The role of growth heterogeneity in solid nodular non-small cell lung cancer in clinical practice: a narrative review

Introduction

Global Cancer Observatory 2022 data indicate that lung cancer remains the leading cause of global cancer incidence and mortality, accounting for 12.4% of new cancer cases and 18.7% of cancer-related deaths, with the highest burden in East Asia, especially China (1,2). Early lung cancer usually presents as isolated pulmonary nodules (<3 cm). With the development of low-dose computed tomography (CT) lung cancer screening and the widespread application of chest thin-layer CT, the detection rate of pulmonary nodules has significantly increased (3). According to density, pulmonary nodules can be divided into solid nodules (SNs) and sub-SNs (SSNs). SNs are defined as discrete opacities on thin-section CT with homogeneous soft-tissue attenuation that completely obscure underlying bronchovascular structures. SSNs include pure ground-glass nodules, which allow visualization of bronchovascular markings, and part-SNs, which contain both ground-glass and solid components (4,5). As we all known, for SNs, especially those with small volume, we usually consider their malignancy probability as low. However, if it is malignant, the degree of malignancy is usually high, growth is fast, metastasis occurs early, the risk of recurrence is higher, and the prognosis is poor (6-8). However, in recent years, we and some scholars have observed that a part of malignant SNs show relatively indolent growth behavior, and the treatment and management strategies of this type of lung cancer are still uncertain (9,10). To our knowledge, this study is among the first to explicitly describe and emphasize the growth heterogeneity of solid nodular lung cancer, underscoring its potential clinical significance.

This review summarized and integrated the current research progress on the natural growth mode, imaging characteristics, pathological mechanism, artificial intelligence (AI) prediction and prognosis of solid nodular lung cancer, with the purpose of providing scientific and powerful evidence for the clinical decision-making of surgical intervention timing and individualized follow-up strategy, and pointing out the future research direction of solid nodular lung cancer. We present this article in accordance with the Narrative Review reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2697/rc).

Methods

Our narrative review approach was adopted to allow qualitative integration of heterogeneous evidence spanning imaging, pathology, molecular biology, and AI in solid nodular lung cancer. Given the substantial variability in study designs, outcomes, and analytical methods, a purely systematic synthesis was not feasible.

We conducted a narrative review of the literature, with a main focus on the growth heterogeneity and prognosis of solid nodular non-small cell lung cancer (NSCLC). A systematic search was conducted in PubMed, Embase, Web of Science, and Scopus from January 1, 2000 until August 1, 2025. The search strategy included a combination of the following terms: (“solid pulmonary nodule” OR “solid nodular lung cancer” OR “NSCLC solid nodule”) AND (“growth kinetic” OR “growth” OR “volume doubling time” OR “VDT” OR “tumor doubling time”) AND (“prognosis” OR “survival” OR “clinical outcome” OR “imaging” OR “pathology” OR “molecular mechanisms” OR “prediction”). Other terms included “radiomics”, “artificial intelligence”, “molecular mechanisms”, “tumor microenvironment”, “single-cell transcriptomics”, and “spatial transcriptomics”, and manual search of references from selected articles. Only peer-reviewed studies published in English were included. We also screened references to relevant guidelines to ensure comprehensive coverage. Inclusion criteria: original studies (retrospective, prospective), systematic reviews, meta-analyses, and consensus/guideline papers, with a focus on solid pulmonary nodules and their growth and prognosis. Exclusion criteria: case reports, conference abstracts without full text, and non-English publications.

In the study selection process, two independent reviewers qualitatively evaluated the relevance of retrieved references (focusing on the growth and prognosis of lung cancer nodules), methodological rigor (including study design, sample size, and tumor double time measurement methods), and clinical applicability (the guiding value of the study conclusions for nodule management and surgical resection). Titles and abstracts were initially screened against the inclusion criteria by both reviewers independently. Any discrepancies were resolved through discussion until a consensus was reached. As this is a narrative review designed to synthesize heterogeneous evidence, formal inter-rater reliability statistics were not calculated; this consensus-based approach ensured the selection of studies most pertinent to the review’s objectives. The search strategy was summarized in Table 1 and Table S1.

Results and discussion

Natural growth dynamics of SNs

There have been various ways in the past to demonstrate the growth rate of pulmonary nodules, including diameter growth rate, volume doubling time (VDT), mass doubling time (MDT), etc. While diameter-based assessment is simpler, it is less sensitive than volumetric measures for detecting small changes. MDT, which considers density changes, may provide additional insights but is less standardized and commonly used than VDT in current guidelines. VDT can theoretically reflect the exponential growth of tumor cells and is widely used for distinguishing between rapidly and slowly growing tumors. The VDT are calculated for growing nodules as log₂ × T/log (X_f/X_i), where X_f and X_i are the final and initial volumes, respectively, and T is the interval between the final and initial CT scans (11). However, differences in calculation, segmentation tools, scan interval variations, and reader variability can significantly affect VDT values, which was an issue insufficiently addressed in earlier literature. For the purpose of this review, growth heterogeneity is specifically defined as the divergent tumor growth kinetics observed clinically, primarily characterized by variations in VDT. This heterogeneity manifests as a spectrum ranging from indolent growth (slower growth, longer VDT) to rapid growth (faster growth, shorter VDT). To ensure consistency in discussion across sections, we adopted a standardized set of VDT thresholds primarily based on the large-scale The Netherlands Belgium Randomized Controlled Lung Cancer Screening Trial (NELSON) and subsequent validation studies (12). These thresholds are defined as follows: VDT <400 days indicating rapid growth, VDT between 400 and 600 days representing an intermediate or moderate growth category, and VDT >600 days signifying indolent growth.

Most malignant SNs grow faster than SSNs (VDT commonly <300–400 days) (13-15). Yet, recent studies show that 10–30% of malignant SNs exhibit indolent growth (VDT >400–600 days) (16). It is worth noting that, in recent 2 years of research, we have also found that some malignant SNs can exhibit inert growth (VDT >400–600 days) (Figure 1). This discovery extends our understanding beyond the previous paradigm of uniform rapid growth in malignant SNs. Whether such nodules ultimately accelerate, metastasize less frequently, or warrant delayed intervention remains unclear.

Figure 1 Comparative cases highlighting growth heterogeneity in solid nodular lung cancer. (A) An example of solid nodular fast-growing lung adenocarcinoma. A 65-year-old male was found to have a solid nodule in the dorsal segment of the right lower lobe during a chest thin-section CT scan at our hospital on May 29, 2023, which was quantitatively analyzed and followed up using the DeepWise system. The baseline measurement was approximately 17 mm × 16 mm with a volume of approximately 2,196.21 mm³. Follow-up chest thin-section CT scans were performed on June 7, 2023, and September 13, 2023, showing a final size of approximately 22 mm × 21 mm and a volume of approximately 5,608.65 mm³. The VDT was 79 days, and the volume growth curve indicated progression. On October 17, 2023, the patient underwent thoracoscopic right lower lobectomy. Pathological examination revealed poorly differentiated adenocarcinoma, predominantly of solid pattern (over 90%). (B) An example of solid nodular inert lung adenocarcinoma. A 56-year-old male was found to have a solid nodule in the anterior segment of the left upper lobe during a physical examination in May 2021. On July 20, 2021, he came to our hospital for chest thin-layer CT examination, which was quantitatively analyzed and followed up using the Deep Wise system. The baseline size was about 9 mm × 7 mm and the volume was about 360.19 mm³. The patient underwent chest thin-layer CT follow-up on April 22, 2022 and October 12, 2022. The final size was about 8 mm × 7 mm and the volume was about 359.94 mm³. The VDT was 3,680 days, and the volume growth curve was stable. On October 25, 2022, left upper lobe wedge resection was performed, and the pathology showed moderately differentiated adenocarcinoma with acinar type (60%) and parietal type (40%). (C) An example of solid nodular lung squamous cell carcinoma. A 72-year-old male was found to have a solid nodule in the anterior segment of the right upper lobe during a chest thin-section CT scan at our hospital on January 23, 2020, which was quantitatively analyzed and followed up using the DeepWise system. The baseline measurement was approximately 27 mm × 17 mm with a volume of approximately 4,593.84 mm³. A follow-up chest thin-section CT scan was performed on May 18, 2020, showing a final size of approximately 34 mm × 29 mm and a volume of approximately 15,336.25 mm³. The VDT was 63 days, and the volume growth curve indicated progression. On May 26, 2020, the patient underwent thoracoscopic right upper lobectomy. Pathological examination revealed moderately differentiated squamous cell carcinoma. These images were original and informed consent was obtained from the patients. (A) was adapted from Chen et al. (Transl Lung Cancer Res, 2025) (10). CT, computer tomography; VDT, volume double time.

Prognosis influenced by growth

Malignant lung SN is usually treated with a “one size fits all” approach in clinical practice. Research underscores that delayed follow-up significantly worsens prognosis in early lung cancer, with time emerging as a critical factor specifically for rapidly progressing malignant SNs (8,17). In an analysis of International Early Lung Cancer Action Program (I-ELCAP), National Lung Screening Trial (NLST), and International Association for the Study of Lung Cancer (IASLC) consortium data, Yankelevitz and colleagues quantified this effect, reporting a near-linear reduction of approximately 1.0% in 10-year cure rates for every 1-mm increase in tumor diameter. The decline in curability was markedly steeper for aggressive tumors (defined as VDT <60 days) presenting with a larger initial size. By contrast, tumors exhibiting moderate (defined as VDT 60–120 days) or slow (defined as VDT 120–240 days) growth kinetics showed considerably less susceptibility to delays in intervention. Meanwhile, more studies have also demonstrated the association between VDT and prognosis (Table 2).

The growth heterogeneity of SSNs in previous studies has a significant impact on their clinical management strategies. Liu et al.’s systematic review suggests that the growth pattern of SSN can be quantified by different thresholds (such as 2 mm growth, 5 mm growth, or stage transition), with VDT being a key indicator for evaluating growth kinetics, helping to distinguish between inert and rapidly growing nodules and optimize personalized follow-up plans (21). Tang et al.’s study further confirmed that VDT (especially ≤400 days) is an independent risk factor for predicting stage metastasis of lung adenocarcinoma [odds ratio (OR) =2.327], and a comprehensive model combining clinical and imaging features can significantly improve predictive performance [area under the curve (AUC) increased from 0.833 to 0.877] (22). Their long-term observational studies revealed differences in growth between ground glass nodule (GGN) and part SN (PSN): PSN had a true growth rate of up to 67.3% within 5 years (median progression time of 3 years), while GGN grew slowly (median progression time of 7–9 years) and stage metastasis occurred later (median progression time of 9 years for PSN and 12 years for GGN), emphasizing the necessity of developing differentiated follow-up intervals based on nodule types (23). These findings can provide important references for the study of SN and can be applied to the stratification of growth risk and the development of personalized management strategies for SNs. Future research can further validate the predictive efficacy of the VDT model in solid nodular lung cancer, in order to optimize clinical decision-making in lung cancer screening.

In view of this, the management strategy for malignant SNs should be personalized based on the tumor growth rate and initial size. For smaller nodules with slow growth, excessive medical intervention should be avoided; for rapidly growing larger tumors, it is necessary to prevent delayed diagnosis and treatment in order to reduce the adverse impact on patient prognosis.

Imaging features and predictive value

Previous studies have shown that the CT imaging features of SN were related to its nature and prognosis, including tumor size, margin, lobulation sign, pleural traction/adjacency/invasion, and VDT under CT imaging observation (24-27). In the management of SNs, imaging performance, as the most intuitive feature of nodules, provides a prediction window for their benign/malignant and growth dynamics. However, there is still limited research on the correlation between nodule growth rate and imaging features. Chen et al. (10) prospectively analyzed 250 SNs of NSCLC, all of which were divided into rapid (VDT ≤200 days) and slow growth groups (VDT >200 days). The results showed that smoking history, higher CT value, and deep lobulation sign were risk factors for rapid nodule growth [AUC: 0.704, 95% confidence interval (CI): 0.636–0.771, sensitivity: 65.5%, specificity: 70.5%]. This finding indicates that specific morphologic characteristics visible on baseline CT may already reflect the intrinsic growth potential of pulmonary nodules. Therefore, these pieces of evidence suggest that baseline and longitudinal imaging features can be used to non invasively characterize the intrinsic growth dynamics of nodules quantified by VDT. This dynamic characteristic stratifies nodules into risk categories ranging from inertness to rapid growth, and then becomes the primary determinant of nodule clinical management, from observation to final surgical intervention. This “baseline imaging—growth kinetics—clinical management” trinity provides a structured approach for personalized management in the era of lung cancer screening.

Pathological and molecular mechanisms of growth heterogeneity

Existing studies consistently demonstrate that the growth rate of pulmonary nodules is strongly associated with their pathological type, with poorly differentiated and more invasive lung cancers typically exhibiting shorter VDTs. There are substantial differences in growth kinetics between the two main subtypes of solid NSCLC. Solid lung adenocarcinoma typically exhibits a wider growth rate spectrum, including partially inert nodules (VDT >600 days), while solid squamous cell carcinoma typically shows faster growth (VDT is shorter, usually <300 days) (28-31). Nonetheless, clinical observations indicate that certain poorly differentiated tumors may still display indolent growth, suggesting that pathological classification alone is insufficient to fully explain the observed growth phenotypes and highlighting the potential influence of underlying molecular determinants.

Advances in genomics, transcriptomics, tumor immune microenvironment profiling, and single-cell and spatial transcriptomic technologies have significantly deepened our understanding of malignant pulmonary nodules. Clear biological distinctions exist between SNs and subsolid or ground-glass nodules (SSNs). SNs demonstrate a higher mutational burden than SSNs and exhibit distinct immune activation signatures, including differences in immune activity, T-cell infiltration, and gene expression related to granulocyte maturation and T helper type 1 (Th1)/type 2 (Th2) cells polarization (32-40). These molecular disparities provide a plausible explanation for the generally more aggressive behavior of SNs compared to SSNs. However, they do not fully account for the significant growth heterogeneity observed among SNs themselves.

Preliminary evidence begins to link specific molecular features with growth kinetics. For example, we hypothesize that tumors with high tumor mutation burden (TMB) may be associated with faster proliferation, and immune escape behavior characterized by specific T cell subsets may also lead to faster growth. However, direct mechanistic evidence linking these molecules and immune features with the observed VDT spectrum in clinical practice is still limited.

Therefore, future research can combine longitudinal imaging data with detailed genomic, transcriptomic, and spatial analysis, which is crucial for bridging this gap. These studies will help reveal the molecular determinants of growth heterogeneity between tumors of the same pathological type, and ultimately improve the prediction of nodule behavior and patient prognosis.

Management of pulmonary nodules based on growth

With the global expansion of lung cancer screening programs, accurate assessment of pulmonary nodule growth kinetics has become increasingly critical for clinical decision-making. Among available imaging biomarkers, VDT has emerged as one of the most robust indicators of malignant potential and subsequent management requirements, serving as a cornerstone in screening algorithms, risk stratification models, and follow-up strategies.

NELSON trial stratified non-calcified lung nodules into three growth categories, VDT <400 days, 400–600 days, and >600 days, and implemented three rounds of scheduled CT surveillance. Participants with nodules demonstrating a VDT <400 days were referred for thoracic surgical evaluation and diagnostic work-up. If lung cancer was confirmed, they received appropriate treatment and exited the screening protocol; if malignancy was excluded, a second routine CT scan was performed within 12 months of baseline. Nodules with VDTs of 400–600 days proceeded to annual screening, while those with VDTs >600 days underwent repeat CT after two years (12). The British Thoracic Society (BTS) guidelines similarly recommend further diagnostic evaluation (biopsy, advanced imaging, or resection) for nodules demonstrating significant nodule growth or a VDT <400 days (assessed at 3 months and 1 year). For VDTs of 400–600 days, annual surveillance or biopsy may be considered depending on patient preference, whereas VDTs >600 days warrant either discharge or continued CT monitoring based on clinical context (41). In contrast, Lung-RADS v2022 emphasizes standardized size-based categorization and recommends short-interval follow-up (1–3 months) or additional evaluation for newly detected or rapidly enlarging nodules, although VDT itself is not incorporated into the core scoring system (42). The Fleischner guidelines prioritize nodule size and patient risk stratification for incidental findings, positioning VDT as valuable supplementary information rather than a primary determinant (43). Collectively, these guidelines underscore the central role of nodule growth rate and VDT in modern lung cancer management. Across screening and diagnostic frameworks, there is a consistent recognition that VDT is essential for distinguishing indolent from clinically significant nodules, optimizing surveillance intervals, and reducing overdiagnosis. As advanced imaging analytics and computational tools continue to evolve, integrating growth dynamics into routine practice will remain pivotal for enhancing diagnostic accuracy and delivering personalized care to patients with pulmonary nodules.

AI in growth prediction

Recent advances in AI have markedly improved the detection and characterization of pulmonary nodules, particularly small SNs. Deep learning models have demonstrated performance exceeding that of experienced radiologists. For instance, Liu et al. developed a self-supervised fine-grained network for sub-centimeter nodules, achieving overall diagnostic accuracy of 71.5% compared with 38.5% for radiologists, with subgroup accuracies of 75.5% vs. 28.3% for 3–6 mm nodules, 62.0% vs. 28.2% for 6–8 mm nodules, and 77.6% vs. 55.3% for 8–10 mm nodules (44). In a separate DenseNet-based study, the deep learning model achieved an AUC of 0.964 in the internal test set (accuracy 93.4%, sensitivity 96.5%, specificity 90.8%) and 0.945 in external validation (accuracy 91.1%, sensitivity 97.7%, specificity 86.0%) (45). These findings demonstrate that current AI models are highly accurate and robust for distinguishing benign from malignant nodules, making them valuable tools for early lung cancer diagnosis. Beyond malignancy classification, preliminary studies have explored AI-based prediction of nodule growth. Tao et al. applied a convolutional neural network (CNN) model to simulate temporal changes in 115 GGNs and 198 SNs, achieving AUCs of 0.857 and 0.843 for distinguishing growing from non-growing nodules, and 0.892 for classifying high- vs. low-risk nodules based on predicted consolidation to tumor ratio (CTR) (46). Transformer-based longitudinal models have also been applied to tumor growth studies, producing reliable predictions of dynamic growth trends with strong performance metrics [root-mean-square error (RMSE) 11.32%, Dice 89.31%, recall 90.57%, specificity 89.64%] (47).

Although AI models have performed well in preliminary research, their translation into reliable clinical tools still faces multiple challenges such as validation, universality, and clinical integration. Currently, most models are developed based on retrospective single center data, and it is unclear whether they can be applied to different populations, imaging protocols, and scanning devices in the real world. Therefore, strict external validation is crucial in large-scale prospective multi center queues. In addition, the “black box” nature of many complex deep learning models leads to a lack of interpretability in the decision-making process, which weakens the trust of clinical doctors and hinders their application. In addition to algorithm performance, clinical applications also face practical challenges such as integrating with actual workflows and verifying the effectiveness of large-scale prospective trials in improving patient prognosis. Therefore, future research needs to shift from technical feasibility to clinical feasibility, ensuring that AI tools are accurate, robust, interpretable, and easy to integrate, ultimately achieving reliable applications in diverse clinical scenarios.

Conclusions

Therefore, the growth heterogeneity of solid nodular lung cancer has led to a shift in clinical management from a unified strategy to a personalized strategy that adapts to risk. It is important to acknowledge that VDT measurement can be influenced by variability in segmentation tools, differences in CT scan intervals, and reader interpretation, which may affect its precision and inter-study comparability.

Risk stratification based on growth and personalized monitoring intervals. For rapidly growing nodules (VDT <400 days), timely surgical intervention is necessary, and other adjuvant treatments can be used according to the pathological type after surgery. For slow growing nodules (VDT >400–600 days), delayed resection can be considered to avoid overtreatment. Therefore, integrating VDT assessment and prediction tools into the diagnostic workflow is crucial for optimizing results and resource utilization.

However, in clinical practice, the integration of imaging, AI, and multi omics faces significant feasibility and implementation barriers, including the need for multi center and prospective multi omics analysis, as well as the challenge of seamlessly embedding AI tools into existing clinical workflows. Addressing these conversion gaps is a prerequisite for the widespread application of personalized management in clinical practice.

Future research can mainly be divided into the following three directions: (I) developing and validating a model for predicting the growth heterogeneity of solid nodular lung cancer based on baseline imaging; (II) exploring the molecular mechanisms that cause growth heterogeneity, especially in adenocarcinoma; (III) developing clinical management strategies for solid nodular lung cancer with different growth risk stratification, and striving to improve patient prognosis. Validation of these methods requires large, prospective, multicenter studies. The study endpoints should be clearly defined, such as standardized protocols for imaging, segmentation, reduction of unnecessary intervention rates, and improvement in patient survival outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2697/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2697/prf

Funding: This work was supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (grant Nos. 2024ZD0520000 and 2024ZD0520002), Beijing Natural Science Foundation (grant No. 7222148), National Natural Science Foundation of China (grant No. 81971616).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1-2697/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.

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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