Search Results (283)

¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used for oncologic staging, restaging, and response assessment. Its CT component frequently reveals pulmonary lesions that are not the principal target of the examination. In patients with known malignancy, such lesions may have a higher pre-test probability of malignancy than incidental pulmonary lesions detected in the general population and cannot be reliably characterized by CT morphology or FDG uptake alone. Small lesion size, partial-volume effects, respiratory motion, and low tumor cellularity may render metastases metabolically occult, whereas infectious and inflammatory processes may show intense FDG uptake and mimic malignancy. This review presents a case-based, lesion-by-lesion approach integrating thin-section CT morphology, concordance or discordance between FDG uptake and CT findings, interval evolution, and oncologic and clinical context. Illustrative cases demonstrate PET-occult pulmonary metastases, inflammatory mimics, coexisting infection and metastasis, synchronous or metachronous primary lung cancer, and subsolid nodules representing either atypical metastases or primary lung adenocarcinoma. Subsolid or cavitary morphology, waxing-and-waning interval changes, and low FDG uptake should not prompt premature benign labeling. Systematic evaluation of the CT component, followed by lesion-based integration of imaging and clinical data, is essential for characterizing these lesions and may help reduce both false-positive and false-negative interpretations. Full article

Background: Patients presenting with concurrent lung and brain lesions pose a unique diagnostic and therapeutic challenge. The role of neurosurgical resection in the management of de novo non-small-cell lung cancer brain metastases (NSCLC-BM) remains poorly defined. Methods: Consecutive patients undergoing resection for NSCLC-BM between February 2018 and October 2025 were retrospectively analyzed. De novo NSCLC-BM was defined as radiographic detection of pulmonary and intracranial lesions concurrently or within 1 month during the initial diagnostic work-up. Clinical characteristics and outcomes were compared between de novo and metachronous brain metastases (BM). Multivariable Cox regression identified factors associated with overall survival (OS). Factors associated with long-term postoperative survival in the de novo subgroup were further explored using Firth logistic regression. Results: Among 248 surgically treated patients, 113 (45.6%) presented with de novo BM. Compared with metachronous BM, de novo patients more frequently presented with symptomatic and multifocal intracranial disease. Among 73 de novo patients with evaluable survival, median postoperative OS was 57.0 months (95% CI, 22.9–not reached). Postoperative OS and intracranial progression-free survival (iPFS) did not differ significantly between the two groups, and de novo presentation was not independently associated with postoperative survival. Driver-positive status and lower intracranial tumor burden were associated with durable postoperative survival in the de novo subgroup. Conclusions: Neurosurgical resection represents a reasonable option for appropriately selected patients with de novo NSCLC-BM. These findings provide real-world evidence to inform multidisciplinary, individualized decision-making in patients presenting with concurrent lung and brain lesions. Full article

Background: Endobronchial ultrasound (EBUS) is widely used for the diagnosis of pulmonary lesions. When combined with rapid on-site cytologic evaluation (ROSE), it can improve diagnostic accuracy and facilitate early identification of malignancy origin. However, differentiating primary lung cancer from metastatic tumors (e.g., breast or colorectal origin) during ROSE remains challenging due to limited cytopathology support and morphological similarities between tumor cells. This study aimed to develop a computer-aided diagnostic (CAD) system for classifying malignancy types from limited cytological samples to facilitate clinical decision-making. Methods: We utilized a retrospective dataset of cytological images obtained during EBUS procedures between November 2018 and June 2024. The study focused on classifying three malignancies: pulmonary adenocarcinoma, metastatic breast cancer, and metastatic colorectal cancer. A deep learning-based model (PLFCH) was developed, integrating few-shot learning, parameter-efficient fine-tuning, and a hybrid CNN–Transformer architecture to address limited annotated data. Results: A total of 41 patients with 346 cytological images were included. Under a 3-way 5-shot setting, the proposed model achieved an accuracy of 49.26%, outperforming existing few-shot learning methods, with precision, recall, and F1-score of 0.477, 0.493, and 0.480, respectively. Increasing the number of reference images to 20 per class further improved accuracy to 55.48%. Conclusions: The proposed framework demonstrated improved performance for cytological classification under limited data conditions. These findings provide an early proof of concept for applying few-shot learning to cytological assessment during EBUS procedures. Further improvements in model performance and validation in prospective, real-time clinical settings are required before its potential role in assisting diagnostic decision-making can be established. Full article

Acquired resistance to targeted therapies is inevitable in EGFR-mutant non-small cell lung cancer (NSCLC), yet the principles governing its emergence in vivo remain incompletely understood. In particular, how lesion-level response patterns vary across distinct EGFR allele contexts during therapy has not been systematically examined at single-lesion resolution. Here, we establish a longitudinal in vivo imaging platform enabling single-lesion resolution tracking of tumor behavior during therapy in genetically engineered mouse models representing clinically relevant EGFR alleles. Using high-resolution micro-computed tomography (micro-CT) and three-dimensional reconstruction, we monitor tumor growth, therapeutic response, and resistance during osimertinib treatment. EGFR genotype is associated with distinct patterns of tumor growth, response kinetics, and resistance timing. Therapeutic response is spatially heterogeneous, with coexisting lesions undergoing complete regression, persistence, or progression within the same lung. During treatment, spatially distinct lesion-level behaviors included persistent growth during therapy and initial regression followed by regrowth. These findings demonstrate the utility of longitudinal micro-CT imaging to investigate allele-associated differences in treatment response and resistance timing at single-lesion resolution in vivo. Full article

Background/Objectives: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) is the preferred imaging modality for evaluating pulmonary nodules because of its high spatial resolution; however, it primarily provides morphological information, including lesion size, shape, and density. Dual-energy CT (DECT), a more recent imaging technique, uses two different energy levels to enable material decomposition and quantitative parameter assessment. These parameters may provide additional information regarding tumor perfusion, vascularization, and tissue composition. This rapid review aimed to evaluate the current evidence regarding the clinical utility of DECT in the detection, characterization, and staging of lung tumors. Methods: This rapid review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was performed in the PubMed and Cochrane Library databases for studies published between 2005 and 2026. Studies were included if they evaluated the detection, characterization, or staging of lung tumors using quantitative DECT parameters. Case reports, editorials, duplicate studies, and studies without quantitative DECT data were excluded. Descriptive data analysis was performed using Microsoft Excel. Results: A total of 24 studies were included, comprising 18 retrospective (75%) and 6 prospective studies (25%). Only one study evaluated the role of DECT in lung tumor detection, demonstrating improved detection of mixed ground-glass nodules and invasive adenocarcinoma. Significant correlations were found between iodine uptake and tumor perfusion, highlighting the potential of DECT to improve differentiation between benign and malignant lesions. Several studies also demonstrated associations between DECT parameters and tumor biomarkers, including Ki-67 Proliferation Index (Ki-67) expression, Epidermal Growth Factor Receptor (EGFR) mutation status, Programmed Death-Ligand 1 (PD-L1) expression, and treatment response in non-small cell lung cancer. In addition, DECT provided complementary metabolic information regarding tumor malignancy and showed correlations between iodine uptake and fluorodeoxyglucose (FDG) parameters. Associations between iodine volume and tumor differentiation grade were also reported. One study demonstrated the potential role of DECT in tumor staging by predicting mediastinal lymph node metastasis. Across all included studies, iodine-based parameters (50%), radiomics and material decomposition parameters (16.67% each), and spectral attenuation parameters (12.50%) were the most frequently investigated DECT metrics. Conclusions: DECT appears to be a promising complementary imaging technique that provides quantitative perfusion-related and compositional surrogate information beyond the morphological assessment offered by conventional CT. However, the current evidence remains heterogeneous and is largely based on retrospective studies with relatively small patient cohorts. Larger prospective studies with standardized imaging protocols are necessary to further establish the clinical utility of DECT in lung tumors. Full article

Objectives: Precise lymph node staging remains a cornerstone in the management of early-stage and locally advanced non-small cell lung cancer (NSCLC), directly influencing surgical planning and multimodal therapy. Despite the widespread use of 2-[18F]FDG PET/CT, occult nodal metastases frequently lead to unexpected upstaging after surgery, potentially affecting prognosis and therapeutic strategies. This study aimed to investigate whether radiomic features derived from preoperative PET/CT scans can predict nodal involvement in patients with early-stage lung cancer. Methods: A retrospective analysis was conducted on 124 patients with cT1N0 NSCLC who underwent 2-[18F]FDG PET/CT scans as part of the preoperative workup, followed by anatomical lung resection and systematic mediastinal lymph node dissection. Radiomic features were extracted from PET predictive of pathological nodal upstaging. Results: During the study period, 67 patients who underwent anatomical lung resection for early-stage lung cancer demonstrated unexpected nodal metastasis; a continuous series of 57 patients with the same clinical TMN was enrolled as a control group. Several radiomic parameters were significantly associated with nodal upstaging. According to variable importance (VIMP) analysis, metabolic tumor volume (MTV), total lesion glycolysis (TLG), run-length non-uniformity (RLNU), and gray-level non-uniformity (GLNU) emerged as the strongest predictors of lymph node involvement. Conclusions: Although the clinical utility of these findings remains to be validated, radiomic analysis of 2-[18F]FDG PET/CT imaging offers non-invasive biomarkers that may enhance the preoperative prediction of nodal involvement in early-stage NSCLC. Integrating radiomics into clinical workflows could improve surgical decision-making, refine patient selection, and reduce the incidence of unforeseen nodal upstaging. Full article

Dynamic transcriptomic remodeling of epithelial cells represents a critical hallmark of lung adenocarcinoma (LUAD) progression, yet epithelial-cell progression signatures (ECPSs) linked to this process remain incompletely characterized, impeding our understanding of malignant LUAD transformation. Here, we performed a multi-resolution multi-omics study and identified two functionally opposing ECPSs: a Cancer-Promoting Signature (CPS) and a Cancer-Suppressing Signature (CSS). The CPS was progressively upregulated from normal to early- and advanced-stage lesions, while the CSS was gradually downregulated, and both were validated by multi-resolution transcriptomic data. Both the CPS and CSS demonstrated robust diagnostic value for LUAD, particularly in early-stage detection (median AUC > 0.95). In seven independent validation cohorts, the CPS and CSS served as robust prognostic risk and protective factors, respectively. Their combination could better stratify LUAD patients into distinct prognostic subgroups, with the CPS_high–CSS_low subgroup showing the worst prognosis, accompanied by high genomic instability, an immunosuppressive tumor microenvironment, and resistance to chemotherapy. Notably, the CPS and CSS exhibited broad translational value in other epithelium-derived cancers. HMGA1, a key CPS gene, showed epithelium- and advanced-stage-specific high expression, which was significantly associated with poor prognosis, genomic instability, and immune escape. Collectively, our study identifies the CPS and CSS as clinically reliable ECPSs, providing valuable biomarkers for clinical application to LUAD. Full article

Bone metastasis is often treated clinically as a late complication of advanced cancer, yet accumulating evidence indicates that it is also a spatial evolutionary process shaped by clonal selection, niche adaptation, dormancy, and reseeding. This review examines BoM through a phylogeographic framework that links tumor ancestry with anatomical location and time. We discuss how heterogeneous primary tumors generate bone-tropic subclones, how circulating tumor cells pass through dissemination bottlenecks, and how disseminated tumor cells enter perivascular and endosteal niches that either maintain dormancy or support early micrometastatic outgrowth. We then compare clonal architectures across breast, prostate, lung, and renal cell carcinomas, emphasizing both lineage-specific programs and convergent bone-adaptive states, including osteomimicry, immune evasion, metabolic plasticity, and epigenetic remodeling. Methodological platforms such as multiregion sequencing, single-cell and spatial transcriptomics, lineage tracing, and liquid biopsy are evaluated with attention to the technical limitations imposed by mineralized tissue. Finally, we consider how bone lesions may function as reservoirs for secondary dissemination and how evolutionary thinking could improve biomarker development, dormancy prediction, trial design, and therapy selection. Viewing BoM as an evolving ecosystem may help shift the field from reactive skeletal management toward earlier, biology-informed intervention. Full article

Neoadjuvant immunochemotherapy (NICT), mainly anti-PD-1/PD-L1 therapy combined with cytotoxic chemotherapy, significantly improved perioperative outcomes for resectable solid tumors such as lung cancer and breast cancer. But a large number of patients still had residual lesions and eventually relapsed. Residual tumor cells are not simply unremoved cellular debris, but represent a therapy-selected and therapy-amplified subset of a pre-existing heterogeneous and plastic tumor ecosystem. To avoid implying that therapy generates a new form of tumor plasticity de novo, we use the term “plasticity of non-apoptotic residual tumor cells” to describe the plastic behavior of viable malignant cells that survive treatment-induced cytotoxicity rather than entering apoptosis. In this review, we define the plasticity of non-apoptotic residual tumor cells as the capacity of residual malignant cells to preserve, switch, or re-enter phenotypic states such as dormancy, hybrid EMT, stem-like regeneration, and immune evasion under the combined influence of intrinsic tumor heterogeneity, systemic therapy pressure, and microenvironmental protection. Before the NICT-specific discussion, we outline general theoretical frameworks including therapeutic stress response, apoptosis-induced regeneration, genetic and non-genetic heterogeneity, as well as spatial heterogeneity of involved lymph nodes, so as to provide a more robust interpretation of residual lesion biology under NICT. Also, this review proposes that residual disease may be reconceptualized as a treatment-shaped plastic niche, whose biological behavior is jointly shaped by clonal selection, reversible phenotypic transformation, and microenvironmental ecological protection. We summarize several key states of residual tumor cells: persistent-like/resting state, hybrid EMT/invasive plasticity state, stem-like/regenerative state, and immune escape state, and elucidate the underlying epigenetic basis, including DNA methylation, histone modification, chromatin remodeling, and non-coding RNA network reprogramming. Meanwhile, niche factors such as immune stress, CAF/TAM enrichment, fibrotic matrix, hypoxia, and metabolic stress can further stabilize these states and promote the survival of relapse seeds. Based on this, we propose that future postoperative assessments should be upgraded from residual volume to a stratified residual state, and dynamically identified by combining single-cell omics, spatial pathology, and ctDNA/MRD monitoring. Furthermore, treatment strategies should shift from simply shrinking tumors to plasticity-locking therapy, that is, identifying, classifying, and blocking the plasticity escape pathways of residual lesions before they evolve into recurrence. Full article

Background/Objectives: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, representing nearly 85% of cases worldwide. Predicting patient response before treatment, however, remains a major clinical challenge. Radiomics enables non-invasive extraction of quantitative imaging data that reflects tumor characteristics and heterogeneity. In this study, we developed and externally validated a CT-derived radiomic signature for predicting outcomes in advanced NSCLC patients treated with first-line platinum-based chemotherapy. Methods: NSCLC across three tertiary care centers were included. Tumor lesions from baseline contrast-enhanced CT scans were semi-automatically outlined using 3D Slicer software (version 5.12.2), followed by the extraction of 851 quantitative imaging biomarkers through PyRadiomics version 3.0. The extracted parameters comprised histogram-based features, morphological measurements, texture-related variables, and wavelet-derived attributes. Reliability of feature extraction was evaluated using intraclass correlation coefficient analysis, whereas LASSO regression together with stability selection was applied to identify the most relevant predictors. Predictive capability was assessed across five machine learning techniques, including logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron models. The resulting radiomics-based composite score was then tested in an independent external validation cohort. Response to treatment was determined according to RECIST 1.1 guidelines. Results: After feature reproducibility assessment and stability screening, 421 radiomic parameters were retained for further analysis and 14 feasible parameters were selected by LASSO. There was good predictive power of the integrated model that included both radiomic and clinical features, with AUC values of 0.876, 0.849, and 0.831 in the training set, internal validation set and external validation set, respectively. The radiomic signature successfully differentiated patients according to their probability of therapeutic response. Furthermore, by using decision-curve analysis, the combined radiomics nomogram was more clinically useful than models based on clinical characteristics within clinically relevant decision-threshold ranges with greater net benefit. Conclusions: This pre-treatment CT-based radiomics signature demonstrates potential as a decision-support tool for chemotherapy-response prediction in NSCLC. However, prospective and multi-ethnic validation is required before clinical application. Based on this data, larger multi-ethnic cohorts should be considered for prospective validation. Full article

Background: Lung adenocarcinoma (LUAD) develops through a stepwise pathological status from atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IA). Although malignant epithelial evolution during this process has been increasingly characterized, the dynamic remodeling of cancer-associated fibroblasts (CAFs) and their contribution to the immunosuppressive tumor microenvironment (TME) remain incompletely explored. Methods: Single-cell RNA sequencing data from treatment-naïve LUAD lesions, including AAH, AIS, MIA, and IA, were analyzed together with external bulk transcriptomic cohorts. CAF subsets were characterized according to their transcriptional features, inferred developmental states, transcription factor activity, functional programs, and predicted cell–cell interactions. Ligand–receptor analysis was used to examine MIF-related communication between epithelial cells and CAFs. MIF-related genes were then used to develop a machine learning-based prognostic signature in the TCGA-LUAD cohort, followed by validation in independent GEO cohorts. Results: Single-cell transcriptomic analysis of 131,639 cells from 25 treatment-naïve LUAD lesions identified six CAF subtypes, including alveolar CAFs, antigen-presenting CAFs, extracellular matrix CAFs, EndMT-like CAFs, inflammatory CAFs, and myofibroblastic CAFs. CAF composition differed across pathological stages, with MIA lesions showing a distinct enrichment of eCAFs and reduced proportions of inflammatory and myofibroblastic CAF populations. Compared with pre-invasive lesions, IA lesions exhibited increased proportions of exhausted CD4+ and CD8+ T cells together with reduced cytotoxic features. Cell–cell communication analysis identified enhanced epithelial–CAF interactions in IA, including enrichment of MIF-CD74/CD44 signaling. Based on MIF-related genes, a machine learning prognostic signature was developed and validated in independent cohorts, consistently stratifying patients into distinct risk groups with significantly different survival outcomes. Conclusions: These findings suggest that CAF-related stromal remodeling is associated with immune suppression during LUAD progression. MIF-mediated epithelial–CAF communication may be involved in the formation of an immunosuppressive microenvironment and is associated with poor prognosis. The MIF-related signature may provide a useful approach for prognostic stratification in LUAD. Full article

Background/Objectives: Intraoperative molecular imaging (IMI) enables real-time visualization of tumor biology during surgery using fluorescent probes and near-infrared imaging systems. As lung cancer screening increases detection of small and nonpalpable pulmonary nodules, conventional localization and margin assessment techniques remain limited, particularly during minimally invasive surgery. This review summarizes the technical foundations, imaging agents, clinical applications, and future directions of IMI in thoracic oncology. Methods: We performed a narrative review to synthesize current evidence regarding the technical foundations, molecular imaging agents, clinical applications, and future directions of intraoperative molecular imaging in thoracic oncology. Given the multidisciplinary scope of the field, a narrative approach was selected to integrate mechanistic, translational, and clinical evidence rather than to answer a single narrowly defined clinical question. Results: IMI generates dynamic intraoperative contrast based on preferential probe accumulation or activation within malignant tissue. Current approaches include non-specific fluorophores such as indocyanine green, activatable probes targeting tumor-associated proteases or acidic microenvironments, and receptor-targeted agents such as pafolacianine. Across prospective studies and multicenter trials, IMI improved localization of nonpalpable lesions, identified occult synchronous malignancies, and enhanced intraoperative margin assessment, frequently altering surgical management. Phase 2 and 3 studies of folate receptor-targeted imaging demonstrated clinically significant findings in a substantial proportion of patients, including lesions not detected by conventional imaging or palpation. However, performance remains dependent on tumor biology, target expression, lesion depth, and optical constraints. Conclusions: IMI represents an emerging transition from anatomy-guided toward biology-informed thoracic surgery by providing real-time molecular information during resection. Current evidence supports its role as a complementary intraoperative technology that augments conventional imaging and surgical techniques, particularly for small, peripheral, and nonpalpable lesions. Full article

MET pathway alterations have evolved from a niche translational interest into one of the most clinically actionable axes in non-small cell lung cancer (NSCLC). Three biologically distinct lesions—MET exon 14 (METex14) skipping mutations, focal high-level MET amplification, and c-Met protein overexpression—are now individually targetable, each with its own diagnostic prerequisites and therapeutic class. Selective type Ib MET tyrosine kinase inhibitors (capmatinib, tepotinib) anchor first-line therapy for METex14, while next-generation agents and type II inhibitors are being developed to address on-target D1228 and Y1230 resistance mutations. In parallel, MET amplification has emerged as a leading mechanism of acquired resistance to osimertinib in EGFR-mutated NSCLC, with the SAVANNAH, SACHI, and INSIGHT 2 trials providing biomarker-guided combination strategies. The 2025 accelerated approval of telisotuzumab vedotin for c-Met-overexpressing tumors expanded the therapeutic armamentarium beyond kinase inhibition. Despite these advances, lineage plasticity, polyclonal bypass signaling, and inconsistent diagnostic thresholds for MET amplification continue to limit durable benefit. This review integrates the molecular biology, current clinical evidence, resistance mechanisms, and a proposed 2025 treatment algorithm for MET-altered NSCLC, with emphasis on the translational interface between mutation class, drug class, and emerging combinatorial approaches. As a narrative review, it synthesizes peer-reviewed literature and pivotal trial and regulatory data through early 2026, identified by structured searches of PubMed and major oncology congress proceedings, and prioritizes sources that link mutation class to drug class and resistance mechanism. Full article

Background/Objectives: Brain metastases (BMs) are a major cause of morbidity and mortality in non-small-cell lung cancer (NSCLC). In this setting, EGFR-mutant and ALK-rearranged tumors represent clinically actionable, CNS-relevant oncogenic subgroups for which matched TKIs are essential to management, yet lesion-level molecular profiling is not always feasible or immediately available. We aimed to develop and externally validate DriverNet, a clinical and MRI-based framework for noninvasive pre-treatment molecular triage of EGFR/ALK status in NSCLC-BM. Methods: In this multicenter study, we analyzed pretreatment clinical, T1CE and T2-FLAIR MRI data to develop unimodal radiomics, 2D/2.5D deep learning (DL), and multimodal fusion models. The final model used ImageNet-pretrained CNNs for feature extraction and a Transformer-based architecture for fusion. The primary cohort was split strictly at the patient level before slice extraction and model development, and two independent external cohorts were used for testing. Clinical-only, imaging-only, and clinical-imaging models were compared using discrimination, calibration, Brier score, and decision-curve analyses. Model interpretability and exploratory prognostic stratification were also assessed. Results: A total of 374 patients from three centers were included. Center 1 comprised 224 patients (Chinese) and was divided into training (n = 179, EGFR/ALK+ 55.3%) and internal validation (n = 45, EGFR/ALK+ 57.8%) sets. External cohorts included 54 patients from Center 2 (Chinese, test 1, EGFR/ALK+ 42.6%) and 96 from Center 3 (Western, test 2, EGFR/ALK+ 12.5%). Among all evaluated models, DriverNet achieved the best overall performance, with AUCs of 0.967, 0.947, 0.962, and 0.952 in the training, internal validation, and two external cohorts, respectively, outperforming the clinical-only and imaging-only models. Model-derived labels were also associated with overall survival in exploratory analyses. Conclusions: DriverNet is a clinical and MRI-based framework for noninvasive pre-treatment molecular triage in NSCLC-BM. It may provide complementary information for future molecular triage studies when lesion-level profiling is unavailable or delayed. Prospective validation in larger and more molecularly balanced cohorts remains necessary before any clinical implementation can be considered. Full article

Background: Spread through air spaces (STAS) is a recognized histologic pattern of invasion associated with poor prognosis in non-small-cell lung cancer (NSCLC), particularly adenocarcinoma. However, its presence in pulmonary squamous cell carcinoma (SCC), whether primary or metastatic, remains largely unexplored. Given the limited available evidence, this study was designed as an exploratory analysis to evaluate the prevalence and potential prognostic significance of STAS in pulmonary SCC. Material and Methods: In this exploratory retrospective study, we analyzed 57 patients who underwent surgical resection for pulmonary squamous cell carcinoma (P-SCC) at the Department of Thoracic Surgery at the University Hospital Erlangen between 2008 and 2020. The cohort included both primary lung SCC and metastatic SCC to the lung from extrapulmonary sites, primarily from ear, nose, and throat (ENT) tumors. Histological slides were reviewed to assess the presence of STAS, as defined by established morphological criteria. The Chi-square test was used to investigate the presence of STAS. Disease-free survival (DFS) and overall survival (OS) was evaluated using Kaplan–Meier analysis, and the prognostic impact of STAS along other variables were assessed using Cox proportional hazards regression. Results: A total of 57 patients with squamous cell carcinoma (SCC), 22 (39%) had primary lung SCC and 35 (61%) had metastatic SCC from head and neck tumours (ENT). Spread through air spaces (STAS) was detected in 20 patients (35%). Disease-free survival (DFS) differed according to primary tumour location (p-value of 0.009), with higher 1-, 3-, and 5-year DFS in patients with primary lung SCC (86.4%, 77.3%, 63.3%) than in those with head and neck SCC (54.3%, 31.4%, 22.2%). DFS was also significantly higher in patients undergoing solitary resections compared with multiple resections (78.6%, 64.3%, 49.5% vs. 33.3%, 6.7%, not estimable; p-value < 0.001). DFS was slightly longer in STAS-negative patients compared with STAS-positive patients (1-, 3-, 5-year DFS: 64.9%, 51.4%, 40.5% vs. 70%, 45%, not estimable), (median DFS 36 vs. 25 months; p-value of 0.776). Overall survival (OS) was significantly longer in patients with primary lung SCC (median OS 125 months) than in those with head and neck SCC (27 months; p-value of 0.039). STAS-negative patients had also a longer OS than STAS-positive patients (median OS 46 vs. 38 months; HR = 1.11, 95% CI 0.56–2.20; p-value of 0.771). Conclusions: STAS was identified in metastatic pulmonary SCC lesions as well as in primary lung SCC, occurring in approximately one-third of cases. However, due to the limited cohort size and the exploratory univariate design of the study, the prognostic significance of STAS could not be definitively established and requires further investigation in larger, adequately powered studies. Full article