Spread Through Air Spaces Research Articles

Habitat-based CT radiomics enhances the ability to predict spread through air spaces in stage T1 invasive lung adenocarcinoma.

Spread through air spaces (STAS) represents a novel invasive pattern in lung adenocarcinoma (LUAD) and is a risk factor for poor prognosis in stage T1 LUAD. This study aims to develop and validate a CT habitat imaging analysis model for predicting STAS in stage T1 invasive LUAD. We retrospectively analyzed 217 patients with preoperative stage T1 invasive LUAD (115 STAS-positive and 102 STAS-negative cases, including 151 in the train set and 66 in the test set). Semi-automatic segmentation was performed on the regions of interest (ROIs) in all CT images, with an automatic 3mm expansion around the tumor, considering the intratumoral and peritumoral 3mm area. This area was divided into three sub-regions via K-means clustering, and 1197 radiomic features were extracted from each sub-region and the overall combined region. After dimension reduction through the Mann-Whitney U test, Pearson correlation analysis, and least absolute shrinkage and selection operator(LASSO), the best features for each sub-region and overall were selected. Models were then built using the selected radiomic features through the Adaptive Boosting (AdaBoost) and Multilayer Perceptron (MLP) classifiers. Four different models were established based on different sub-regions and the overall features. The performance of these models was evaluated through receiver operating characteristic curves (AUC) under the DeLong test, calibration curves via the Hosmer-Lemeshow test, and decision curve analysis to assess the performance of these features. In this study, we evaluated the predictive performance of AdaBoost and MLP classifiers on rad feature models across various subregions and the overall dataset. In the test set, the AdaBoost classifier achieved a maximum AUC of 0.871 in Habitat 3, whereas the MLP classifier demonstrated slightly superior performance with an AUC of 0.879. Both classifiers exhibited high efficiency in habitat 3, with the MLP algorithm showing enhanced model performance. CT habitat imaging analysis for the preoperative prediction of STAS in stage T1 invasive LUAD shows satisfactory diagnostic performance, with the habitat3 model exhibiting the highest efficacy, reflecting tumor heterogeneity.

How Morphology Shapes Survival in Invasive Squamous Cell Carcinoma of the Lung.

Squamous cell carcinoma (SQCC) represents a significant proportion of human malignancies affecting various anatomical sites, including the lung. Understanding the prognostic factors is crucial for establishing effective risk stratification in these patients, as multiple critical aspects significantly impact overall survival. A retrospective study was conducted on 99 patients with operable lung SQCC treated at a tertiary center. The exclusion criteria included patients under 18, those with in situ or metastatic SQCC, and those who received neoadjuvant therapy. The surgical specimens were re-analyzed, and data were collected on multiple variables, including pTNM staging, tumor characteristics, and overall survival (OS). The Kaplan-Meier survival analysis and Cox regression models were used to identify significant prognostic factors. The Kaplan-Meier analysis showed a median survival of 36 months with a 65.65% mortality rate. Significant factors influencing survival included keratinization, histological grading, tumor size and stage, pleural invasion, tumor cell arrangement, tumor budding, spread through air space (STAS), and mitotic index. A multiple Cox regression highlighted the nonkeratinizing tumors, advanced pT stages, single-cell invasion, and high mitotic index as key predictors of poorer outcomes. The nonkeratinizing tumors showed higher mortality and shorter median survival rates compared to keratinizing tumors. The tumor staging, cell arrangement, and tumor budding significantly impacted the survival curves. The study underscores the importance of detailed histopathological evaluations in lung SQCC. The nonkeratinizing tumors, advanced pT stage, single-cell invasion, and high mitotic index were associated with higher hazard rates, emphasizing the need for a comprehensive grading system incorporating these factors to improve prognostic accuracy and guide treatment strategies.

Spatial analyses revealed S100P + TFF1 + tumor cells in spread through air spaces samples correlated with undesirable therapy response in non-small cell lung cancer

Spread through air spaces (STAS) is a recognized aggressive pattern in lung cancer, serving as a crucial risk factor for postoperative recurrence. However, its phenotype and related spatial structure have remained elusive. To address these limitations, we conducted a comprehensive study based on spatial data, analyzing over 30,000 spots from 14 non-STAS samples and one STAS sample. We observed increased proliferation activities and angiogenesis in STAS, identifying S100P as a potential biomarker for STAS. Furthermore, our investigation into the heterogeneity of STAS tumor cells revealed a subset identified as S100P + TFF1 +, exhibiting a negative impact on patients' survival in public datasets. This subtype exhibited the highest activities in the TGFb and hypoxia, suggesting its potential pro-tumor role within the tumor microenvironment. To assess the role of S100P + TFF1 + tumor cells in therapy response, we included data from two clinical trial cohorts (BPI-7711 for EGFR-TKI therapy and ORIENT-3 for immunotherapy). The presence of S100P + TFF1 + tumor cells correlated with worse responses to both EGFR-TKI therapy and immunotherapy. Notably, TFF1 emerged as a serum marker for predicting EGFR-TKI response. Cell–cell communication analysis revealed that the TGFb signaling pathway was the most activated in S100P + TFF1 + tumor cells, with TGFB2-TGFBR2 identified as the main ligand-receptor pair. This was further validated by multiplex immunofluorescence performed on twenty NSCLC samples. In summary, our study identified S100P as the biomarker for STAS and highlighted the adverse role of S100P + TFF1 + tumor cells in survival outcomes.

Feasibility of intraoperative assessment of STAS in pathologic stage 1 lung adenocarcinomas in Chinese patients

BackgroundIntraoperative assessment of tumor spread through air spaces (STAS) in early-stage lung adenocarcinomas (ADC) has been proposed to stratify patients for surgical management. However, data on the accuracy and reproducibility of detecting STAS on frozen sections (FS) and the prognostic value of STAS on FS remain limited and contradictory. MethodsWe conducted a retrospective study on the feasibility of intraoperative assessment of STAS by comparing the STAS patterns identified on FS and permanent sections from 524 patients diagnosed with pathologic stage 1 lung ADC. We also evaluated the association between STAS with patients’ clinicopathological characteristics and their postoperative survival outcomes. ResultsSTAS was identified in 117 out of 524 patients (22.3%) on permanent sections. Patients with STAS identified on permanent sections experienced shorter progression-free survival (PFS; P=0.042) and overall survival (OS; P=0.005) compared to those without. STAS was identified in 87 out of 509 patients on FS. Patients with STAS detected on FS also had shorter PFS (P=0.01) and OS (P<0.001) than those without. Compared to permanent sections, STAS detection on FS yielded 66.73% (74/111) sensitivity, 96.7% (385/398) specificity, 85.1% (74/87) positive predictive value, 91.2% (385/422) negative predictive value, and 90.2% (459/509) overall agreement. The kappa coefficient was 0.688 (P < 0.001). ConclusionsOur results from a large series of Chinese patients with stage 1 lung ADC indicated that STAS was associated with poorer survival outcomes on both FS and permanent sections. FS is a highly specific method for assessing STAS in stage 1 lung ADC, but caution is warranted regarding false-positive results.

STAS: New explorations and challenges for thoracic surgeons.

Spread through air spaces (STAS) represents a relatively novel concept in the pathology of lung cancer, and it specifically refers to the dissemination of tumour cells into the parenchymal air spaces adjacent to the primary tumour. In 2015, the World Health Organization (WHO) classified STAS as a new invasive form of lung adenocarcinoma (LUAD). Many studies investigated the role of STAS and revealed its association with the prognosis of LUAD and its influence on the outcomes of other malignant pulmonary neoplasms. Additionally, the underlying mechanisms and predictive models of STAS have received considerable attention in recent years. This paper provides a comprehensive overview of the research advancements and prospects of STAS by examining it from multiple perspectives.

Evaluation of the novel International Association for the Study of Lung Cancer grading system in adenocarcinoma with spread through air space.

The International Association for the Study of Lung Cancer (IASLC) pathology panel has proposed a new grading system for invasive lung adenocarcinoma (LADC). This study aims to validate this novel grading system for invasive LADC using propensity score matching (PSM), with a specific focus on patients exhibiting spread through air space (STAS). We retrospectively analyzed the clinicopathologic features of a large cohort of 910 non-mucinous LADCs with STAS from 2017 to 2020 and classified them according to the novel grading system. We applied PSM to adjust for potential confounders between the grading groups. Kaplan-Meier and Cox proportional hazards models were adopted for prognostic evaluation. The results showed that the IASLC grading system (grades 2 and 3) stratified well in terms of recurrence-free survival (RFS) and overall survival (OS) (P=0.02 and P=0.02, respectively) after matching, with Grade 3 being an independent predictor of RFS [hazard ratio (HR), 1.533; P=0.02] and OS (HR, 2.765; P=0.02) in multivariable models. The concordance index (C-index) and area under the curve (AUC) of the IASLC system were 0.719 and 0.754 for recurrence and 0.844 and 0.891 for death, respectively. In addition, anaplastic lymphoma kinase (ALK) fusion and tumor protein p53 (TP53) mutations were detected more frequently in grade 3 tumors, while epidermal growth factor receptor (EGFR) mutations were more prevalent in grade 2 tumors. The IASLC grade did not predict the benefit of adjuvant chemotherapy (ACT). This study suggests that the new IASLC grading system is a valuable prognostic tool for patients with STAS-positive LADC.

Prognostic Impact and Clinical Features of Spread through Air Spaces in Operated Lung Cancer: Real-World Analysis.

Background and Objectives: Lung cancer is the leading cause of cancer-related deaths. Spread through air spaces (STAS) is an adverse prognostic factor that has become increasingly known in recent years. This study aims to investigate the impact of STAS presence on overall survival (OS) and disease-free survival (DFS) in patients with surgically resected stage IA-IIIA lung cancer and to identify clinicopathological features associated with STAS. Materials and Methods: This research involved 311 lung cancer surgery patients. The relationship between the presence of STAS in the patients' surgical pathology and OS and DFS values was examined. Clinicopathological features associated with the presence of STAS were determined. Results: There were 103 (33%) STAS-positive patients. Adenocarcinoma histological subtype, perineural invasion (PNI), and lymphovascular invasion (LVI) were significantly correlated with being STAS positive. STAS significantly predicted DFS and OS. One-year and five-year DFS rates were significantly lower in the STAS-positive group compared to the STAS-negative group (65% vs. 88%, 29% vs. 62%, respectively, p ≤ 0.001). Similarly, one-year and five-year OS rates were significantly lower in the STAS-positive group compared to the STAS-negative group (92% vs. 94%, 54% vs. 88%, respectively, p ≤ 0.001). In multivariate analysis, STAS was found to be an independent prognostic factor for both DFS and OS (HR: 3.2 (95%CI: 2.1-4.8) and 3.1 (95%CI: 1.7-5.5), p < 0.001 and <0.001, respectively). Conclusions: In our study, STAS was found to be an independent prognostic biomarker in operated stage IA-IIIA lung cancer patients. It may be a beneficial pathological biomarker in predicting the survival of patients and managing their treatments.

Tumor spread through air spaces in lung cancer: prospective analysis of the accuracy of intraoperative frozen section examination.

To establish the accuracy of frozen section examination in identifying tumor spread through air spaces (STAS), as well as to propose a reproducible technical methodology for frozen section analysis. We also aim to propose a method to be incorporated into the decision making about the need for conversion to lobectomy during sublobar resection. This was a nonrandomized prospective study of 38 patients with lung cancer who underwent surgical resection. The findings regarding STAS in the frozen section were compared with the definitive histopathological study of paraffin-embedded sections. We calculated a confusion matrix to obtain the positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity and accuracy. The intraoperative frozen section analysis identified 7 STAS-positive cases that were also positive in the histopathological examination, as well as 3 STAS-negative cases that were positive in the in the histopathological examination. Therefore, frozen section analysis was determined to have a sensitivity of 70%, specificity of 100%, PPV of 100%, NPV of 90.3%, and accuracy of 92% for identifying STAS. Frozen section analysis is capable of identifying STAS during resection in patients with lung cancer. The PPV, NPV, sensitivity, and specificity showed that the technique proposed could be incorporated at other centers and would allow advances directly linked to prognosis. In addition, given the high accuracy of the technique, it could inform intraoperative decisions regarding sublobar versus lobar resection.

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72–0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.

Spread Through Air Spaces: Interresponder Agreement and Comparison Between Pulmonary and General Pathologists

Spread through air spaces (STAS), an important prognostic indicator included in the 2015 World Health Organization classification, is defined as micropapillary, solid, and/or single tumor cell clusters beyond the edge of the main mass and distinct from processing artifacts. This study aimed to assess the interresponder agreement on current STAS criteria vs artifacts, identify discrepancies, and compare responses between pulmonary and general pathologists. A multiple-choice online questionnaire illustrating multiple criteria for STAS vs artifacts was available internationally for 6 days to Pulmonary Pathology Society members, thoracic pathology course attendees, and International Association for the Study of Lung Cancer pathology committee members. Additional 4 questions gathered demographic and practice setting information. One hundred thirty-six unique responses were analyzed. The majority were from North America and Europe (42.6% and 30.2%), practicing pulmonary pathology (70.6%) in academia (64.7%), and with >20 years of experience (31.6%). Excluding trainees, the greatest overall agreement was in defining solid and micropapillary tumor clusters of STAS located ≥3 alveolar spaces from the main tumor edge (91.5%) and recognizing strips of ciliated cells as artifacts (97.7%). Lesser agreement on STAS was evident when tumor cell clusters were immediately adjacent to the tumor edge, a single tumor cell cluster was present at the tissue edge, tumor cell clusters were jagged edged, or tumor cell clusters were admixed with ciliated cell strips (artifacts). There was no significant difference in agreements on STAS for multiple criteria between pulmonary and general pathologists. Significant interresponder agreement on STAS vs artifacts was achieved only for a few criteria. To improve the reproducibility of STAS vs artifacts, areas of lesser agreement require further clarification.

Improving prediction accuracy of spread through air spaces in clinical-stage T1N0 lung adenocarcinoma using computed tomography imaging models

ObjectivesTo develop computed tomography (CT)-based models to increase the prediction accuracy of spread through air spaces (STAS) in clinical-stage T1N0 lung adenocarcinoma. MethodsThree cohorts of patients with stage T1N0 lung adenocarcinoma (n = 1258) were analyzed retrospectively. Two models using radiomics and deep neural networks (DNNs) were established to predict the lung adenocarcinoma STAS status. For the radiomic models, features were extracted using PyRadiomics, and 10 features with nonzero coefficients were selected using least absolute shrinkage and selection operator regression to construct the models. For the DNN models, a 2-stage (supervised contrastive learning and fine-tuning) deep-learning model, MultiCL, was constructed using CT images and the STAS status as training data. The area under the curve (AUC) was used to verify the predictive ability of both model types for the STAS status. ResultsAmong the radiomic models, the linear discriminant analysis model exhibited the best performance, with AUC values of 0.8944 (95% confidence interval [CI], 0.8241-0.9502) and 0.7796 (95% CI, 0.7089-0.8448) for predicting the STAS status on the test and external validation cohorts, respectively. Among the DNN models, MultiCL exhibited the best performance, with AUC values of 0.8434 (95% CI, 0.7580-0.9154) for the test cohort and 0.7686 (95% CI, 0.6991-0.8316) for the external validation cohort. ConclusionsCT-based imaging models (radiomics and DNNs) can accurately identify the STAS status of clinical-stage T1N0 lung adenocarcinoma, potentially guiding surgical decision making and improving patient outcomes.

Established the prediction model of early-stage non-small cell lung cancer spread through air spaces (STAS) by radiomics and genomics features.

This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features. We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3). The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC]=0.85; AUC=0.70; AUC=0.85). The prediction model based on radiomics and genomics features has a good prediction performance for STAS.

Clinical Importance of Grading Tumor Spread through Air Spaces in Early-Stage Small-Lung Adenocarcinoma.

This study aimed to identify the clinical manifestation and implications according to the grading of tumor spread through air spaces in early-stage small (≤2 cm) pathological stage I non-mucinous lung adenocarcinomas. Medical records of patients with pathological stage I tumors sized ≤2 cm were retrospectively reviewed and analyzed. The furthest distance of the spread through air spaces from the tumor margin was measured on a standard-length scale (mm). Enrolled patients were categorized into spread through air spaces (STAS) (-) and STAS (+), and STAS (+) was subdivided according to its furthest distance as follows: STAS (+)-L (<2 mm) and STAS (+)-H (≥2 mm). Risk factors for STAS (+) included papillary predominant subtype (p = 0.027), presence of micropapillary patterns (p < 0.001), and EGFR (p = 0.039). The overall survival of the three groups did not differ significantly (p = 0.565). The recurrence-free survival of STAS (+)-H groups was significantly lower than those of STAS (-) and STAS (+)-L (p < 0.001 and p = 0.039, respectively). A number of alveolar spaces were definite risk factors for STAS (+)-H groups (p < 0.001), and male gender could be one (p = 0.054). In the patient group with small (≤2 cm) pathological stage I lung adenocarcinomas, the presence of STAS ≥ 2 mm was related to significantly lower recurrence-free survival. For identifying definite risk factors for the presence of farther STAS, more precise analysis from a larger study population should be undertaken.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

Predicting spread through air space and immune status in lung adenocarcinoma by CT-based deep learning model.

e20043 Background: Tumor spread through air space (STAS) is a novel histologic pattern of invasion related to high recurrence and poor prognosis. Meanwhile, there is a potential relationship between STAS status and the tumor immune microenvironment. The aim of this study was to predict STAS status in lung adenocarcinoma (LUAD) using a CT-based 3D-CNN deep learning model and explore the tumor immune microenvironment in the GSE58661 cohort. Methods: We retrospectively collected data from 368 patients with lung adenocarcinoma and STAS status from Guangdong Province General Hospital between 2021 and 2023. We performed propensity score matching (PSM) based on gender, year, BMI, and smoking status to obtain a cohort of 368 patients without STAS status. A 3D-Resnet-Resnetearning model was developed to identify the STAS status of lung adenocarcinoma, and the performance of the models was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Meanwhile, bule RNA sequencing and baseline CT of LUAD from 41 patients in the GSE58661 cohort and the TCIA dataset were used to investigate the potential biological mechanism in the patients with STAS status. Results: We included 584 and 152 patients in the training and testing sets, respectively. The 3D-Resnet algorithm was used in the 3D patch with voxel sizes of 128*128*128, 96*96*96, and 64*64*64 to construct the DL-STAS model. With the training set's AUC of 0.899, the results showed that the model achieved an AUC, sensitivity, specificity, and accuracy of 0.884, 0.831, 0.838, and 0.835 in the testing cohort, respectively. According to our model, we divided the patients in GSE58661 into STAS and non-STAS. By identifying and analyzing the differentially expressed genes between the STAS and non-STAS groups, we found that several immune-related pathways, such as the T cell receptor signaling pathway, the PD-1/PD-L1 expression pathway, the natural killer cell-mediated cytotoxicity pathway, and the macrophage activation pathway, were enriched in the group with STAS status. Conclusions: The DL-STAS model we developed has great potential for accurately predicting STAS status in lung adenocarcinoma. In the meanwhile, the prognosis of patients with STAS status may be impacted by their particular tumor immune microenvironment.

Clinicopathologic and molecular landscape of invasive mucinous adenocarcinoma of the lung.

8037 Background: Invasive mucinous adenocarcinoma (IMA), accounting for 2–10% of lung adenocarcinomas, often mimics pneumonia with multicentric opacities in multiple lobes and both lungs. Notable for its microscopic skip lesions leading to tumor spread through air spaces (STAS), IMA is still under-researched in terms of its comprehensive clinical and genetic characteristics in large, multicenter studies. Methods: Primary lung cancer cases were obtained from the Tempus database, categorized into either IMA or non-IMA, and compared by patient, clinical, biopsy, and molecular characteristics. The normalization of RNA-seq data involved computing transcripts per million (TPM), performing log2 transformations, and adjusting for assay and batch effects. Significantly up- and down-regulated genes were defined as false discovery rate q-values &lt; 0.05 and |log2(fold change)| &gt; 0.5. Results: The study analyzed 18,857 cases, with 655 (3.5%) in the IMA group and 18,202 (96.5%) in the non-IMA group. The IMA group predominantly consisted of older individuals (median age 70 vs. 68; p&lt;0.001), had fewer smokers (63% vs. 73%; p&lt;0.001), and included more individuals of Hispanic or Latino ethnicity (4.3% vs. 2.7%; p=0.007). This group was more likely to be diagnosed with early-stage cancer and less likely to have stage 4 cancer at diagnosis (stage 1: 23% vs. 14%, stage 2: 18% vs. 7.3%, stage 3: 23% vs. 17%, stage 4: 36% vs. 62%; p&lt;0.001). They exhibited significantly lower tumor mutational and neoantigen burdens (median 3.1 vs. 4.6 mutations/Mb and 6 vs. 9 neoantigens/Mb, respectively; both p&lt;0.001), fewer positive PD-L1 statuses (27% vs. 58%; p&lt;0.001), and different immune cell infiltration patterns (Table). Gene expression analysis revealed 416 upregulated and 258 downregulated genes in the IMA group compared to the non-IMA group. Conclusions: This analysis underscores the unique clinicopathologic and molecular characteristics of IMAs. An improved understanding of IMA’s biological behavior could lead to the development of more effective, personalized treatment strategies. [Table: see text]

Histology of Bronchiolar Tumor Spread Through Air Spaces.

The clinical significance of lung tumor spread through air spaces (STAS) has been extensively studied, and is recognized as a unique pattern of invasion. Previous studies of STAS have focused primarily on STAS in alveolar spaces, whereas STAS in the bronchiolar spaces (bronchiolar STAS) has been described in only a few case reports only. Here, we examined 306 cases of primary lung adenocarcinoma and found that bronchiolar STAS was present in 18%. Bronchiolar STAS was associated with an inferior prognosis, more advanced stage, and higher histologic grade. No significant difference in clinicopathological factors or prognosis was observed between cases with bronchiolar STAS and those with alveolar STAS alone. Notably, bronchiolar STAS often occurred simultaneously with alveolar STAS and endobronchial spread of adenocarcinoma, particularly when bronchiolar STAS was present outside the main tumor. We also identified cases where bronchiolar STAS and endobronchial spread of adenocarcinoma occurred simultaneously in the same bronchi or bronchioles located outside the main tumor, as well as cases with bronchiolar STAS adjacent to intrapulmonary metastatic nodules. Our results highlight the significant role of bronchiolar STAS in the aerogenous spread of adenocarcinoma cells. Bronchiolar STAS can be regarded as a histologic variant of alveolar STAS. This study also supports the idea that STAS is not a tissue processing artifact, but a true biological process with clinical implications, offering histologic evidence of aerogenous spread in lung adenocarcinoma.

Mean computed tomography value to predict spread through air spaces in clinical N0 lung adenocarcinoma

BackgroundThe aim of this study was to assess the ability of radiologic factors such as mean computed tomography (mCT) value, consolidation/tumor ratio (C/T ratio), solid tumor size, and the maximum standardized uptake (SUVmax) value by F-18 fluorodeoxyglucose positron emission tomography to predict the presence of spread through air spaces (STAS) of lung adenocarcinoma.MethodsA retrospective study was conducted on 118 patients those diagnosed with clinically without lymph node metastasis and having a pathological diagnosis of adenocarcinoma after undergoing surgery. Receiver operating characteristics (ROC) analysis was used to assess the ability to use mCT value, C/T ratio, tumor size, and SUVmax value to predict STAS. Univariate and multiple logistic regression analyses were performed to determine the independent variables for the prediction of STAS.ResultsForty-one lesions (34.7%) were positive for STAS and 77 lesions were negative for STAS. The STAS positive group was strongly associated with a high mCT value, high C/T ratio, large solid tumor size, large tumor size and high SUVmax value. The mCT values were − 324.9 ± 19.3 HU for STAS negative group and − 173.0 ± 26.3 HU for STAS positive group (p < 0.0001). The ROC area under the curve of the mCT value was the highest (0.738), followed by SUVmax value (0.720), C/T ratio (0.665), solid tumor size (0.649). Multiple logistic regression analyses using the preoperatively determined variables revealed that mCT value (p = 0.015) was independent predictive factors of predicting STAS. The maximum sensitivity and specificity were obtained at a cutoff value of − 251.8 HU.ConclusionsThe evaluation of mCT value has a possibility to predict STAS and may potentially contribute to the selection of suitable treatment strategies.

Preoperative nomogram for predicting spread through air spaces in clinical-stage IA non-small cell lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography

PurposeThis study aims to assess the predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiological features and the maximum standardized uptake value (SUVmax) in determining the presence of spread through air spaces (STAS) in clinical-stage IA non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was conducted on 180 cases of NSCLC with postoperative pathological assessment of STAS status, spanning from September 2019 to September 2023. Of these, 116 cases from hospital one comprised the training set, while 64 cases from hospital two formed the testing set. The clinical information, tumor SUVmax, and 13 related CT features were analyzed. Subgroup analysis was carried out based on tumor density type. In the training set, univariable and multivariable logistic regression analyses were employed to identify the most significant variables. A multivariable logistic regression model was constructed and the corresponding nomogram was developed to predict STAS in NSCLC, and its diagnostic efficacy was evaluated in the testing set.ResultsSUVmax, consolidation-to-tumor ratio (CTR), and lobulation sign emerged as the best combination of variables for predicting STAS in NSCLC. Among these, SUVmax and CTR were identified as independent predictors for STAS prediction. The constructed prediction model demonstrated area under the curve (AUC) values of 0.796 and 0.821 in the training and testing sets, respectively. Subgroup analysis revealed a 2.69 times higher STAS-positive rate in solid nodules compared to part-solid nodules. SUVmax was an independent predictor for predicting STAS in solid nodular NSCLC, while CTR and an emphysema background were independent predictors for STAS in part-solid nodular NSCLC.ConclusionOur nomogram based on preoperative 18F-FDG PET/CT radiological features and SUVmax effectively predicts STAS status in clinical-stage IA NSCLC. Furthermore, our study highlights that metabolic parameters and CT variables associated with STAS differ between solid and part-solid nodular NSCLC.

Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma

BackgroundLung adenocarcinoma, a leading cause of cancer-related mortality, demands precise prognostic indicators for effective management. The presence of spread through air space (STAS) indicates adverse tumor behavior. However, comparative differences between 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography(PET)/computed tomography(CT) and CT in predicting STAS in lung adenocarcinoma remain inadequately explored. This retrospective study analyzes preoperative CT and 18F-FDG PET/CT features to predict STAS, aiming to identify key predictive factors and enhance clinical decision-making.MethodsBetween February 2022 and April 2023, 100 patients (108 lesions) who underwent surgery for clinical lung adenocarcinoma were enrolled. All these patients underwent 18F-FDG PET/CT, thin-section chest CT scan, and pathological biopsy. Univariate and multivariate logistic regression was used to analyze CT and 18F-FDG PET/CT image characteristics. Receiver operating characteristic curve analysis was performed to identify a cut-off value.ResultsSixty lesions were positive for STAS, and 48 lesions were negative for STAS. The STAS-positive was frequently observed in acinar predominant. However, STAS-negative was frequently observed in minimally invasive adenocarcinoma. Univariable analysis results revealed that CT features (including nodule type, maximum tumor diameter, maximum solid component diameter, consolidation tumor ratio, pleural indentation, lobulation, spiculation) and all 18F-FDG PET/CT characteristics were statistically significant difference in STAS-positive and STAS-negative lesions. And multivariate logistic regression results showed that the maximum tumor diameter and SUVmax were the independent influencing factors of CT and 18F-FDG PET/CT in STAS, respectively. The area under the curve of maximum tumor diameter and SUVmax was 0.68 vs. 0.82. The cut-off value for maximum tumor diameter and SUVmax was 2.35 vs. 5.05 with a sensitivity of 50.0% vs. 68.3% and specificity of 81.2% vs. 87.5%, which showed that SUVmax was superior to the maximum tumor diameter.ConclusionThe radiological features of SUVmax is the best model for predicting STAS in lung adenocarcinoma. These radiological features could predict STAS with excellent specificity but inferior sensitivity.