Computed tomography features of resected lung adenocarcinomas with spread through air spaces

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.

CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma

Objectives

This study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models' performance.

Methods

This clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation.

Results

For tumors ≤ 3 cm, ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103-0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213-2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000-1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023-1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756-0.877) in the training set and 0.789 (95% CI: 0.689-0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731-0.891) in the training set and 0.833 (95% CI: 0.733-0.932) in the test set.

Conclusion

For tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study

OBJECTIVES

To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma.

METHODS

A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models.

RESULTS

Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model.

CONCLUSIONS

DLCT has added value for STAS prediction in lung adenocarcinoma.

CLINICAL RELEVANCE STATEMENT

Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future.

KEY POINTS

• Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Significance of Spread Through Air Spaces and Vascular Invasion in Early-stage Adenocarcinoma Survival: A Comprehensive Clinicopathologic Study of 427 Patients for Precision Management

Spread through air spaces (STAS) is a novel invasive pattern of lung cancer associated with poor prognosis in non-small cell cancer (NSCLC). We aimed to investigate the incidence of STAS in a surgical series of adenocarcinomas (ADCs) resected in our thoracic surgery unit and to identify the association of STAS with other clinicopathological characteristics. We retrospectively enrolled patients with stage cT1a-cT2b who underwent resection between 2016 and 2022. For each case, a comprehensive pathologic report was accessible which included histotype, mitoses, pleural invasion, fibrosis, tumor infiltrating lymphocytes, necrosis, inflammation, vascular and perineural invasion, as well as STAS. PD-L1 expression was also investigated. A total of 427 patients with ADCs underwent surgery. Regarding overall survival (OS), no significant difference was observed between the STAS positive (STAS+) and STAS negative (STAS-) groups ( P =0.44). However, vascular invasion (VI) was associated with a poorer survival probability ( P =0.018). STAS+/VI+ patients had tendentially worse survival compared with STAS+/VI- ( P =0.089). ADCs with pathologic evidence of immune system (IS) activation (TILs>10% and PD-L1≥1) demonstrated significantly increased OS compared with ADCs with no IS and VI. In terms of recurrence rate, no statistical differences were found between the STAS+ and STAS- samples ( P =0.2). VI was also linked to a significantly elevated risk of recurrence ( P =0.0048). Our study suggests that in resected early-stage ADCs, STAS+ does not seem to influence recurrence or mortality. VI was instead an adverse pathologic prognostic factor for both survival and recurrence, whereas IS seemed to be protective.

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

BACKGROUND

The 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.

METHODS

A 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.

RESULTS

Forty-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.

CONCLUSIONS

The 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

PURPOSE

This 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).

METHODS

A 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.

RESULTS

SUVmax, 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.

CONCLUSION

Our 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

BACKGROUND

Lung 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.

METHODS

Between 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.

RESULTS

Sixty 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.

CONCLUSION

The 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.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma

BACKGROUND

Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This study aimed to develop a STAS deep-learning (STAS-DL) prediction model in lung adenocarcinoma with tumor smaller than 3 cm and a consolidation-to-tumor (C/T) ratio less than 0.5.

METHODS

The study retrospectively enrolled of 581 patients from two institutions between 2015 and 2019. The STAS-DL model was developed to extract the feature of solid components through solid components gated (SCG) for predicting STAS. The STAS-DL model was assessed with external validation in the testing sets and compared with the deep-learning model without SCG (STAS-DLwoSCG), the radiomics-based model, the C/T ratio, and five thoracic surgeons. The performance of the models was evaluated using area under the curve (AUC), accuracy and standardized net benefit of the decision curve analysis.

RESULTS

The study evaluated 458 patients (institute 1) in the training set and 123 patients (institute 2) in the testing set. The proposed STAS-DL yielded the best performance compared with the other methods in the testing set, with an AUC of 0.82 and an accuracy of 74%, outperformed the STAS-DLwoSCG with an accuracy of 70%, and was superior to the physicians with an AUC of 0.68. Moreover, STAS-DL achieved the highest standardized net benefit compared with the other methods.

CONCLUSION

The proposed STAS-DL model has great potential for the preoperative prediction of STAS and may support decision-making for surgical planning in early-stage, ground glass-predominant lung adenocarcinoma.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas

PURPOSE

To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets.

MATERIALS AND METHODS

This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance.

CONCLUSION

A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma

OBJECTIVES

Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC).

MATERIALS AND METHODS

A total of 466 stage I LAC patients who underwent 2-[18F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort (n = 232, 20.3% STAS-positive), a validation cohort (n = 122, 27.0% STAS-positive), and a test cohort (n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUVmax were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC.

RESULTS

The differences in age (P = 0.009), lesion density subtype (P < 0.001), spiculation sign (P < 0.001), bronchus truncation sign (P = 0.001), and SUVmax (P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [OR(95%CI):3.310(1.150-9.530), P = 0.027], lesion density subtype (P = 0.004) and SUVmax ≥ 2.5 g/ml [OR(95%CI):3.268(1.021-1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUVmax) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively.

CONCLUSIONS

STAS was more likely to occur in older patients, in solid lesions and higher SUVmax in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing.

The correlation between tumor radiological features and spread through air spaces in peripheral stage IA lung adenocarcinoma: a propensity score-matched analysis

BACKGROUND

The consolidation tumor ratio (CTR) is a predictor of invasiveness in peripheral T1N0M0 lung adenocarcinoma. However, its association with spread through air spaces (STAS) remains largely unexplored. We aimed to explore the correlation between the CTR of primary tumors and STAS in peripheral T1N0M0 lung adenocarcinoma.

METHODS

We collected data from patients who underwent surgery for malignant lung neoplasms between January and November 2022. Univariate and multivariate analyses following propensity-score matching with sex, age, BMI, were performed to identify the independent risk factors for STAS. The incidence of STAS was compared based on pulmonary nodule type. A smooth fitting curve between CTR and STAS was produced by the generalized additive model (GAM) and a multiple regression model was established using CTR and STAS to determine the dose-response relationship and calculate the odds ratio (OR) and 95% confidence interval (CI).

RESULTS

17 (14.5%) were diagnosed with STAS. The univariate analysis demonstrated that the history of the diabetes, size of solid components, spiculation, pleural indentation, pulmonary nodule type, consolidation/tumor ratio of the primary tumor were statistically significant between the STAS-positive and STAS-negative groups following propensity-score matching(p = 0.047, 0.049, 0.030, 0.006, 0.026, and < 0.001, respectively), and multivariate analysis showed that the pleural indentation was independent risk factors for STAS (with p-value and 95% CI of 0.043, (8.543-68.222)). Moreover, the incidence of STAS in the partially solid nodule was significantly different from that in the solid nodule and ground-glass nodule (Pearson Chi-Square = 7.49, p = 0.024). Finally, the smooth fitting curve showed that CTR tended to be linearly associated with STAS by GAM, and the multivariate regression model based on CTR showed an OR value of 1.24 and a p-value of 0.015.

CONCLUSIONS

In peripheral stage IA lung adenocarcinoma, the risk of STAS was increased with the solid component of the primary tumor. The pleural indentation of the primary tumor could be used as a predictor in evaluating the risk of the STAS.

Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study

BACKGROUND

Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer.

METHOD

Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features.

RESULTS

The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps.

CONCLUSION

The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.

Impact of surgery and adjuvant chemotherapy on the survival of stage I lung adenocarcinoma patients with tumor spread through air spaces

OBJECTIVES

Tumor spread through air spaces (STAS) is a unique mechanism of lung cancer metastasis; however, its clinical value for stage I lung adenocarcinoma (ADC) remains unclear at present. We investigated the (1) prognosis of patients after sublobar resection compared with lobectomy for stage I lung adenocarcinoma with STAS; and (2) potential benefits of adjuvant chemotherapy (ACT) for patients with stage I ADC and STAS.

METHODS

A total of 3328 consecutive patients with stage I ADC were retrospectively identified between 2014 and 2018 at our institution; among them, 600 were diagnosed with STAS. Kaplan-Meier analysis and Cox proportional hazard regression models were used to evaluate the impact of STAS on overall survival (OS) and recurrence-free survival (RFS).

RESULTS

Among stage IA patients with STAS, there was no significant difference between those who underwent sublobar resection and lobectomy in OS (P = 0.919) and RFS (P = 0.066). Multivariate analysis confirmed this result (sublobar resection versus lobectomy, OS: HR = 0.523, 95 % CI, 0.056-18.458, P = 0.714; RFS, HR = 0.360, 95 % CI, 0.115-1.565, P = 0.897). ACT did not improve the prognosis of stage IA patients but did improve the RFS of stage IB patients with high-risk recurrence factors, including poorly differentiated tumors, lymphovascular invasion and visceral pleural invasion (P = 0.046).

CONCLUSIONS

Sublobar and lobectomy resection provided a comparable prognosis for stage IA ADC patients with STAS. When STAS was confirmed postoperatively, ACT should be considered for patients with stage IB with high-risk recurrence factors but not for those with stage IA disease.

Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma: A Meta-analysis

To compare computed tomography (CT)-based radiologic features in patients, who are diagnosed with lung adenocarcinoma with the pathologically detected spread of tumor cells through air spaces (STAS positive [STAS+]) and those with no STAS. PubMed, Embase, and Scopus databases were systematically searched for observational studies (either retrospective or prospective) of patients with lung adenocarcinoma that had compared CT-based features between STAS+ and STAS-negative cases (STAS-). The pooled effect sizes were reported as odds ratio (OR) and weighted mean difference (WMD). STATA software was used for statistical analysis. The meta-analysis included 10 studies. Compared with STAS-, STAS+ adenocarcinoma was associated with increased odds of solid nodule (OR: 3.30, 95% CI: 2.52, 4.31), spiculation (OR: 2.05, 95% CI: 1.36, 3.08), presence of cavitation (OR: 1.49, 95% CI: 1.00, 2.22), presence of clear boundary (OR: 3.01, 95% CI: 1.70, 5.32), lobulation (OR: 1.65, 95% CI: 1.11, 2.47), and pleural indentation (OR: 1.98, 95% CI: 1.41, 2.77). STAS+ tumors had significant association with the presence of pulmonary vessel convergence (OR: 2.15, 95% CI: 1.61, 2.87), mediastinal lymphadenopathy (OR: 2.06, 95% CI: 1.20, 3.56), and pleural thickening (OR: 2.58, 95% CI: 1.73, 3.84). The mean nodule diameter (mm) (WMD: 6.19, 95% CI: 3.71, 8.66) and the mean solid component (%) (WMD: 24.5, 95% CI: 10.5, 38.6) were higher in STAS+ tumors, compared with STAS- ones. The findings suggest a significant association of certain CT-based features with the presence of STAS in patients with lung adenocarcinoma. These features may be important in influencing the nature of surgical management.

Comprehensive molecular characterizations of stage I-III lung adenocarcinoma with tumor spread through air spaces

Purpose: The aim of this study is to investigate integrative genomic spectra of stage I-III lung adenocarcinoma with tumor spread through air spaces (STAS). Methods: We retrospectively identified 442 surgically resected lung adenocarcinoma patients of pathological stage I-III in Shanghai Chest Hospital from January 2018 to February 2021. Surgically resected tissues were used for next-generation sequencing (NGS) with a panel of 68 lung cancer-related genes to profile comprehensive molecular characterizations. Results: A total of 442 cases were analyzed, including 221 (50%) STAS-positive (SP) and 221 (50%) STAS-negative (SN) lung adenocarcinoma patients. In total, 440 cases (99.6%) were positive for the overall mutational spectrum, and the higher mutational genes were EGFR, TP53, KRAS, ALK, SMAD4, and ERBB2 (62%, 42%, 14%, 10%, 7%, and 7%, respectively). Compared with the SN population, there was significantly lower EGFR alteration in the single-nucleotide variant (SNV) mutation spectrum (52.5% vs 69.7%, p < 0.001) and significantly higher TP53 alteration in the SP population (49.8% vs 34.8%, p = 0.002). EGFR L858R missense mutation (19.5% vs 37.6%, p < 0.001) and ERBB2 exon 20 indel mutation (1.8% vs 5.9%, p = 0.045) were more frequent in the SN population. The detection rate of ALK fusion rearrangements in the SP population was significantly higher than that in the SN population (13.1% vs 2.3%, p < 0.001). In the analysis of signaling pathways, no significant difference was discovered between SP and SN patients. No difference in 1-year disease-free survival was observed between SP and SN patients in this study. Conclusion: Significant differences exist in stage I-III lung adenocarcinoma patients with STAS in molecular characterizations.

Segmentectomy Provides Comparable Outcomes to Lobectomy for Stage IA Non-small Cell Lung Cancer with Spread through Air Spaces

This study aimed to compare the recurrence-free survival (RFS) and overall survival (OS) among wedge resection (non-anatomical resection), segmentectomy and lobectomy for pathological stage IA non-small cell lung cancer (NSCLC) with spread through air spaces (STAS). Patients underwent surgical treatment for pathological stage IA NSCLC between January 1, 2005, and March 31, 2016, at our hospital. Surgical procedures were classified as lobectomy, segmentectomy, and wedge resection. Among the 555 analyzed cases, STAS was observed in 148 patients (26.7%). STAS was correlated with worse RFS (P < 0.001) and OS (P < 0.001) and was an independent poor prognostic factor for RFS (hazard ratio: 2.37, P < 0.001) and OS (hazard ratio: 2.02, P < 0.001) in the multivariate analysis. In patients with STAS, the RFS and OS in the segmentectomy group were comparable to those in the lobectomy group. However, the RFS and OS in the wedge resection group were significantly lower than those in the lobectomy group (RFS, P < 0.001; OS, P = 0.001). Wedge resection was an independent prognostic factor for poor RFS (hazard ratio [HR] = 3.87; 95% confidence interval [CI] = 1.84 - 8.12, P < 0.001), and poor OS (hazard ratio [HR] = 3.39; 95% confidence interval [CI] = 1.33 - 8.76, P = 0.011) in the multivariate analysis. Segmentectomy is an adequate operation for patients with stage IA NSCLC with or without STAS. However, wedge resection is associated with a higher risk of recurrence in this patient population.

[Research Progress in the Effect of Consolidation Tumor Ratio on the Diagnosis and Treatment of Early-stage Peripheral Lung Cancer]

Consolidation tumor ratio (CTR) is a hot issue in lung cancer imaging studies in recent years. It is defined as the proportion of the maximum consolidation diameter divided by the maximum tumor diameter in the lung window scanned by high resolution computed tomography (HRCT). Many studies have also confirmed that it can be used as an indicator to identify whether a lung tumor is benign or malignant at the early stage, the main basis on which to decide whether sublobectomy can be performed, and is an independent factor for the recurrence and prognosis of early-stage lung cancer. Especially after tumor size and CTR results of JCOG0804 and JCOG0802 trials in Japan were published, a breakthrough in the treatment method upends the conventional surgical approach, which benefits patients with early-stage lung cancer. But insufficient research data on CTR leads to the fact that an evaluation system to measure CTR is yet to be built. This paper discusses the research progress in CTR prediction of benign or malignancy of pulmonary nodules, how to choose a surgical approach, lymph node dissection, spread through air spaces (STAS) and other hot issues. It also investigates the possible indicators to predict efficacy based on CTR, summarizes and analyzes the development trend of surgical methods to treat early-stage peripheral lung cancer and challenges, to provide new ideas for clinical application.
.

3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer

PURPOSE

The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning.

MATERIALS AND METHODS

A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS.

RESULTS

For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.

Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma

The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.

A guide for managing patients with stage I NSCLC: deciding between lobectomy, segmentectomy, wedge, SBRT and ablation-part 2: systematic review of evidence regarding resection extent in generally healthy patients

Background

Clinical decision-making for patients with stage I lung cancer is complex. It involves multiple options (lobectomy, segmentectomy, wedge, stereotactic body radiotherapy, thermal ablation), weighing multiple outcomes (e.g., short-, intermediate-, long-term) and multiple aspects of each (e.g., magnitude of a difference, the degree of confidence in the evidence, and the applicability to the patient and setting at hand). A structure is needed to summarize the relevant evidence for an individual patient and to identify which outcomes have the greatest impact on the decision-making.

Methods

A PubMed systematic review from 2000-2021 of outcomes after lobectomy, segmentectomy and wedge resection in generally healthy patients is the focus of this paper. Evidence was abstracted from randomized trials and non-randomized comparisons with at least some adjustment for confounders. The analysis involved careful assessment, including characteristics of patients, settings, residual confounding etc. to expose degrees of uncertainty and applicability to individual patients. Evidence is summarized that provides an at-a-glance overall impression as well as the ability to delve into layers of details of the patients, settings and treatments involved.

Results

In healthy patients there is no short-term benefit to sublobar resection vs. lobectomy in randomized and non-randomized comparisons. A detriment in long-term outcomes is demonstrated by adjusted non-randomized comparisons, more marked for wedge than segmentectomy. Quality-of-life data is confounded by the use of video-assisted approaches; evidence suggests the approach has more impact than the resection extent. Differences in pulmonary function tests by resection extent are not clinically meaningful in healthy patients, especially for multi-segmentectomy vs. lobectomy. The margin distance is associated with the risk of recurrence.

Conclusions

A systematic, comprehensive summary of evidence regarding resection extent in healthy patients with attention to aspects of applicability, uncertainty and effect modifiers provides a foundation on which to build a framework for individualized clinical decision-making.

Preliminary Results of NGS Gene Panel Test Using NSCLC Sputum Cytology and Therapeutic Effect Using Corresponding Molecular-Targeted Drugs

As more molecular-targeted drugs for advanced non-small cell lung cancer are brought to market, batch tests for the identification of gene mutations are needed at initial diagnosis. However, since current gene panel tests require a sufficient amount of tissue sample, there are many instances where panel tests cannot be performed. Therefore, we have developed a highly sensitive next generation sequencing (NGS) panel test to facilitate cytological specimens. Herein, we describe three cases positive for epidermal growth factor receptor (EGFR) exon 19 deletion, MET exon 14 skipping, and KRAS G12A using NGS analysis from sputum. In each case, genetic information was consistent with companion diagnostic analysis obtained from tissue samples collected under bronchoscopy. In cases of EGFR and MET mutations, the corresponding tyrosine kinase inhibitors were highly effective. This is the first report to demonstrate that a novel panel test could detect gene mutations in sputum samples in clinical practice and compare the gene allele ratio with the sample directly collected from the lesion.

Spread through air spaces positivity and extent of resection in patients with Stage I non-small cell lung cancer: A contemporary review

The concept of spread through air spaces is a type of cancer spread that is unique to lung and may be established as a criterion for invasion. It is a potential risk factor for recurrence and poor prognosis in patients with early-stage non-small cell lung cancer. This review provides a contemporary overview on recent data in this field and aim to help surgeons to decide the extent of resection according to patients" spread through air spaces status.

Prognostic implication of tumor spread through air spaces in patients with pathologic N0 lung adenocarcinoma

OBJECTIVES

Tumor spread through air spaces (STAS) has been identified as an invasive pattern in lung adenocarcinoma (ADC), but the prognostic implication of STAS has not been well studied in patients with pathologic N0 lung ADC. The purpose of this study was to evaluate the prognostic implication of STAS in pathologic N0 lung ADC patients after radical surgery.

MATERIALS AND METHODS

Between January 2017 and December 2018, 796 patients with completely resected pathologic N0 lung ADC were reviewed. Pearson's chi-square test or Fisher exact test was used for comparing the relationship between STAS and clinicopathological features. The log-rank test and multivariate Cox regression models were used to explore prognostic factors.

RESULTS

Among the 796 patients, STAS was positive in 201 patients (25.3%). The presence of STAS was significantly associated with patients with solid nodules (P < 0.001), micropapillary pattern-predominant adenocarcinoma/solid pattern-predominant adenocarcinoma (P < 0.001), larger tumor size (P < 0.001), visceral pleural invasion (P < 0.001) and lymphovascular invasion (P < 0.001). Multivariable analysis showed that STAS was an independent prognostic factor for recurrence-free survival (RFS) in pathologic N0 lung ADC patients (P = 0.014). For patients with acinar pattern-predominant adenocarcinoma (APA) / papillary pattern-predominant adenocarcinoma (PPA) / invasive mucinous adenocarcinoma (IMA) and patients who underwent lobectomy, STAS was an independent prognostic factor for RFS (P = 0.015, P = 0.011; respectively) and overall survival (OS) (P = 0.038, P = 0.020; respectively).

CONCLUSION

In this study, STAS was an independent prognostic factor for RFS in pathologic N0 lung adenocarcinomas, and it was also an independent prognostic factor for RFS and OS in patients with APA/PPA/IMA and those who received lobectomy.

Radiomic values from high-grade subtypes to predict spread through air spaces in lung adenocarcinoma

BACKGROUND

We aimed to establish a radiomic prediction model for tumor spread through air spaces (STAS) in lung adenocarcinoma using radiomic values from high-grade subtypes (solid and micropapillary).

METHODS

We retrospectively reviewed 327 patients with lung adenocarcinoma from two institutes (Cohort 1: 227 patients; Cohort 2: 100 patients) between March 2017 and March 2019. STAS was identified in 113 (34.6%) patients. A high-grade likelihood prediction model was constructed based on a historical cohort of 82 patients with "near-pure" pathological subtype. The STAS prediction model based on the patch-wise mechanism identified the high-grade likelihood area for each voxel within the internal border of the tumor. STAS presence was indirectly predicted by a volume percentage threshold of the high-grade likelihood area. Performance was evaluated by receiver operating curve analysis with 10-repetition, 3-fold cross-validation in Cohort 1, and was individually tested in Cohort 2.

RESULTS

Overall, 227 patients (STAS-positive: 77 [33.9%]) were enrolled for cross-validation (Cohort 1) while 100 (STAS-positive: 36 [36.0%]) underwent individual testing (Cohort 2). The gray level co-occurrence matrix (variance) and histogram (75^th percentile) features were selected to construct the high-grade likelihood prediction model, which was used as the STAS prediction model. The proposed model achieved good performance in Cohort 1 with an area under the curve, sensitivity, and specificity, of 81.44%, 86.75%, and 62.60%, respectively, and correspondingly, in Cohort 2, they were 83.16%, 83.33%, and 63.90%, respectively.

CONCLUSIONS

The proposed computed tomography-based radiomic prediction model could help guide preoperative prediction of STAS in early-stage lung adenocarcinoma and relevant surgeries.

Clinical implication of tumour spread through air spaces in pathological stage I lung adenocarcinoma treated with lobectomy

OBJECTIVES

The aim of this study was to evaluate the clinical implication of tumour spread through air spaces (STAS) as a prognostic factor in pathological stage I lung adenocarcinoma treated with lobectomy and to identify related parameters.

METHODS

Medical records of patients who underwent pulmonary lobectomy for stage I (American Joint Committee on Cancers eighth edition) lung adenocarcinomas between 2012 and February 2018 at our institutions were reviewed retrospectively. Patients with minimally invasive adenocarcinomas and tumours ≥3 cm in size were excluded. Included patients were classified into STAS (+) and STAS (-) groups. Clinical implications of STAS and recurrence in patients were investigated.

RESULTS

A total of 109 patients was analysed: 41 (37.6%) in the STAS (+) and 68 (62.4%) in the STAS (-) group. STAS was associated with larger consolidation diameter on chest tomography (≥1.5 cm; P = 0.006) or a higher invasive ratio (≥85%; P = 0.012) and presence of a micropapillary pattern in multivariable analysis (P = 0.003) The recurrence-free survival curve showed statistical difference (P = 0.008) with 3-year survival rates of 73.0% (9 patients) and 96.8% (2 patients) in the STAS (+) and STAS (-) group, respectively. However, no statistical significance was observed in the lung cancer-related survival curve (P = 0.648). The presence of STAS was an independent risk factor for recurrence in multivariable analysis (hazard ratio = 5.9, P = 0.031).

CONCLUSIONS

The presence of STAS could be an important risk factor for recurrence in patients with early-stage invasive lung adenocarcinoma treated with pulmonary lobectomy.

Prognostic impact of tumour spread through air space in radiological subsolid and pure solid lung adenocarcinoma

OBJECTIVES

The aim of the study was to investigate the relationship between ground-glass opacity (GGO) and tumour spread through air space (STAS), as well as their joint influence on the prognosis of stage I lung adenocarcinoma.

METHODS

A total of 620 consecutive patients with surgically resected lung adenocarcinoma between January 2011 and December 2012 were reviewed retrospectively. The relevance of STAS and GGO was analysed by logistic regression, and their prognostic significance was investigated via the Kaplan-Meier method and Cox regression models. Furthermore, to determine the magnitude of the prognostic impact of GGO and STAS, we respectively performed survival analysis in subgroups according to the presence of STAS and GGO.

RESULTS

Of all 620 cases, 26.7% were positive for STAS, and 32.9% had a GGO component. STAS was present in 10.8% of part-solid lesions and 34.9% of pure solid nodules (P < 0.001). Lepidic-predominant histologic subtype, GGO component and pathological T stage exhibited significant relevance to the presence of STAS. For the whole population, STAS and GGO component were each revealed as independent predictors for overall survival and recurrence-free survival (each with P < 0.001). Interestingly, in the GGO-present subgroup, STAS failed to significantly stratify prognosis. However, in the STAS-positive subgroup, the presence of a GGO component was independently associated with favourable oncological results.

CONCLUSIONS

STAS is associated with a GGO component, and each factor was a significant predictor for the prognosis of stage I lung adenocarcinoma. Moreover, the favourable prognostic impact of a GGO component was greater than the adverse prognostic influence of STAS, indicating that GGO is a more reliable prognostic predictor in stage I lung adenocarcinoma.

Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer

Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (−) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.

Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning

OBJECTIVES

As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma.

METHODS

We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using 'PyRadiomics' package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally.

RESULTS

A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55-0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69.

CONCLUSIONS

CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.

Current status and perspectives of spread through air spaces in lung cancer

According to the World Health Organization classification of 2015, spread through air spaces (STAS) is a newly recognized pattern of invasion in lung adenocarcinoma. Many researchers have reported that STAS is recognized in all histological subtypes, and there is a strong association between STAS and prognosis in lung cancer. However, there are several technical issues associated with STAS, such as distinction between the actual in vivo phenomenon and an artifact, difficulty in assessing STAS in frozen specimens, and establishing the relationship between morphological and molecular properties of STAS. This review focuses on the current state of knowledge and the outlook of the STAS phenomenon from the perspective of surgeons, pathologists, and radiologists.

Detection of EGFR mutation of pulmonary adenocarcinoma in sputum using droplet digital PCR

Background

It is still unclear whether epidermal growth factor receptor (EGFR) mutation of primary lung adenocarcinoma can be detected on sputum samples. This study aimed to examine EGFR mutations of primary lung adenocarcinoma in sputum samples using droplet digital polymerase chain reaction (ddPCR) and compare it with an EGFR mutation in surgically resected lung cancer.

Methods

Sputum was prospectively collected from the patients before complete resection of the primary lung cancer at Kanagawa Cancer Center from September 2014 to May 2016. ddPCR was performed to detect EGFR exon 21 L858R point mutation (Ex21) and EGFR exon 19 deletion mutation (Ex19) in sputum samples from patients with lung adenocarcinoma. The concordance of EGFR mutation status in sputum samples and tumors in surgically resected specimen was evaluated for each positive and negative cytology group.

Results

One hundred and eighteen patients with primary lung adenocarcinoma provided sputum samples. Sputum cytology was positive in 13 patients (11.0%). ddPCR detected two cases of Ex21 and two cases of Ex19 in sputum cytology positive cases. Compared to surgically resected specimens, the sensitivity, specificity, and positive predictive value of EGFR mutation (Ex19 and Ex21) detection were 80.0%, 100%, and 100%, respectively, in sputum cytology positive cases. In contrast, the sensitivity, specificity, and positive predictive value of EGFR mutation (Ex19 and Ex21) detection were 3.1%, 100%, and 100%, respectively, in sputum cytology negative cases.

Conclusions

EGFR mutations in primary lung adenocarcinoma can be detected with high sensitivity in sputum samples if sputum cytology is positive.

Predictors of CT Morphologic Features to Identify Spread Through Air Spaces Preoperatively in Small-Sized Lung Adenocarcinoma

Objectives

This study aimed to explore the predictive CT features of spread through air spaces (STAS) in patients with small-sized lung adenocarcinoma.

Methods

From January 2017 to May 2019, patients with confirmed pathology of small-sized lung adenocarcinoma (less than or equal to 2 cm) and who underwent surgery were retrospectively analyzed. The clinical, pathological, and surgical information and CT features were analyzed.

Results

A total of 47 patients with STAS (males, 61.7%; mean age, 56 ± 8years) and 143 patients without STAS (males, 58%; mean age, 53 ± 11 years) were included. Pathologically, papillary, micropapillary, solid predominant subtypes, and vascular and pleural invasion were most commonly observed features in the STAS group. Radiologically, higher consolidation tumor ratio (CTR), presence of spiculation, satellites, ground glass ribbon sign, pleural attachment, and unclear tumor–lung interface were more commonly observed features in the STAS group. CTR, presence of ground glass ribbons and pleural connection, and absence of cystic airspaces were considered as stable predictors of STAS in multivariate logistic models. The receiver operating characteristic curve (ROC) analysis for predicting STAS demonstrated higher area under the curve (AUC) in the model that used CTR (0.760, 95% confidence interval, 0.69–0.83) for predicting STAS than in the model that used long diameter of entire lesion (0.640).

Conclusions

CTR is the best CT sign for predicting STAS in small-sized lung adenocarcinoma. The ground glass ribbon is a newly found indicator and has the potential for predicting STAS.

Could tumor spread through air spaces benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-institutional study

Background:

The benefit of adjuvant chemotherapy (ACT) remains unknown for patients with stage I lung adenocarcinoma (ADC) with spread through air spaces (STAS). This study investigated the effect of adjuvant chemotherapy in stage I ADC/STAS-positive patients.

Methods:

A total of 3346 patients with stage I ADC from five institutions in China were identified from 2009 to 2013, of whom 1082 were diagnosed with STAS (32.3%). By using the Kaplan–Meier method and Cox proportional hazard regression model, we explored the impact of STAS on prognosis, and determined if the use of adjuvant chemotherapy was associated with improved outcomes in patients with stage I ADC/STAS-positive. A validation cohort was also included in this study.

Results:

Patients with stage I ADC/STAS-positive in the primary cohort had unfavorable overall survival (OS) and disease-free survival (DFS). A multivariate Cox regression model confirmed the survival disadvantages of STAS in patients with stage I ADC [OS: hazards ratio (HR) = 1.877, 95% confidence interval (CI): 1.579–2.231; p < 0.001; DFS: HR = 1.895, 95% CI: 1.614–2.225; p < 0.001]. Lobectomy was associated with better OS and DFS than sublobar resection (SR) in both stage IA and IB ADC/STAS-positive. Similar results were observed in the validation cohort. For patients with stage IB ADC/STAS-positive, ACT was revealed as an independent factor for favorable survival (OS: HR = 0.604, 95% CI: 0.397–0.919; p = 0.018; DFS: HR = 0.565, 95% CI: 0.372–0.858; p = 0.007). However, among patients with stage IA ADC/STAS-positive, ACT was associated with improved outcomes only for those undergoing SR (OS: HR = 0.787, 95% CI: 0.359–0.949; p = 0.034; DFS: HR = 0.703, 95% CI: 0.330–0.904; p = 0.029).

Conclusion:

The presence of STAS was correlated with poor prognosis in patients with stage I ADC. Our study suggested that ACT might be considered for patients with stage IB ADC/STAS-positive and those with stage IA ADC/STAS-positive who underwent SR.

Prognostic significance of tumor spread through air spaces in patients with stage IA part-solid lung adenocarcinoma after sublobar resection

OBJECTIVES

The purpose of this study was to assess the clinicopathologic implications of tumor spread through air spaces (STAS) in patients with stage IA part-solid lung adenocarcinoma after sublobar resection.

MATERIALS AND METHODS

Medical records of patients with stage IA part-solid adenocarcinoma who underwent curative pulmonary resection between February 2009 and December 2016 were retrospectively reviewed. The clinicopathological features of STAS and its influence on postoperative recurrence and survival were investigated.

RESULTS

Among the 115 patients with stage IA part-solid adenocarcinoma who underwent wedge resection, 20 (17.4 %) had STAS. The multivariable analysis showed presence of STAS [HR (hazard ratio), 9.447; p = 0.002) and a larger invasive component size (HR, 1.097; p = 0.034) were independent risk factors for recurrence. The 5-year freedom from recurrence rates were 62.4 % and 97.9 % in cases with and without STAS, respectively (p < 0.001), and the 5-year disease-free survival rates were 58.5 % and 97.9 % in cases with and without STAS, respectively (p < 0.001). The presence of STAS was associated with old age (p = 0.030), male gender (p = 0.023), acinar predominant histologic pattern (p = 0.004), presence of micropapillary pattern (p < 0.001), lymphovascular invasion (p < 0.001) and larger invasive component (p < 0.001).

CONCLUSION

STAS could be an important prognostic factor in patients with stage IA part-solid lung adenocarcinoma after sublobar resection. Effective preoperative evaluation and postoperative surveillance may help improve the outcome of patients with small part-solid nodules, particularly when accompanied by STAS.

Radiologic Features of Resected Lung Adenocarcinoma With Epithelial-Mesenchymal Transition

BACKGROUND

Epithelial-mesenchymal transition plays a crucial role in cancer progression and is a significant prognosticator for postoperative survival in patients with lung cancer. Predicting epithelial-mesenchymal transition preoperatively using computed tomography may help to determine the optimal surgical strategy.

METHODS

We performed an immunohistochemical analysis of E-cadherin and vimentin expressions using tumor specimens from resected primary lung adenocarcinoma and classified the results into 3 subgroups according to the expressions: epithelial, intermediate, and mesenchymal. The intermediate and mesenchymal groups were classified as the epithelial-mesenchymal transition conversion group. We analyzed the association between epithelial-mesenchymal transition and radiologic characteristics, especially computed tomographic features.

RESULTS

The epithelial-mesenchymal transition conversion group comprised 162 patients (49.1%). Computed tomography revealed that tumors with epithelial-mesenchymal transition conversion showed a high consolidation/tumor ratio compared with those without conversion. Univariate analysis demonstrated that tumors with epithelial-mesenchymal transition were significantly associated with bronchial and/or vascular convergence (P < .001) and notching (P = .028). When the cutoff value for the consolidation/tumor ratio was set by the receiver operating characteristic curve, independent predictive factors for epithelial-mesenchymal transition by multivariate analysis were high ratio (>0.7946; P < .001) and the presence of convergence (P = .05). Tumors with a high consolidation/tumor ratio and convergence had a 4-fold higher odds ratio for epithelial-mesenchymal transition, and patients had significantly poorer survival.

CONCLUSIONS

Convergence and a high consolidation/tumor ratio were independently associated with epithelial-mesenchymal transition conversion. These preoperative radiologic results will help to predict epithelial-mesenchymal transition conversion in lung adenocarcinoma.

Approach to the Subsolid Nodule

Most focal persistent ground glass nodules (GGNs) do not progress over 10 years. Research suggests that GGNs that do not progress, those that do, and solid lung cancers are fundamentally different diseases, although histologically they seem similar. Surveillance of GGNs to identify those that gradually progress is safe and does not risk losing a window. GGNs with 5 mm solid component or less than 10 mm consolidation (mediastinal and lung windows, respectively, on thin slice CT) are highly curable with resection. The optimal type of resection is unclear; sublobar resection is reasonable but an adequate margin is critically important.

Segmentectomy vs Lobectomy for Clinical Stage IA Lung Adenocarcinoma With Spread Through Air Spaces

BACKGROUND

This study aimed to investigate the prognosis after segmentectomy as compared with lobectomy for small-sized lung adenocarcinoma with spread through air spaces (STAS).

METHODS

This retrospective study included 609 patients who underwent lobectomy or segmentectomy with lymph node dissection for clinical stage IA lung adenocarcinoma between April 2011 and March 2020 at Hiroshima University Hospital. Patient characteristics and prognosis after segmentectomy and lobectomy were investigated.

RESULTS

STAS was detected in 293 patients (48.1%). The recurrence-free survival (RFS) rate was significantly worse with STAS-positive adenocarcinoma than with STAS-negative adenocarcinoma both in patients who underwent lobectomy (5-year RFS, 68.2% vs 90.2%; P < .001) and in patients who underwent segmentectomy (5-year RFS, 81.3% vs 93.0%; P = .003). Among the patients with STAS, there was no significant difference in RFS between patients who underwent lobectomy (5-year RFS, 68.2%) and those who underwent segmentectomy (5-year RFS, 81.3%; P = .225). In a multivariable analysis using propensity score to adjust clinical patient characteristics, segmentectomy was not found to be an independent prognostic factor of RFS (hazard ratio 0.732, P = .326) among patients with STAS. Among the patients with STAS, only 1 patient (1%) with insufficient resection margin (0.5 mm) had local recurrence and 1 patient (1%) with invasive mucinous adenocarcinoma had recurrence in preserved lobe after segmentectomy.

CONCLUSIONS

Spread through air spaces was a poor prognostic factor in patients with clinical stage IA lung adenocarcinoma. Prognosis after segmentectomy was comparable with that of lobectomy in lung adenocarcinoma with STAS without increasing locoregional recurrence.

A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma

Background

Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based logistic regression model to predict STAS in lung adenocarcinoma.

Methods

This retrospective study was approved by the institutional review board of two centers and included 578 patients (462 from center I and 116 from center II) with pathologically confirmed lung adenocarcinoma. STAS was identified from 90 center I patients (19.5%) and 28 center II patients (24.1%) from. The maximum diameter, nodule area, and area of solid components in part-solid nodules were measured. Twenty-one semantic characteristics were assessed. Univariate analysis was used to select CT characteristics, which were associated with STAS in the patient cohort of center I. Multivariable logistic regression was used to develop a CT characteristics-based model on those variables with statistical significance. The model was validated in the validation cohort and then tested in the external test cohort (patients from center II). The diagnostic performance of the model was measured by area under the curve (AUC) of receiver operating characteristic (ROC).

Results

At univariate analysis, age and 11 CT characteristics, including the maximum diameter of the tumor, the maximum area of the tumor, the area and ratio of the solid component, nodule type, pleural thickening, pleural retraction, mediastinal lymph node enlargement, vascular cluster sign, and lobulation, specula were found to be significantly associated with STAS. The optimal logistic regression model included age, maximum diameter and ratio of solid component with odds ratio (OR) value of 0.967 (95% CI: 0.944-0.988), 1.027 (95% CI: 1.008-1.046) and 5.14 (95% CI: 2.180-13.321), respectively. This model achieved an AUC of 0.801 (95% CI: 0.709-0.892) and 0.692 (95% CI: 0.518-0.866) in the validation cohort and the external test cohort, respectively. The difference was not statistically significant (P=0.280).

Conclusions

CT-based logistic regression machine learning model could preoperatively predict STAS in lung adenocarcinoma with excellent diagnosis performance, which could be supplementary to routine CT interpretation.

Spread through air spaces (STAS) in invasive mucinous adenocarcinoma of the lung: Incidence, prognostic impact, and prediction based on clinicoradiologic factors

Background

Spread through air spaces (STAS) has recently been demonstrated to exhibit a negative impact on lung adenocarcinoma prognosis. However, most of these studies investigated STAS in nonmucinous adenocarcinoma. Here, we investigated the incidence of STAS in invasive mucinous adenocarcinoma (IMA) of the lung and evaluated whether tumor STAS was a risk factor of disease recurrence in IMA. We also examined clinicoradiologic factors in patients with IMA harboring STAS.

Methods

We reviewed pathologic specimens and imaging characteristics of primary tumors from 132 consecutive patients who underwent surgical resection for IMA to evaluate STAS. Patients with and without STAS were compared with respect to clinical characteristics as well as computed tomography (CT) imaging using logistic regression. The relationships between all variables including STAS and survival were analyzed.

Results

Among a total of 132 patients, full pathologic specimens were available for 119 patients, and STAS was observed in 86 (72.3%). IMA patients with STAS were significantly associated with older age, presence of lobulated and spiculated margins on CT scan (P = 0.009, P = 0.006, and P = 0.027). In multivariate analysis for overall survival (OS), STAS was a borderline independent poor prognostic predictor (P = 0.028). Older age, history of smoking, higher T stage, presence of lymph node metastasis, and consolidative morphologic type remained independent predictors for OS.

Conclusions

STAS was associated with reduced OS and was a borderline independent poor prognostic factor in IMA. IMA with STAS was associated with older age and presence of lobulated and spiculated margins on CT scan.

Key points

Significant findings of the study

Compared with other subtypes, IMA shows a higher incidence of STAS, which is an independent poor prognostic predictor even in IMA. Lobulated and spiculated margins on CT are associated with STAS.

What this study adds

Considering that STAS can carry the potential for aerogenous metastasis, predicting STAS using preoperative surrogate CT imaging is desirable to avoid limited resection.

Meta-analysis of association between CT-based features and tumor spread through air spaces in lung adenocarcinoma

Objective

Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma after sublobar resection. The aims of this study are to evaluate the association between computed tomography (CT)-based features and STAS for preoperative prediction of STAS in lung adenocarcinoma, eventually, which could help us choose appropriate surgical type.

Methods

Systematic research was conducted to search for studies published before September 1, 2019. The association between CT-based features of radiological tumor size>2 cm、pure solid nodule、 part-solid nodule or Percentage of solid component (PSC)>50% and STAS was evaluated. According to rigorous inclusion and exclusion criteria. Eight studies including 2385 patients published between 2015 and 2018 were finally enrolled in our meta-analysis.

Results

Our results clearly depicted that there is no significant relationship between radiological tumor size>2 cm and STAS with the combined OR of 1.47(95% CI:0.86–2.51). Meta-analysis of 3 studies showed that pure solid nodule in CT image were more likely to spread through air spaces with pooled OR of 3.10(95%CI2.17–4.43). Meta-analysis of 5 studies revealed the part-solid nodule in CT image may be more likely to appear STAS in adenocarcinoma (ADC) (combined OR:3.10,95%CI:2.17–4.43). PSC>50% in CT image was a significant independent predictor in the diagnosis of STAS in ADC from our meta-analysis with combined OR of 2.95(95%CI:1.88–4.63).

Conclusion

In conclusion, The CT-based features of pure solid nodule、part-solid nodule、PSC>50% are promising imaging biomarkers for predicting STAS in ADC and may substantially influence the choice of surgical type. In future, more studies with well-designed and large-scale are needed to confirm the conclusion.

Solid Nodule Appearance as a Predictor of Tumor Spread Through Air Spaces in Patients with Lung Adenocarcinoma: A Propensity Score Matching Study

OBJECTIVE

Spread through air spaces (STAS) has been reported to be an invasive histological pattern with poor prognosis in lung cancer; however, little is known about its intrinsic risk factors. This work analyzed the correlation between pathological and radiological features and STAS in resected lung adenocarcinomas.

PATIENTS AND METHODS

We retrospectively reviewed 1821 consecutive surgically treated patients with histologically diagnosed lung adenocarcinoma (174 positive for STAS and 1647 negative for STAS) from December 2017 to November 2018 at our institution. Propensity score matching identified 170 well-balanced pairs of patients. The correlations between pathological and radiological features and the presence of STAS were analyzed.

RESULTS

Before propensity matching, the incidence rate of STAS was 9.6% in all patients. In matched cohorts, multivariate analysis showed that the presence of STAS was significantly correlated with pure solid nodules (SNs) (p = 0.001) and solid/micropapillary patterns (SMPs) (p = 0.002). The odds ratio for STAS in SN-positive and SMP-positive adenocarcinoma against that in SN-negative and SMP-negative adenocarcinoma was 10.922 (95% confidence interval, 5.826-20.475; p < 0.001). Tumor differentiation, visceral pleural invasion (VPI), lymphovascular invasion (LVI), invasive adenocarcinoma, and non-lepidic subtype were significantly associated with STAS in the univariate analysis (p < 0.05); however, the differences failed to reach a significant level in the multivariate analysis.

CONCLUSION

We found that STAS was significantly correlated with several invasive clinicopathological patterns. The presence of SNs and SMPs were revealed as independent predictors for STAS, which could offer clinicians clues to identify STAS-positive adenocarcinoma.

Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma

To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.

The role of spread through air spaces (STAS) in lung adenocarcinoma prognosis and therapeutic decision making

Spread through air spaces (STAS) was included as a novel pattern of invasion in lung adenocarcinoma by the World Health Organization in 2015. Since then, multiple studies have investigated the association of STAS with clinicopathological and molecular features and its implication in the prognosis of early stage lung cancer patients undergoing different surgery types. The aim of this comprehensive review is to present current data on the role of STAS and its perspective in lung adenocarcinoma management.

Spread through air spaces affects survival and recurrence of patients with clinical stage IA non-small cell lung cancer after wedge resection

Background

Spread through air spaces (STAS) is reportedly a significant risk factor for recurrence and a prognostic factor in patients with non-small cell lung cancer (NSCLC), especially after sublobar resection. Because wedge resection (WR) is associated with insufficient margins, we hypothesized that STAS has a greater prognostic impact in patients who undergo WR compared with segmentectomy. We aimed to clarify the value of STAS as a prognostic factor in patients with NSCLC after WR.

Methods

We evaluated 217 patients with clinical stage IA NSCLC who underwent sublobar resection. The prognostic impact of STAS in these patients was compared between the WR (n=100) and segmentectomy (n=117) cases.

Results

STAS was present in 15.7% of the 217 patients. STAS was a significant prognostic factor for overall survival in univariate (P<0.001) and multivariate (P=0.003) analyses for the WR cases, but not the segmentectomy cases (P=0.399). STAS was also a significant prognostic factor for freedom from recurrence in univariate (P=0.010) and multivariate (P=0.024) analyses for the WR cases, but was only marginally significant for the segmentectomy cases (P=0.050, univariate analysis). The solid tumor size on chest computed tomography was significantly related to STAS. A cut-off solid tumor size of 1.7 cm for predicting the presence of STAS was determined by receiver operating characteristic analysis.

Conclusions

STAS was a significant prognostic factor for patients with clinical stage IA NSCLC who underwent WR, but not those who underwent segmentectomy.

Spread Through Air Spaces (STAS) in Lung Cancer: A Multiple-Perspective and Update Review

BACKGROUND

Spread through air spaces (STAS) is a spreading phenomenon of lung cancers, which is defined as tumor cells within air spaces in the lung parenchyma beyond the edge of the main tumor. To date, several articles have reviewed the studies concerning the significance of STAS; however, most articles focused on the prognosis without summarizing the significance of STAS on other aspects. In this review, we comprehensively summarized the current literature related to STAS, so as to explore the clinical significance of STAS from multiple perspectives.

MAIN BODY

This section provided a comprehensive overview of the significance of STAS from multiple perspectives and summarized current controversies and challenges in the diagnosis and clinical application.

CONCLUSION

STAS is a conspicuous spreading phenomenon of lung cancers indicating worse prognosis; nevertheless, the treatment strategy for patients with STAS remains to be discussed. Further studies are needed to elaborate whether a STAS-positive patient who underwent limited resection needs a second operation or postoperative adjuvant treatment. Meanwhile, the internal mechanism of STAS formation is largely undiscovered. Whether the capability of detachment-migration-reattachment in STAS tumor cells is achieved at the time of primary tumorigenesis or in the progress of tumor development needs to be studied, and the related signal pathways or genetic alterations need to be explored. With this information, it may be possible to improve the prognosis of patients with STAS-positive lung cancers.

Relevance and prognostic ability of Twist, Slug and tumor spread through air spaces in lung adenocarcinoma

BACKGROUND

Tumor spread through air spaces (STAS) is a novel pathologic characteristic in lung adenocarcinomas that indicates invasive tumor behavior. We aimed to explore the relationship between Twist, Slug and STAS in lung adenocarcinoma and to investigate the potential relationship between epithelial-mesenchymal transition (EMT) and STAS.

MATERIALS AND METHODS

Our study retrospectively analyzed 115 patients with resected lung adenocarcinomas to evaluate the relationship between Twist, Slug and STAS. STAS was diagnosed using hematoxylin-eosin (H&E) staining. Immunohistochemistry was used to evaluate the expression levels of Slug and Twist.

RESULTS

In this study, 56 (48.7%) patients had STAS, 40 (34.8%) patients had Slug overexpression, and 28 (24.3%) patients had Twist overexpression. Patients with either STAS or Slug and Twist overexpression experienced poor recurrence-free survival (RFS) and overall survival (OS). There were significant associations between Twist overexpression, Slug overexpression and the presence of STAS. The logistic model further revealed that pathological stage, Twist overexpression and Slug overexpression were independent risk factors for STAS. A multivariate analysis that contained Twist, Slug, pathologic stage and STAS, showed that pathologic stage and STAS were independent prognostic factors for poor RFS and OS. Another multivariate model that contained Twist, Slug and pathologic stage, showed that pathologic stage, Twist overexpression and Slug overexpression were independent risk factors for poor RFS and OS. In the cohort with STAS, the multivariate analysis showed that pathologic stage and Twist overexpression were independent risk factors for poor survival. The subgroup analysis showed that patients with both Slug overexpression and Twist overexpression with STAS received a poor prognosis.

CONCLUSIONS

STAS, Slug and Twist were correlated with poor RFS and OS in resected lung adenocarcinomas. Additionally, STAS was correlated with the overexpression of Twist and Slug, which could potentially provide information on the mechanism of STAS.

Stepwise flowchart for decision making on sublobar resection through the estimation of spread through air space in early stage lung cancer1

OBJECTIVES

The sensitivity for tumor spread through air space (STAS), an independent risk factor for locoregional recurrence after sublobar resection for lung cancer, has been relatively low in frozen sections. We aimed to determine predictors with high negative predictive value for the presence of STAS and to provide the flowchart in combination with these predictors for the decision-making for sublobar resection.

MATERIALS AND METHODS

Between July 2015 and December 2017, 387 patients who underwent surgery for non-small cell lung cancer (NSCLC) with pathologic findings of the total masses measuring ≤ 2 cm were enrolled. The lesions were divided into two groups according to presence of STAS. We compared the preoperative characteristics, operative data, and developed a flowchart for STAS prediction using receiver operator characteristic curve analysis and multivariable logistic regression.

RESULTS

The STAS-positive group (N = 111) had a significantly higher preoperative tumor size (1.70 [1.5] vs 1.50 [0.69], p < 0.001) and standardized uptake value tumor-to-liver (SUV T/L) ratio (1.40 [1.60] vs 0.60 [1.10], p < 0.001) and a significantly lower two-dimensional ground-glass opacity (GGO) percentage (35.86 [61.00] vs 78.14 [39.00], p < 0.001). Meanwhile, the STAS-negative group (N = 286) had higher lepidic predominance (41.6% vs. 1.8%, p < 0.001). We developed a flowchart for predicting STAS in combination with two-dimensional GGO percentage on computed tomography (CT), SUV T/L ratio on positron-emission CT, and lepidic predominant pattern. The sensitivity, specificity, and negative predictive value for STAS positivity were 79.3%, 68.5%, and 89.5%, respectively.

CONCLUSIONS

The stepwise flowchart using two-dimensional GGO percentage on CT, maximum SUV, and lepidic predominance might be helpful in selecting patients with early NSCLC for sublobar resection.