Beyond the surface: the hidden aggression of visceral pleural invasion in early-stage non-small cell lung cancer

Recent landmark trials (CALGB 140503 and JCOG0802/WJOG4607L) have established sublobar resection as the standard-of-care for peripheral, node-negative non-small cell lung cancer (NSCLC) ≤2 cm (1,2). Yet an important question persists: does visceral pleural invasion (VPI; i.e., tumor extension beyond the visceral pleura’s elastic layer) warrant more extensive resection in these small tumors? Since its incorporation into the tumor-node-metastasis (TNM) staging system’s T2 designation (7th edition), VPI has signaled aggressive biology, triggering tumor upstaging and influencing recommendations for adjuvant therapy. However, its prognostic significance remains contentious in NSCLC ≤2 cm, especially for sub-centimeter lesions.

Although VPI reliably predicts poorer survival in larger tumors, its impact on early-stage disease is unclear. Small tumor size inherently limits the prevalence of VPI, leaving evidence sparse and retrospective. This uncertainty fuels clinical debate: some surgeons advocate for lobectomy when VPI is suspected, while others argue that sublobar resection suffices. Resolving this issue requires prospective data, which poses a challenge given that current imaging methods rarely confirm VPI prior to resection.

Altorki et al. (3) addressed this knowledge gap through a secondary analysis of the CALGB 140503 trial, which randomized 697 patients with cT1N0 NSCLC (≤2 cm) to lobar vs. sublobar resection. With 7-year median follow-up, sublobar resection demonstrated non-inferior disease-free survival. Nevertheless, clinical staging relied on computed tomography (CT), which poorly detects VPI. Consequently, 16.2% of participants were upstaged to pT2 post-resection due to occult VPI. This patient subgroup was subsequently analyzed to evaluate oncological outcomes.

The analysis revealed important survival differences between pT1 and pT2 (VPI-positive) tumors. At 5 years, pT2 tumors showed markedly worse disease-free survival (53.3% vs. 65.9%; P=0.02) and recurrence-free survival (58.2% vs. 73.1%; P=0.01) compared to pT1 tumors. Furthermore, VPI correlated with elevated recurrence across all metrics: overall (41.6% vs. 27.6%), locoregional (15.0% vs. 10.8%), and distant (23.9% vs. 14.6%). Notably, the risk of recurrence persisted irrespective of resection extent, as lobectomy failed to outperform sublobar resection in mitigating VPI-associated recurrence.

These results, however, warrant scrutiny of the study’s statistical framework. While the authors appropriately adjusted for tumor size, histology, and smoking status using Cox models, several limitations obscure causal interpretation. Histology classification (grouped broadly as squamous cell carcinoma, adenocarcinoma, or other) may mask subtype-specific prognoses (e.g., indolent lepidic vs. aggressive micropapillary adenocarcinoma). Similarly, smoking status stratification (never, former, current) overlooks cumulative tobacco exposure, a well-established recurrence modifier (4). Additionally, lymphovascular invasion, a proven prognostic marker (5), was excluded from models, raising concerns that residual confounding may inflate the observed hazard ratios (HRs) attributed to VPI.

Finally, the absence of overall survival differences between pT1 and pT2 cohorts (P=0.32) should not downplay the clinical significance of VPI. As the trial was underpowered for this endpoint, and early-stage NSCLC survival outcomes often lag behind recurrence metrics (due to disease biology, salvage therapies, and comorbidities), this null finding remains inconclusive.

Taken together, these findings demand a dual focus: refining preoperative staging and redefining adjuvant strategies. While sublobar resection retains its viability even for VPI-positive tumors, two unresolved challenges loom: improving preoperative VPI detection to optimize surgical planning and determining whether adjuvant therapies could counteract VPI’s recurrence risk. Current trials risk overlooking these nuances unless they systematically stratify patients by VPI status. This study design is necessary for guiding combined surgical and systemic therapy protocols.

The study by Altorki et al. (3) reinforces the prognostic dominance of VPI across different tumor sizes. Even NSCLC ≤2 cm with VPI exhibited recurrence rates akin to larger tumors, confirming that pleural invasion (not diameter alone) drives aggressive biology. This challenges the notion that “small” equates to “indolent” and stresses the need to prioritize biological markers over anatomic size in risk stratification.

Nonetheless, translating this insight into practice hinges on overcoming a barrier: the elusiveness of VPI in preoperative staging. Conventional CT imaging fails here, as evidenced by a retrospective analysis where clinical and pathological T staging disagreed in 33% of cases [κ_w =0.302; 95% confidence interval (CI): 0.158–0.447] (6). Radiologic overdiagnosis of VPI (29% of discrepancies) and tumor size misclassification (21%) disproportionately affected T1 tumors, with 57% of pathologically confirmed T1 lesions wrongly staged as T2. Such inaccuracies risk overtreatment or undertreatment, highlighting the need for advanced imaging biomarkers to detect occult VPI.

Recent advances in CT imaging biomarkers offer incremental progress in preoperative VPI detection. A study of 342 subpleural nodules ≤3 cm found VPI in 31% of pleural-attached nodules vs. 16% of those with pleural tags (i.e., slender bridges of soft tissue extending between the nodule and pleura) (7). The jellyfish sign (i.e., multiple linear septations between the nodule and pleura) was the strongest predictor [odds ratio (OR) =21.60; 95% CI: 8.38–62.11; P<0.001], alongside pleural thickening (OR =6.57; 95% CI: 2.33–18.92; P<0.001) and larger pleural contact area (OR =1.05; 95% CI: 1.01–1.09; P=0.01). For pleural-tag nodules, multi-surface attachments carried an OR of 9.30 (95% CI: 2.70–38.35; P=0.001). These radiological features refine suspicion, but more accurate tools are still needed.

Artificial intelligence (AI) has the potential to bridge this gap. In a large study of clinical stage IA lung adenocarcinomas (n=676 in the training set, n=141 in a temporal validation set), a deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.75 for VPI prediction, matching radiologist performance (8). Remarkably, the model outperformed human readers in specific ranges of sensitivity or specificity, suggesting it can be adjusted to clinical demands. Another multicenter cohort (n=2,077) demonstrated that AI-predicted VPI correlates with reduced disease-free survival, mirroring the prognostic weight of histologically confirmed VPI (9). Even more compelling, radiomics nomograms integrating CT and clinical data report AUC up to 0.957 (10). These tools, though not yet practice-ready, signal a paradigm shift: VPI detection may soon transition from histologic surprise to preoperative certainty.

Despite the promise of AI-driven radiomics for preoperative detection of VPI, several challenges must be addressed before these tools can be widely adopted. First, regulatory pathways for algorithm approval are evolving, and prospective validation in diverse patient populations will be essential to ensure generalizability. Second, imaging protocols and acquisition parameters vary considerably across institutions, underlining the importance of standardization and data harmonization efforts. Third, AI models require robust, unbiased multicenter datasets for both training and testing, ideally with external validation cohorts to confirm reproducibility. Finally, outcomes-focused trials that integrate imaging biomarkers into clinical decision pathways, such as selecting patients for neoadjuvant or adjuvant therapy based on predicted VPI risk, could provide the definitive evidence needed to establish radiomics as a component of lung-cancer staging.

The high recurrence rates of VPI-positive tumors require further investigation. If sublobar resection and lobectomy fail to curb their aggressiveness, as Altorki et al. (3) observed, lymphatic biology may hold answers. A cadaveric study found that 55.8% of 380 dye injections into the subpleural area (the first draining visceral pleural lymphatic vessel) of each lung segment followed an intersegmental pathway (11). An ex-vivo evaluation of lobectomy specimens in 53 lung cancer patients revealed that peripheral tumor location is a risk factor for the intersegmental pathway of visceral pleural lymphatic drainage (OR =0.87; 95% CI: 0.79–0.95; P=0.003) (12). In some cases, lymphatic vessels run directly to mediastinal nodes or even across fissures to neighboring lobes (13), creating potential routes for locoregional spread that are not necessarily controlled by removing a single lobe. These observations may provide insight into the high recurrence rates of VPI-positive tumors and suggest that systemic therapies, rather than wider margins, could be important to prevent dissemination.

Recent evidence suggests that VPI may promote recurrence through multiple biological pathways beyond straightforward extension into pleural lymphatics. For example, VPI-positive tumors often display features of epithelial-mesenchymal transition, wherein cells lose their epithelial characteristics and gain invasive properties, potentially leading to earlier systemic spread and therapy resistance (14). Moreover, the immune microenvironment surrounding VPI-positive tumors may be compromised, reducing antitumor immune surveillance and further facilitating metastatic progression. In parallel, deeper pleural invasion (PL2) is associated with higher recurrence risk than superficial (PL1), implying that the extent of pleural breach can influence this aggressive behavior (15). Finally, programmed death-ligand 1 (PD-L1) expression and molecular alterations, such as epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), may modulate how VPI interacts with these pathways.

Growing evidence indicates that VPI-positive tumors may warrant adjuvant therapy; however, its application remains contentious. Current National Comprehensive Cancer Network guidelines classify VPI as a high-risk feature in stage IB NSCLC, recommending consideration of adjuvant chemotherapy. However, whether survival benefits persist across tumor sizes and populations remains contested.

A multicenter, retrospective study of 251 patients with stage IB (1–4 cm, VPI-positive) NSCLC reported significant chemotherapy benefits: improved 5-year recurrence-free survival (HR =0.57; 95% CI: 0.33–0.96; P=0.036) and overall survival (HR =0.22; 95% CI: 0.09–0.58; P=0.002), even for tumors <3 cm (HR =0.45, 95% CI: 0.24–0.84, P=0.013 for recurrence-free survival; and HR =0.27, 95% CI: 0.09–0.79, P=0.017 for overall survival) (16). These findings contrast with a National Cancer Database analysis of 61,454 patients with clinical stage I NSCLC (<4 cm), where adjuvant chemotherapy improved 5-year overall survival only in VPI-positive tumors ≥3 cm (68.8% vs. 49.9%; P<0.001), with smaller tumors deriving statistically marginal benefit (17).

The issue becomes more complex with the inclusion of further registry data. A survival analysis of 30,858 NSCLC patients included in the Surveillance, Epidemiology, and End Results database found chemotherapy beneficial only for node-positive disease or tumors ≥4 cm, which are traditionally considered high-risk groups (18). Among node-negative NSCLC <4 cm, VPI failed to independently predict prognosis, questioning its utility as a standalone criterion for adjuvant chemotherapy. Conversely, a multicenter, retrospective series of 1,278 patients with pathological stage I NSCLC identified VPI (alongside tumor size of 2–4 cm and lymphovascular invasion) as a marker of chemotherapy responsiveness, with treated patients achieving superior 5-year recurrence-free survival (81.4% vs. 73.8%; P=0.023) and overall survival (92.7% vs. 81.7%; P<0.0001) (19). These differing results (Table 1) emphasize the need for prospective trials stratifying by VPI status to determine which patients, especially those with tumors under 2 cm, benefit from adjuvant therapy.

The study by Altorki et al. (3) solidifies VPI as a significant prognostic marker in early-stage NSCLC and calls for a shift from size-based management to biology-driven strategies. Sublobar resection remains standard for node-negative tumors ≤2 cm; however, the presence of VPI may necessitate multimodal approaches. The challenge lies in refining risk stratification to account for the biological heterogeneity of VPI, which might be amplified by coexisting factors, such as histological subtype, lymphovascular invasion, and molecular alterations. Emerging data hint at selective chemotherapy benefits for tumors ≤2 cm with high-risk features, yet the absence of prospective trials stratifying by VPI status leaves gaps in defining which patients merit adjuvant therapy. Future protocols must integrate pleural invasion depth (PL1 vs. PL2) with molecular profiling to distinguish indolent from aggressive VPI-positive subsets, ensuring therapies align with biological risk.

To address these challenges, advanced trial designs must bridge the gap between surgical and systemic oncology. Adaptive frameworks, such as basket trials stratifying VPI-positive patients by PD-L1 expression or driver mutations (e.g., EGFR, ALK), could accelerate the application of immunotherapy or targeted therapies tailored to this subgroup. Concurrently, trials leveraging preoperative deep learning algorithms to detect radiological VPI could clarify whether neoadjuvant therapy mitigates micrometastatic spread. To ensure clinical readiness, such AI-based tools should undergo prospective validation with predefined performance benchmarks (for example, achieving >80% sensitivity and specificity for VPI detection across diverse patient populations). Additionally, measuring circulating tumor DNA during or after therapy may help identify minimal residual disease in VPI-positive tumors, further guiding the intensity and duration of adjuvant treatments. Such approaches would isolate the prognostic impact of VPI and test interventions against its distinct biology.

Central to these efforts is the translation of diagnostic advances into clinical practice. The integration of computed tomography (CT) biomarkers with AI-based imaging analysis holds the potential to shift VPI from being an unexpected postoperative finding to a predictable preoperative factor. This shift could enable tailored neoadjuvant strategies, allowing clinicians to weigh resection against systemic therapy prioritization before surgery. Realizing this potential depends on multidisciplinary collaboration: radiologists refining detection algorithms to reduce staging discrepancies, surgeons optimizing resection boundaries, pathologists identifying molecular correlates of VPI aggression, and oncologists tailoring adjuvant regimens to molecular and radiographic risk profiles.

Managing VPI-positive NSCLC illustrates the limits of isolated decision-making. By uniting advances in imaging, molecular profiling, and adaptive trial design, the thoracic oncology community can redefine VPI from a static histologic finding to a dynamic biomarker that guides precision therapy. Only through such integration will we transform this prognostic threat into a therapeutic opportunity, ensuring survival gains match the biological complexity of early-stage disease.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, AME Clinical Trials Review. The article has undergone external peer review.

Peer Review File: Available at https://actr.amegroups.com/article/view/10.21037/actr-25-34/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://actr.amegroups.com/article/view/10.21037/actr-25-34/coif). S.L. serves as an unpaid editorial board member of AME Clinical Trials Review from August 2023 to July 2025. The other author has 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.

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