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA). SSNs typically exhibit an indolent growth pattern. Current evidence indicates that the median volume doubling time (VDT) of SSNsranges from 678 to 1,448 days, with approximately 13–50% showing interval growth during surveillance (5-8). Numerous studies (7,9,10) have indicated that the growth patterns of SSNs are heterogeneous; mostly exhibit indolent growth or remain stable, whereas a few display rapid growth. Notably, rapidly growing SSNs demonstrate a higher risk of invasion​(5), requiring intensive follow-up surveillance or clinical intervention. Therefore, early identification of SSN growth and elucidation of the underlying molecular mechanisms are important.

Previous research has extensively explored the clinical and radiological factors influencing the growth of SSNs, including diameter, volume, mass, solid component, and morphological characteristics (7-9). However, the molecular mechanisms that trigger or regulate the growth of SSNs remain incompletely understood. Some prior studies have primarily compared gene expression profiles and tumor microenvironment (TME) characteristics between SSN-manifested adenocarcinomas and solid nodule-manifested lung cancer to elucidate the mechanisms underlying SSN indolence (11-13). Other studies have investigated genomic, transcriptomic, or proteomic alterations associated with SSN pathological progression (14-16). Collectively, these efforts highlight significant gaps in the molecular mechanisms that trigger SSN growth and regulate growth rates. Given the critical role of genetic alterations in regulating cellular proliferation, differentiation, and apoptosis, somatic mutations and dysregulated gene expression may play a pivotal role in influencing SSN growth and interval growth patterns.

Therefore, this study aims to investigate the correlation between growth pattern of pulmonary SSNs and common gene mutations and explore key genes that trigger or regulate SSN growth based on long-term CT follow-up. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1249/rc).

Methods

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College (No. 25/767-5714). Individual consent for this study was waived due to its retrospective nature.

Patient selection

Patients with SSNs who underwent thin-section chest CT scans (≤1.25 mm) between November 2010 and December 2024 were retrospectively reviewed. The inclusion criteria were as follows: (I) SSNs with a follow-up duration of at least 3 years or SSNswith a follow-up period of less than 3 years but showing growth; (II) SSNs confirmed by surgical pathology; (III) patients who underwent common genetic testing [including epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene homolog (KRAS), and v-raf murine sarcoma viral oncogene homolog B1 (BRAF)]. The exclusion criteria were: (I) diffuse SSNs, as well as fibrotic alterations, suspicion of interstitial lung disease or bronchiolitis; (II) absence of thin-section CT scans at baseline and final examinations; (III) patients who had received targeted therapy, chemotherapy, and other adjuvant therapies (Figure 1).

Figure 1 Flowchart depicting the cohort selection. The flow diagram shows the inclusion and exclusion criteria for patients with SSNs. CT, computed tomography; SSNs, subsolid nodules; VDT, volume doubling time.

CT examinations

All scans were acquired using 64-detector row CT scanners, including GE Medical Systems (LightSpeed VCT, Discovery CT 750 HD, Optima CT 660), or Toshiba Medical Systems (Aquilion). A tube voltage of 120 kVp was used, with automatic tube current modulation (200–350 mA) and a noise index of 13. The pitch was set at 0.992 or 0.984, with a rotation time of 0.5 s. The slice thickness was fixed at 5 mm for the initial acquisition. For contrast-enhanced studies, iopromide (300 mg iodine/mL) was administered intravenously at a dose of 80–90 mL and a flow rate of 2.5 mL/s. Image acquisition began 35 seconds post-injection to ensure optimal vascular enhancement. Thin-section reconstructions were performed at 1.0–1.25 mm slice thickness with a 0.8 mm overlap, using a standard reconstruction algorithm. Images were reviewed in both lung [window width: 1,600 Hounsfield unit (HU); level: −600 HU] and mediastinal (window width: 360 HU; level: 60 HU) settings for comprehensive evaluation.

Clinical and radiological analysis

The clinical and radiological characteristics of all included SSNs were systematically assessed using our institutional Radiology Information System and Picture Archiving Communication System (RIS/PACS). For standardized image interpretation, two board-certified thoracic radiologists (S.C. and L.Q., with 3 and 10 years of dedicated thoracic oncology experience, respectively) evaluated all imaging features. Any diagnostic discrepancies were resolved through consensus review with a senior radiologist (J.W., 30 years of thoracic imaging expertise).

Clinical characteristics were analyzed including age, gender, smoking status, history of malignancy, family history of malignant tumors, CT follow-up period, and VDT. Radiological characteristics were analyzed from multi-planar reformation images, including synchronous multiple SSNs, nodule types, mean diameter, consolidation-to-tumor ratio (CTR), and morphological characteristics (shape, lobulation, spiculation, vacuole, air bronchogram, cystic sign, pleural adhesion, pleural retraction).

The initial nodule type, classified as pGGN or PSN, was evaluated on lung-window images. For each SSN, the longest diameter and its perpendicular short-axis diameter were carefully measured on the slice showing the maximal nodule dimension in thin-section CT scans. The diameter of the solid component was manually measured on the axial slice where the nodule appeared largest, using lung window settings. The nodule volumes were automatically calculated using the AI-driven Dr. Wise platform (Deepwise AI Lab; https://www.deepwise.com/product-drwise). The VDT was calculated based on the total nodule volume, including both the ground-glass and solid components of each lesion. The following equation, which is derived from a modified Schwartz formula (17) based on an exponential growth model, was used to calculate the VDT for nodules: T*log2 / log (V_t/V₀), where V_tand V₀are the final and initial volumes, respectively, and T is the interval between the final and initial CT scans.

Classification of SSN growth patterns

The enrolled SSNs were divided into growth and non-growth groups according to interval changes during the CT follow-up period. Growth of SSNs was defined by a mean diameter increase of at least 2 mm, or the emergence or enlargement of solid components by at least 2 mm (8,18). Furthermore, growing SSNs were classified into rapidly growing and slowly growing groups based on their growth rates. Rapidly growing SSNs were characterized by having a VDT of ≤800 days. Slowly growing SSNs were characterized by having a VDT of >800 days.

Pathological and genetic analysis

Pathological assessment of all SSNs followed the 2021 WHO Classification of Thoracic Tumors (19). The conclusive diagnoses were derived from the examination of surgically obtained specimens. Histological evaluations followed established protocols and were systematically reviewed to minimize inter-observer variability. According to the pathological results, the pulmonary nodules were categorized into two subgroups: the non-invasive group (including AAH/AIS/MIA), and the invasive group (including IA). Routine genetic testing was conducted on the SSN pathological specimens included in the study. This testing encompassed the amplification refractory mutation system polymerase chain reaction (ARMS-PCR) method, a second-generation sequencing panel specific to lung cancer, and an extensive second-generation sequencing panel covering 520 genes. The panels were designed to detect mutations in the EFGR (exons 18–21), KRAS (exons 2–4), and BRAF (exons 15), including their respective mutation sites.

Statistical analysis

Continuous data with a normal distribution were expressed as mean ± standard deviation, while non-normally distributed data were expressed as median [interquartile range (IQR)]. Categorical data were presented as counts (percentages). For continuous variables, Mann-Whitney U tests were applied, while categorical variables were analyzed using Chi-squared tests or Fisher’s exact tests. The bilateral P value <0.05 was considered statistically significant. Variables with P<0.05 in the univariate analysis were included in the multivariable logistic regression analysis to determine the independent risk factors. Statistical analyses were performed using IBM SPSS Statistics software (version 26.0) and R version 4.4.3 were used for data visualization.

Results

Clinical and radiological characteristics of the enrolled SSNs

A total of 164 SSNs from 159 patients (IQR, 50.0–62.0 years; 90 females, 69 males) were included. The majority of patients were non-smokers (82.3%). The initial and final clinical and radiological characteristics of these SSNs are summarized in Table 1. The median CT follow-up period was 1,218.5 (IQR, 922.0–1,580.3) days. Among the initial SSNs, the proportions of pGGNs and PSNs were 73.8% (n=121) and 26.2% (n=43), respectively. During the follow-up, 92 pGGNs (92/121, 76.0%) showed growth, of which 46 pGGNs showing mean diameter growth, 6 showing the emergence of solid components and 40 showing both mean diameter growth and the emergence of solid components. Among 43 PSNs, 42 (97.7%) showed growth, of which 11 showing mean diameter growth, 8 showing the enlargement of solid components and 23 showing growth of both mean diameter and solid component. Thus, the final CT follow-up exhibited 75 pGGNs, 85 PSNs, and 4 solid nodules (SNs). The growing 92 pGGNs and 42 PSNs were classified into the growth group and the stable 30 SSNs were classified into the non-growth group (Figure 2A). The VDT of growing SSNs was 764.2 (IQR, 480.9–1,401.4) days.

Figure 2 Overview of the growth patterns and pathological characteristics of SSNs. (A) The Sankey diagram illustrates the growth and evolution process of SSNs and the pathological outcomes. (B) Pathological distribution of the growth and non-growth group. AIS, adenocarcinoma in situ; IA, invasive adenocarcinoma; IQR, interquartile range; MIA, microinvasive adenocarcinoma; pGGN, pure ground-glass nodule; PSN, part-solid nodule; SN, solid nodule; SSNs, subsolid nodules.

Pathologically, the resected lesions consisted of 115 IA, 33 MIA, 16 AIS. Among the growing SSNs, IA, MIA and AIS were observed in 82.8% (111/134), 16.4% (22/134), and 0.7% (1/134), respectively. Among the non-growing SSNs, IA, MIA and AIS were observed in 13.3% (4/30), 36.7% (11/30), and 50% (15/30), respectively (Figure 2B).

Furthermore, genetic analysis revealed a predominance of EGFR mutations, identified in 61.6% SSNs (101/164), mainly consisting of 56.4% (57/101) L858R and 33.7% (34/101) exon 19 deletion subtypes. KRAS and BRAF_V600E mutations were only identified in 7.3% (12/164) and 1.2% (2/164) cases, respectively (Table S1, Figure 3). When stratified by growth pattern, growing SSNs harbored 73.1% (98/134) EGFR. Whereas, non-growing SSNs exhibited only 10% (3/30) EGFR mutations. When stratified by histology, IA lesions harbored 69.6% (80/115) EGFR mutations. Whereas AIS/MIA lesions carried 42.9% (21/49) EGFR mutations (Figure 4).

Figure 3 Distribution of gene mutations of pulmonary SSNs and mutational landscapes of EGFR and KRAS variants. The pie chart (A) illustrates the overall distribution of gene mutations among 164 cases, with EGFR mutations being the most frequent (101/164, 61.6%), followed by non-mutations (49/164, 29.9%), KRAS mutations (12/164, 7.3%), and BRAF V600E mutations (2/164, 1.2%). The pie chart (B) details the mutational landscape of EGFR variants (n=101), with EGFR_L858R mutations being the most frequent (57/101, 56.4%). The pie chart (C) presents the mutational landscape of KRAS variants (n=12), with KRAS_G12C mutations being the most frequent (5/12, 41.7%). BRAF, v-raf murine sarcoma viral oncogene homolog B1; EGFR, epidermal growth factor receptor; EGFR_G719x, EGFR_G719S/C/A; KRAS, Kirsten rat sarcoma viral oncogene; KRAS_Q61X, KRAS_Q61H/L/R; SSNs, subsolid nodules.

Figure 4 Genetic mutation profiles of SSNs stratified by growth pattern and pathological subtype. (A) Gene mutation distribution of the growth and non-growth group. (B) Gene mutation distribution across different pathological subtypes. AIS/MIA, adenocarcinoma in situ/microinvasive adenocarcinoma; BRAF, v-raf murine sarcoma viral oncogene homolog B1; EGFR, epidermal growth factor receptor; IA, invasive adenocarcinoma; KRAS, Kirsten rat sarcoma viral oncogene; SSNs, subsolid nodules.

Correlation analyses between SSNs growth pattern and pathological characteristics as well as genetic mutation

Univariate analysis revealed significant associations between growth and several clinical and radiological factors: age (P<0.001), gender (P=0.042), initial nodule type (P=0.002), initial mean diameter (P=0.001), initial CTR (P=0.002), and pleural retraction (P=0.009). EGFR mutation was significantly correlated with SSN growth (P<0.001). Both EGFR_ex19_del and EGFR_L858R mutations were significantly associated with SSN growth (P=0.002). BRAF_V600E also demonstrated significant differences in SSN growth (P=0.03) (Figure 5). Multivariable logistic regression analysis showed that age (P=0.03), initial nodule type (P=0.01), pathological subtypes (P<0.001), and EGFR mutation (P<0.001) were independent risk factors for the SSN growth (Table 2).

Figure 5 Association between SSNs growth and genetic mutations. The chart illustrates the distribution of different genetic markers (EGFR status, EGFR_ex19_del, EGFR_L858R, and BRAF_V600E) between the growth and non-growth groups of SSNs (A-D). *, a P value <0.05 was considered statistically significant. BRAF, v-raf murine sarcoma viral oncogene homolog B1; EGFR, epidermal growth factor receptor; SSNs, subsolid nodules.

Correlation analyses between the growth rate of SSNs and genetic mutations

The distribution of VDT in the growth group is shown in Figure 6. Based on VDT, the rapidly growing group comprised 69 cases (51.5%) with VDT ≤800 days [median, IQR, 486.6 (360.9–601.5) days], and the slowly growing group comprised 65 cases (48.5%) with VDT >800 days [median, IQR, 1,445.1 (997.6–2,235.9) days]. There were no significant differences in the mutation rates of EGFR, KRAS, or BRAF_V600E between the rapidly and slowly growing groups (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) (Table 3, Figure 7).

Figure 6 The VDT distribution of the growth group. The VDT of growing SSNs was 764.2 (IQR, 480.9–1,401.4) days. The rapidly growing group comprised 69 cases (51.5%) with VDT ≤800 days [median, IQR, 486.6 (360.9–601.5) days], and the slowly growing group comprised 65 cases (48.5%) with VDT >800 days [median, IQR, 1,445.1 (997.6–2,235.9) days]. IQR, interquartile range; SSNs, subsolid nodules; VDT, volume doubling time.

Figure 7 Representative cases of SSNs showing the correlation between the growth rate of SSNs and genetic mutations. (A) A 58-year-old female patient with a baseline pGGN in the right upper lobe (mean diameter 12.5 mm, irregular shape). The lesion exhibited rapid growth; over 1,249 days of follow-up, it evolved into a PSN with a preoperative mean diameter of 22 mm. The volume doubling time was 380.8 days. Postoperative pathology revealed moderately to poorly differentiated adenocarcinoma with an EGFR mutation (exon 18 missense, p.G719S/C/A). (B) A 60-year-old male patient with a baseline PSN in the right lower lobe (mean diameter 8.5 mm, lobulated margin). The lesion exhibited slow growth; over 1,429 days of follow-up, it evolved into a PSN with a preoperative mean diameter of 11 mm. The volume doubling time was 884.6 days. Postoperative pathology revealed well-to-moderately differentiated adenocarcinoma with an EGFR mutation (exon 21 missense, p.L858R). (C) A 43-year-old male patient presented with a baseline pGGN in the right upper lobe (mean diameter 10 mm, regular shape). After 1,425 days of follow-up, no growth was observed. Postoperative pathology confirmed well-differentiated adenocarcinoma without EGFR mutation. EGFR, epidermal growth factor receptor; pGGN, pure ground-glass nodule; PSN, part-solid nodule; SSNs, subsolid nodules.

Correlation analyses between the nodule subtypes and pathological subtypes and genetic mutations

Correlation analyses between the nodule subtypes and pathological subtypes, and genetic mutations were shown in Table 4, respectively. Significant differences in EGFR mutations were observed among the nodule subtypes (P=0.001). The EGFR mutation rate was notably higher in the PSN subgroup (71.8%) compared to the pGGN subgroup (53.3%), with specific mutations showing significant variance. KRAS mutations also demonstrated significant variability (P=0.001) (Figure 8A,B).

Figure 8 Association between nodule subtypes, pathological subtypes and genetic mutations. (A,B) Association between nodule subtypes and genetic mutations. (C,D) Association between pathological subtypes and genetic mutations. *, a P value <0.05 was considered statistically significant. AIS/MIA, adenocarcinoma in situ/minimally invasive adenocarcinoma; EGFR, epidermal growth factor receptor; IA, invasive adenocarcinoma; KRAS, Kirsten rat sarcoma viral oncogene; pGGN, pure ground-glass nodule; PSN, part-solid nodule; SN, solid nodule.

A significant prevalence of EGFR mutation was observed in IA compared to AIS/MIA (69.6% versus 42.9%, P=0.001). A notable distinction was the occurrence of the EGFR_ex19_del, which was significantly more frequent in IA at a rate of 25.2% compared to 10.2% in AIS/MIA, achieving statistical significance (P=0.03) (Figure 8C,D).

Discussion

In this study, we investigated the correlation between growth pattern of pulmonary SSNs and common gene mutations and explore key genes that trigger or regulate SSN growth based on long-term CT follow-up. We found that age, initial nodule type, pathological subtypes, and EGFR mutation were independent risk factors for the growth of SSNs. EGFR mutations are likely a key driver of SSN growth; however, EGFR, KRAS, or BRAF_V600E mutations do not serve as critical regulators of SSN growth rate. The incidence of EGFR mutations was significantly higher in IA than in AIS/MIA (P=0.001).

It is widely acknowledged that some clinical and radiological characteristics were risk factors of SSN growth (5,8,20). Consistently, our study identified similar determinants of SSN growth. Age, initial nodule pattern, pathological subtypes were independent risk factors for the growth of SSNs.

SSNs harbor multiple driver genes. In this study, EGFR mutations were the most prevalent, accounting for 61.6%, which is consistent with previous research (21-23). Nakamura et al. (24) demonstrated that 35 EGFR-mutated non-small cell lung cancer (NSCLC) patients had a significantly longer VDT compared to 67 EGFR-wild-type cases; however, their analysis lacked systematic integration of VDT, SSN, and EGFR mutation status. In contrast, our study identified EGFR mutations are likely key drivers of SSN growth, yet found no association between the mutation status of EGFR, KRAS, or BRAF (including specific subtypes) and SSN growth rates. To date, no studies have been reported on the key factors that regulate the growth rate of SSNs, and our study provides new evidence that 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. Notably, a study based on a real-life European cohort reported that SSNs with rapid growth exhibited a preferential enrichment of KRAS mutations compared to those with slower growth (25). This discrepancy may stem from differences in cohort demographics: our Asian population had a high prevalence of EGFR mutations, whereas Western studies often include more KRAS-mutant cases. These findings highlight the genetic heterogeneity across SSNs, which may have implications for the diagnosis and treatment of lung adenocarcinomas. The distinct mutational landscapes further underscore the importance of tailored therapeutic approaches based on SSNs patterns.

The probability of EGFR mutation was higher in SSNs than in SNs, consistent with the previous study (26). Additionally, this study found no EGFR mutations in SNs. In terms of pathological subtypes, the overall EGFR mutation rate and the frequency of EGFR_ex19_del were significantly higher in IA than in AIS/MIA. Concurrently, the frequency of EGFR mutations and copy number variation (CNV) load was also elevated in IA(12), suggesting a dual contribution of mutational and genomic alterations to invasiveness. Similarly, Kobayashi et al. (22) also found that EGFR-positive ground-glass nodules (GGNs) demonstrated significant associations with both pathological progression to MIA/IAC and SSNs growth compared to EGFR-negative lesions. These findings collectively indicate that EGFR mutations may serve as key drivers of SSNs progression from pre-invasive to invasive states. Li et al. (27) found that maximum diameter, CTR, mean CT value, vascular convergence sign, and air bronchogram sign, as independent imaging predictors of EGFR mutations in SSNs. Notably, while EGFR alterations dominate the molecular landscape of early-stage SSNs, Yu et al. (23) found that the KRAS-mutant group had a higher proportion of IA, suggesting potential divergence in oncogenic pathways between EGFR- and KRAS-driven tumorigenesis. These findings suggest that genetics may drive the growth and aggressiveness of SSNs rather than growth rate, warranting further investigations into the underlying cytological and molecular mechanisms.

Notably, EGFR and KRAS mutations exhibit mutual exclusivity, which is generally consistent with previous findings (23,28). Yu et al. (23) proposed that activation of either EGFR or KRAS is sufficient to drive constitutive signaling through the RTK/RAS pathway, rendering co-mutation unnecessary for tumorigenesis.

There are several limitations in this study. Firstly, this was a retrospective design, which may introduce selection bias. Secondly, this study was a single center with a relatively small sample size. Thirdly, common mutations in the EGFR and KRAS genes do not appear to be significant regulators of the growth rate of SSNs. However, this study did not evaluate potential key factors influencing SSN growth rate at different biological levels, including transcriptional and protein regulatory mechanisms. Future research will aim to address this knowledge gap and elucidate the underlying factors involved.

Conclusions

Growing SSNs identified on CT follow-up were significantly correlated with pathologically 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. These findings may help promote personalized management for SSN patients and uncover potential biomarkers and therapeutic targets with value.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1249/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1249/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1249/prf

Funding: This work was funded by the Fundamental Research Funds for the Central Universities, Peking Union Medical College (grant No. 3332024207), the Special Research Fund for Central Universities, Peking Union Medical College (grant No. 3332022025), and the National Natural Science Foundation of China (grant No. 81971616).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-aw-1249/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College (No. 25/767-5714). Individual consent for this study was waived due to its retrospective nature.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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