Tumor Spread Through Air Spaces Is a Predictor of Occult Lymph Node Metastasis in Clinical Stage IA Lung Adenocarcinoma

Predictors of occult lymph node metastasis in clinical T1 lung adenocarcinoma: a retrospective dual-center study

BACKGROUND

The optimal surgical strategy for lymph node dissection in lung adenocarcinoma remains controversial. Accurate predicting occult lymph node metastasis (OLNM) in patients with clinical T1 lung adenocarcinoma is essential for optimizing treatment decisions and improving patient outcomes. This study analyzes the relationship between anaplastic lymphoma kinase (ALK) status, clinicopathological characteristics, computed tomography (CT) features, and OLNM in patients with clinical T1 lung adenocarcinoma.

METHODS

A retrospective analysis was conducted on data from patients with clinical T1 lung adenocarcinoma who showed no lymph node metastasis on preoperative CT and underwent surgical resection with lymph node dissection at two centers from January 2016 to December 2023. Univariate and multivariate logistic regression analyses were performed to identify factors associated with OLNM.

RESULTS

Among 1138 patients with clinical T1 lung adenocarcinoma, 167 (14.6%) were found to have OLNM, including 55 (4.8%) with pathological N1 status and 112 (9.8%) with pathological N2 status. Multivariate logistic regression analysis identified lobulation, spiculation, solid density, lymphovascular invasion, spread through air spaces (STAS), micropapillary pattern, solid pattern, and carcinoembryonic antigen (CEA) levels as independent positive predictors of OLNM. Furthermore, lobulation, lymphovascular invasion, STAS, micropapillary pattern, solid pattern, CEA levels, and ALK were independent positive predictors of occult N2 lymph node metastasis. The lepidic pattern, however, was identified as an independent negative predictor for OLNM and occult N2 lymph node metastasis.

CONCLUSION

The identified predictors may assist clinicians in evaluating the risk of OLNM in patients with clinical T1 lung adenocarcinoma, potentially guiding more targeted intervention strategies.

Comparison of segmentectomy and wedge resection for cT1cN0M0 non-small cell lung cancer

OBJECTIVE

Sublobar resection is considered a standard surgical procedure for early non-small cell lung cancer, although the survival of patients undergoing sublobar resection for clinical T1cN0M0 non-small cell lung cancer remains unclear. This study aimed to compare survival between segmentectomy and wedge resection for clinical T1cN0M0 non-small cell lung cancer.

METHODS

This retrospective study included patients who had undergone curative surgery for cT1cN0M0 stage IA3 non-small cell lung cancer. The overall and recurrence-free survival rates of 91 patients who underwent segmentectomy or wedge resection were compared.

RESULTS

Thirty-nine (42.9%) and 52 patients (57.1%) were included in the segmentectomy and wedge resection groups, respectively. The median length of follow-up was 6.0 years (95% confidence interval 4.2 - - years) (Kaplan-Meier estimate). The 5 year overall survival rates were not significantly different between the segmentectomy and wedge resection groups (67.7% vs 52.0%, P = 0.132). The 5 year recurrence-free survival rate was worse in the wedge resection group than in the segmentectomy group (66.6% vs 46.9%, P = 0.047). In univariable analysis, spread through air spaces (hazard ratio, 5.889; 95% confidence interval, 2.357-14.715; P < 0.001) was an important prognostic factor for recurrence-free survival in the wedge resection group.

CONCLUSIONS

The overall survival of patients who underwent segmentectomy for clinical T1cN0M0 non-small cell lung cancer was not significantly different from that of patients who underwent wedge resection. However, patients with cT1cN0M0 non-small cell lung cancer who underwent wedge resection tended to have a worse recurrence-free survival prognosis than those who underwent segmentectomy.

A CT-based deep learning model for preoperative prediction of spread through air spaces in clinical stage I lung adenocarcinoma

Objective

To develop and validate a deep learning signature for noninvasive prediction of spread through air spaces (STAS) in clinical stage I lung adenocarcinoma and compare its predictive performance with conventional clinical-semantic model.

Methods

A total of 513 patients with pathologically-confirmed stage I lung adenocarcinoma were retrospectively enrolled and were divided into training cohort (n = 386) and independent validation cohort (n = 127) according to different center. Clinicopathological data were collected and CT semantic features were evaluated. Multivariate logistic regression analyses were conducted to construct a clinical-semantic model predictive of STAS. The Swin Transformer architecture was adopted to develop a deep learning signature predictive of STAS. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive and negative predictive value, and calibration curve. AUC comparisons were performed by the DeLong test.

Results

The proposed deep learning signature achieved an AUC of 0.869 (95% CI: 0.831, 0.901) in training cohort and 0.837 (95% CI: 0.831, 0.901) in validation cohort, surpassing clinical-semantic model both in training and validation cohort (all P<0.01). Calibration curves demonstrated good agreement between STAS predicted probabilities using deep learning signature and actual observed probabilities in both cohorts. The inclusion of all clinical-semantic risk predictors failed to show an incremental value with respect to deep learning signature.

Conclusions

The proposed deep learning signature based on Swin Transformer achieved a promising performance in predicting STAS in clinical stage I lung adenocarcinoma, thereby offering information in directing surgical strategy and facilitating adjuvant therapeutic scheduling.

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features

PURPOSE

This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns.

MATERIALS AND METHODS

As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features.

RESULTS

Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)-eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively).

CONCLUSION

Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

BACKGROUND

Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited.

PURPOSE

This study aimed to develop and validate a vision transformer-based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging.

METHODS

This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT-based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT-based DTL signature was combined with independent clinical predictors to construct a ViT-based deep transfer learning nomogram (DTLN).

RESULTS

The ViT-based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775-0.867) in the training cohort and 0.825 (95% CI, 0.758-0.891) in the validation cohort. The ViT-based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT-based DTL signature was then used to construct ViT-based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827-0.903) and 0.894 (95% CI, 0845-0942), respectively, surpassing both the clinical factor model and the ViT-based DTL model (p < 0.001).

CONCLUSION

This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

Predicting High-risk Lung Adenocarcinoma in Solid and Part-solid Nodules on Low-dose CT: A Multicenter Study

RATIONALE AND OBJECTIVES

High-grade patterns, visceral pleural invasion, lymphovascular invasion, spread through air spaces, and lymph node metastasis are high-risk factors and associated with poor prognosis in lung adenocarcinomas (LUADs). This study aimed to construct and validate a radiomic model and a radiographic model derived from low-dose CT (LDCT) for predicting high-risk LUADs in solid and part-solid nodules.

MATERIALS AND METHODS

This study retrospectively enrolled 658 pathologically confirmed LUADs from July 2018 to December 2022 from four centers, which were divided into training set (n=411), internal validation set (n=139), and external validation set (n=108). Radiomic features and radiographic features including maximal diameter, consolidation/tumor ratio (CTR), and semantic features, were obtained to construct a radiomic model and a radiographic model through multivariable logistic regression. Area under receiver operating characteristic curve (AUC) was utilized to assess the diagnostic performance of the models.

RESULTS

Three radiomic features (GLCM_Correlation, GLSZM_SmallAreaEmphasis, and GLDM_LargeDependenceHighGrayLevelEmphasis) and four radiographic features (maximal diameter, CTR, spiculation, and pleural indentation) were selected to build models. The radiomic model yielded AUCs of 0.916 in the internal validation set and 0.938 in the external validation set, which were significantly higher than the AUCs of the radiographic model (0.916 vs. 0.868, P=0.014 and 0.938 vs. 0.880, P=0.002).

CONCLUSION

Our LDCT-based radiomic model enabled non-invasive identification of high-risk LUADs in solid and part-solid nodules with good diagnostic performance and might assist in case-specific decision-making in lung cancer screening.

Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer and poses a serious threat to human health. Due to the advances in lung cancer screening, more and more clinical T1 NSCLC defined as a tumor with a maximum diameter of 3cm surrounded by lung tissue or visceral pleura have been detected and have achieved favorable treatment outcomes, greatly improving the prognosis of NSCLC patients. However, the preoperative lymph node staging and intraoperative lymph node dissection patterns of operable clinical T1 NSCLC are still subject to much disagreement, as well as the heterogeneity between primary tumors and metastatic lymph nodes poses a challenge in designing effective treatment strategies. This article comprehensively describes the clinical risk factors of clinical T1 NSCLC lymph node metastasis, and its invasive and non-invasive prediction, focusing on the genetic heterogeneity between the primary tumor and the metastatic lymph nodes, which is significant for a thoroughly understanding of the biological behavior of early-stage NSCLC.

Development and external validation of a nomogram for predicting lymph node metastasis in 1-3 cm lung adenocarcinoma

Aim: This study aimed to investigate the risk factors for lymph node metastasis in 1-3 cm adenocarcinoma and develop a new nomogram to predict the probability of lymph node metastasis.Materials & methods: This study collected clinical data from 1656 patients for risk factor analysis and an additional 500 patients for external validation. The logistic regression analyses were employed for risk factor analysis. The least absolute shrinkage and selection operator regression was used to select variables, and important variables were used to construct the nomogram and an online calculator.Results: The nomogram for predicting lymph node metastasis comprises six variables: tumor size (mediastinal window), consolidation tumor ratio, tumor location, lymphadenopathy, preoperative serum carcinoembryonic antigen level and pathological grade. According to the predicted results, the risk of lymph node metastasis was divided into low-risk group and high-risk group. We confirmed the exceptional clinical efficacy of the model through multiple evaluation methods.Conclusion: The importance of intraoperative frozen section is increasing. We discussed the risk factors for lymph node metastasis and developed a nomogram to predict the probability of lymph node metastasis in 1-3 cm adenocarcinomas, which can guide lymph node resection strategies during surgery.

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

Objectives

To develop computed tomography (CT)-based models to increase the prediction accuracy of spread through air spaces (STAS) in clinical-stage T1N0 lung adenocarcinoma.

Methods

Three 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.

Results

Among 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.

Conclusions

CT-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.

Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study

Objective

To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients.

Methods

This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet). The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model. Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography (HRCT) features for the model. The predictive performance was assessed by area under the curves (AUCs), accuracy, precision, recall, and F1 score. Subgroup analysis was performed to evaluate the potential bias of the study population.

Results

A total of 1,009 patients were included in this study; 409 (40.5%) were male and 600 (59.5%) were female. The median age was 57.0 years (inter-quartile range, IQR: 50.0-64.0). The deep learning model achieved AUCs of 0.906 (95% CI: 0.873-0.938) and 0.893 (95% CI: 0.857-0.930) for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule (non-pGGN) testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.622). The precisions of this model for predicting pN0 disease were 0.979 (95% CI: 0.963-0.995) and 0.983 (95% CI: 0.967-0.998) in the testing cohort and the non-pGGN testing cohort, respectively. The deep learning model achieved AUCs of 0.848 (95% CI: 0.798-0.898) and 0.831 (95% CI: 0.776-0.887) for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.657). The recalls of this model for predicting pN2 disease were 0.903 (95% CI: 0.870-0.936) and 0.931 (95% CI: 0.901-0.961) in the testing cohort and the non-pGGN testing cohort, respectively.

Conclusions

The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

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.

Dysfunction of CD8+ T cells around tumor cells leads to occult lymph node metastasis in NSCLC patients

Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.

A clinical nomogram for predicting occult lymph node metastasis in patients with non-small-cell lung cancer ≤2 cm

OBJECTIVES

Sublobar resection has been shown to be feasible for non-small-cell lung cancers (NSCLC) <2 cm in size based on several prospective studies. However, the prognosis of clinical N0 patients who experience an N-stage upgrade after surgery [known as occult lymph node metastasis (OLM)] may be worse. The ability of predict OLM in patients eligible for sublobar resection remains a controversial issue.

METHODS

Patients with NSCLC ≤2 cm in diameter and containing a solid component who underwent surgical treatment at the Affiliated Hospital of Qingdao University were retrospectively enrolled, and 1:1 case matching was performed. The risk factors were identified through logistic regression analyses and theoretical criteria, followed by the development of a nomogram that was evaluated using 200 iterations of 10-fold cross-validation.

RESULTS

After case matching, 130 pairs of patients were selected for modelling. According to the multivariable logistic regression analysis, the carcinoembryonic antigen level, consolidation tumour ratio, mean computed tomography number and tumour margin were included in the nomogram. The cross-validated average area under the receiver operating characteristic curve was found to be 0.86. Furthermore, calibration curve and decision curve analyses demonstrated the excellent predictive accuracy and clinical utility of the nomogram respectively.

CONCLUSIONS

By utilizing accessible characteristics, we developed a nomogram that predicts the probability of OLM in patients with NSCLC ≤2 cm with a solid component. Risk stratification with this nomogram could aid in surgical method decision-making.

CLINICAL REGISTRATION NUMBER

Not applicable.

Improving intraoperative diagnosis of spread through air spaces: A cryo-embedding-medium inflation method for frozen section analysis

Objective

Accurate intraoperative diagnosis of spread through air spaces (STAS), a known poor prognostic factor in lung cancer, is crucial for guiding surgical decision-making during sublobar resections. This study aimed to evaluate the diagnostic sensitivity of STAS using frozen section (FS) slides prepared with the cryo-embedding medium inflation technique.

Methods

In this prospective study at Shinshu University Hospital, 99 patients undergoing lung resection for tumors <3 cm in size were included, a total of 114 lesions. FS slides were prepared with injecting diluted cryo-embedding medium into the lung parenchyma of resected specimens. The diagnostic performance of these FS slides for STAS detection was evaluated by comparing FS-STAS results with the gold-standard STAS status.

Results

The incidence of STAS, determined by the gold standard, was 43 (38%) of 114 lesions, including 31 (37%) of 84 primary lung cancers and 12 (40%) of 30 metastatic lung tumors. The sensitivity, specificity, positive and negative predictive values, and accuracy of FS slides for STAS detection were 81%, 89%, 81%, 89%, and 86%, respectively. Specifically, in primary lung cancers, these values were 90%, 89%, 82%, 94%, and 89%, respectively. Regarding metastatic lung tumors, the corresponding values were 58%, 89%, 78%, 76%, and 77%, respectively.

Conclusions

Our adapted cryo-embedding medium inflation method has demonstrated enhanced sensitivity in detecting STAS on FS slides, providing results similar to the gold-standard STAS detection. Compared with historical benchmarks, this technique could show excellent performance and be readily incorporated into clinical practice without requiring additional resources beyond those used for standard FS analysis.

Prognostic impact of spread through air spaces in patients with ≤2 cm stage IA lung adenocarcinoma

Background

In 2015, the World Health Organization (WHO) included spread through air space (STAS) as a new invasive mode of lung cancer. As a new mode of lung cancer dissemination, STAS has a significant and negative impact on patient prognosis. The surgical approach as well as lymph node dissection (LND) for STAS-positive patients is currently unclear. The aim of this study was to investigate the impact of different surgical approaches to STAS and LND on the prognosis of patients with ≤2 cm stage IA lung adenocarcinoma (LUAD). This study also investigated the possible relationship between STAS and the micropapillary histological subtype and its impact on patient prognosis.

Methods

A total of 212 patients with LUAD were included in this study from January 2016 to December 2017, and the overall survival (OS) of the patients was compared. The chi-square test and t-test were applied to compare the clinicopathological data of the patients, and the Cox model was used for the multivariate survival analysis.

Results

Of the 212 patients, 93 (43.9%) were STAS positive. The univariate analysis showed that the surgical approach, LND type, micropapillary pattern (MP), solid pattern, and STAS were risk factors for OS. The multivariate analysis showed that the surgical approach, MP, and STAS were risk factors for OS. The STAS-positive patients who underwent lobectomy had a better prognosis than those who underwent sublobar resection; however, there was no significant difference between the two surgical procedures in the STAS-negative group. Additionally, the STAS-positive patients who underwent systematic lymph node dissection (SLND) had a better prognosis than those who underwent limited lymph node dissection (LLND); however, there was no significant difference between the two LNDs in the STAS-negative group.

Conclusions

STAS plays an important role in patient prognosis and is an independent risk factor for OS of patients with ≤2 cm stage IA LUAD. When STAS is positive, the choice of lobectomy with SLND may result in a better long-term prognosis for patients.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography

RATIONALE AND OBJECTIVES

To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules.

MATERIALS AND METHODS

The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis.

RESULTS

The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules.

CONCLUSION

Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

The value of high-risk clinicopathologic features for chemotherapy in stage I non-small cell lung cancer: a propensity score-matched study

Background

Surgical resection is the main treatment for early-stage non-small cell lung cancer (NSCLC), but recurrence remains a concern. Adjuvant chemotherapy has been shown to have survival benefits for resected stage II and III NSCLC, but debate continues regarding its use in stage I NSCLC. High-risk features, such as tumor size and stage, are considered in deciding whether to administer adjuvant chemotherapy.

Methods

The data of 666,689 patients diagnosed with lung cancer from 2004 to 2016 were collected from the Surveillance, Epidemiology, and End Results database. Ultimately, 26,160 patients diagnosed with stage I NSCLC were included in the study based on a screening procedure.

Results

After matching, 4,285 patients were identified, of whom 1,440 (33.6%) received chemotherapy. High-risk clinicopathologic features, including a high histologic grade, visceral pleural invasion (VPI), the examination of an insufficient number of lymph nodes (LNs), and limited resection, were independent risk factors for a poor prognosis. Chemotherapy significantly improved lung cancer-specific survival (LCSS) and overall survival (OS) in stage I patients with VPI [LCSS: hazard ratio (HR): 0.839, 95% confidence interval (CI): 0.706-0.998, P=0.047; OS: HR: 0.711, 95% CI: 0.612-0.826, P<0.001], regardless of whether or not the patient had fewer than 11 LNs (LCSS: HR: 0.809, 95% CI: 0.664-0.986, P=0.04; OS: HR: 0.677, 95% CI: 0.570-0.803, P<0.001). Chemotherapy was only observed to improve OS for stage IB patients with a high histologic grade when combined with either or both of the following high-risk factors: the presence of VPI and fewer than 11 LNs examined.

Conclusions

The presence of VPI was the dominant predictor and the examination of an insufficient number of LNs was the secondary indicator, and a high histologic grade was a potential indicator of the need to administer chemotherapy in the treatment of stage I NSCLC.

Preliminary exploration of the correlation between spectral computed tomography quantitative parameters and spread through air spaces in lung adenocarcinoma

Background

The invasive pattern called spread through air spaces (STAS) is linked to an unfavorable prognosis in patients with lung adenocarcinoma (LUAD). Using computed tomography (CT) signs alone to assess STAS is subjective and lacks quantitative evaluation, whereas spectral CT can provide quantitative analysis of tumors. The aim of this study was to investigate the association between spectral CT quantitative parameters and STAS in LUAD.

Methods

We retrospectively collected consecutive patients with LUAD who underwent surgical resection and preoperative spectral CT scan at our institution. The quantitative parameters included CT values at 40, 70, and 100 keV [CT40keVa/v, CT70keVa/v, and CT100keVa/v (a: arterial; v: venous)]; iodine concentration (ICa/ICv); normalized iodine concentration (NICa/NICv); and slope λHU of the spectral curve (λHUa/λHUv). Clinical and CT features of the patients were also collected. Statistical analysis was performed to identify the quantitative parameters, clinical and CT features that were significantly correlated with STAS status. We evaluated the diagnostic performance of significant factors or models which combined quantitative parameters and CT features, using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Results

We enrolled a total of 47 patients, with 32 positive and 15 negative for STAS. The results revealed that CT100keVa (P=0.002), CT100keVv (P=0.007), pathologic stage (P=0.040), tumor density (P<0.001), spiculation (P=0.003), maximum solid component diameter (P=0.008), and the consolidation/tumor ratio (CTR) (P=0.001) were significantly correlated with STAS status. The tumor density demonstrated a superior diagnostic capability [AUC =0.824, 95% confidence interval (CI): 0.709-0.939, sensitivity =59.4%, specificity =100.0%] compared to other variables. CT100keVa exhibited the best diagnostic performance (AUC =0.779, 95% CI: 0.633-0.925, sensitivity =78.1%, specificity =80.0%) among the quantitative parameters. Combination models were then constructed by combining the quantitative parameters with CT features. The total combined model showed the highest diagnostic efficiency (AUC =0.952, 95% CI: 0.894-1.000, sensitivity =90.6%, specificity =86.7%).

Conclusions

Spectral CT quantitative parameters CT100keVa and CT100keVv may be potentially useful parameters in distinguishing the STAS status in LUAD.

CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer

BACKGROUND

The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration.

OBJECTIVE

This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models.

METHODS

We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated.

RESULTS

Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature.

CONCLUSIONS

Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.

Predictability and Utility of Contrast-Enhanced CT on Occult Lymph Node Metastasis for Patients with Clinical Stage IA-IIA Lung Adenocarcinoma: A Double-Center Study

RATIONALE AND OBJECTIVES

With the advantage of minimizing damage and preserving more functional lung tissue, limited surgery is considered depend on the lymph node (LN) involvement situation. However, occult lymph node metastasis (OLM) may be ignored by limited surgery and become a risk factor for local recurrence after surgical resection. The aim of this study was to assess the risk factors for OLM based on computed tomography enhanced image in patients with clinical lung adenocarcinoma (ADC).

MATERIALS AND METHODS

From January 2016 to July 2022, 707 patients with clinical stage IA-IIA ADC underwent lobectomy with systematic LN dissection and were divided into training and validation group based on different institution. Univariate analysis followed by multivariable logistic regression were performed to estimate different risk factors of OLM. A predictive model was established with visual nomogram and external validation, and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

Fifty-nine patients were diagnosed with OLM (11.9%), and four independent predictors of LN involvement were identified: larger consolidation diameter (odds ratio [OR], 2.35, 95% confidence interval [CI]: 1.06, 5.22, p = 0.013), bronchovascular bundle thickening (OR, 1.99, 95% CI: 1.00, 3.95, p = 0.049), lobulation (OR, 2.92, 95% CI: 1.22, 6.99, p = 0.016) and obstructive change (OR, 1.69, 95% CI: 1.17, 6.16, p = 0.020). The model showed good calibration (Hosmer-Lemeshow goodness-of-fit, p = 0.816) with an AUC of 0.821 (95% CI: 0.775, 0.853). For the validation group, the AUC was 0.788 (95% CI: 0.732, 0.806).

CONCLUSION

Our predictive model can non-invasively assess the risk of OLM in patients with clinical stage IA-IIA ADC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision-making procedure.

Micropapillary and Solid Histologic Patterns in N1 and N2 Lymph Node Metastases Are Independent Factors of Poor Prognosis in Patients With Stages II to III Lung Adenocarcinoma

INTRODUCTION

High-grade histologic patterns are associated with poor prognosis in patients with primary nonmucinous lung adenocarcinoma (ADC). We investigated whether the presence of micropapillary (MIP), solid (SOL), or both patterns in lymph node (LN) metastases has prognostic value.

METHODS

Patients who underwent lobectomy for pathologic stages II to III lung ADC with N1 or N2 LN metastases (N = 360; 2000-2012) were analyzed. We assessed overall survival (OS), lung cancer-specific cumulative incidence of death (LC-CID), and cumulative incidence of recurrence (CIR) between patients with and without MIP/SOL patterns in LN metastases. Multivariable Cox regression analysis was used to quantify the association between MIP/SOL patterns and outcomes.

RESULTS

MIP and SOL in LN metastases were associated with a higher incidence of smoking history (p = 0.004), tumor necrosis (p = 0.013), and spread of tumor through air spaces (p < 0.0001), a higher prevalence of MIP or SOL in the primary tumor (p < 0.0001), shorter OS (5-y OS, 40% [95% confidence interval or CI: 29%-56%] versus 63% [48%-83%] for no MIP/SOL in LNs, p = 0.03), higher LC-CID (5-y, 43% [29%-56%] versus 14% [4%-29%], p = 0.013), and higher CIR (5-y, 65% [50%-77%] versus 43% [25%-60%], p = 0.057). MIP and SOL in LN metastases were independently associated with poor outcomes: OS (hazard ratio [HR] = 1.81 [95% CI: 1.00-3.29], p = 0.05), LC-CID (HR = 3.10 [1.30-7.37], p = 0.01), and CIR (HR = 2.06 [1.09-3.90], p = 0.026).

CONCLUSIONS

MIP/SOL histologic patterns in N1 or N2 LN metastases are associated with worse outcomes in patients with stages II to III lung ADC. MIP/SOL histologic patterns in LN metastases can stratify patients with high-risk stages II to III lung ADC.

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.

Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

OBJECTIVES

This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features.

METHODS

The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results.

RESULTS

The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%).

CONCLUSION

The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC.

KEY POINTS

• The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.

Role of intrapulmonary lymph nodes in patients with NSCLC and visceral pleural invasion. The VPI 1314 multicenter registry study protocol

BACKGROUND

In the lung cancer classification (TNM), the involvement of thoracic lymph nodes is relevant from a diagnostic and prognostic point of view. Even if imaging modality could help in selecting patients who should undergo surgery, a systematic lymph node dissection during lung surgery is mandatory to identify the subgroup of patients who can benefit from an adjuvant treatment.

METHODS

Patients undergoing elective lobectomy/bilobectomy/segmentectomy) for non-small cell lung cancer and lymphadenectomy with lymph nodes station 10-11-12-13-14 sampling that meet the inclusion and exclusion criteria will be recorded in a multicenter prospective database. The overall incidence of N1 patients (subclassified in: Hilar Lymph nodes, Lobar Lymph nodes and Sublobar Lymph nodes) will be examined as well as the incidence of visceral pleural invasion.

DISCUSSION

The aim of this multicenter prospective study is to evaluate the incidence of intrapulmonary lymph nodes metastases and the possible relation with visceral pleural invasion. Identifying patients with lymph node station 13 and 14 metastases and/or a link between visceral pleural invasion and presence of micro/macro metastases in intrapulmonary lymph nodes may have an impact on decision-making process.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT05596578.

Clinical analysis of 78 pulmonary sarcomatoid carcinomas with surgical treatment

OBJECTIVE

To evaluate clinical factors influencing the postoperative pulmonary sarcomatoid carcinoma (PSCs) prognosis.

METHODS

We retrospectively evaluated patients with PSCs treated from October 2012 to October 2017. Kaplan-Meier survival curves were calculated using univariable analysis (log-rank test). Univariable/multivariable Cox regression analysis was also performed.

RESULTS

Mixed PSCs were most common (64.10%). Pure PSCs occurred more often with large tumors compared with mixed PSCs. Patients with vs without pleural retraction, respectively, had significantly worse overall survival (OS; 16 vs 23 months) and disease-free survival (DFS; 11 vs 20 months), and patients with airway dissemination had significantly shorter OS (14 vs 21 months) and DFS (11 vs 20 months). Patients with PSC with an adenocarcinoma component had favorable OS. Airway dissemination, pleural retraction, metastatic mediastinal lymph node (LN) number, and pathological tumor-node-metastasis (pTNM) stage were risk factors for short OS. Neither adjuvant chemotherapy nor adjuvant radiotherapy provided a survival advantage. Airway dissemination was an independent prognostic factor (odds ratio, 1.87; 95% confidence interval, 1.04-3.36).

CONCLUSION

Pure PSCs were more likely with large tumors compared with mixed PSCs. Airway dissemination, pleural retraction, and metastatic mediastinal LN number were associated with OS. Airway dissemination was an independent prognostic factor.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm

Objective

We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance.

Methods

The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test.

Results

The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model.

Conclusion

In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma

Objective

Spread through air space (STAS) is an invasive characterization of lung adenocarcinoma and is regarded as a risk factor for poor prognosis. The aim of this study is to develop a random forest model for preoperative prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma.

Methods

92 patients with stage IA lung adenocarcinoma, who underwent computed tomography (CT) scan and surgical resection, were retrospectively reviewed. Each pulmonary nodule was automatically segmented by artificial intelligence (AI) software, and its CT-based radiomics were extracted. All patients were pathologically classified into STAS-negative and STAS-positive cohorts; then, clinical pathological and CT-based radiomics were compared between the two cohorts. Finally, a prediction model for evaluating STAS status in stage IA lung adenocarcinoma was established by a random forest model.

Results

Among 92 patients with stage IA lung adenocarcinoma, STAS positive was identified in 19 patients. The random forest classification model identified predictive features, including CT maximum value, consolidation to tumor ratio (CTR), 3D diameter, CT mean value, entropy, and CT minimum value. The misclassification rate of the random forest model is only 7.69%.

Conclusion

The risk factors of STAS in stage IA lung adenocarcinoma can be effectively identified based on the random forest model, and the hierarchical management of characteristic risk can be effectively realized. A random forest model for predicting STAS in IA lung adenocarcinoma is simple and practical.

Retrospective analysis of the prognostic implications of tumor spread through air spaces in lung adenocarcinoma patients treated with surgery

Background

Tumor spread through air spaces (STAS) in lung adenocarcinoma is a novel mechanism of invasion. STAS has been proposed as an independent predictor of poor prognosis. The aim of this study was to evaluate the correlations between STAS status and other clinicopathologic variables and to assess the prognostic implications of STAS and the distance from the edge of the tumor to the farthest STAS in patients with resected lung adenocarcinoma.

Material and methods

This is a single-institution retrospective observational study. We included all patients with resected lung adenocarcinoma from January 2017 to December 2018 at La Paz University Hospital. The cut-off for the distance from the edge of the tumor to the farthest STAS was 1.5 mm and was assessed by the area under the receiver operating characteristic curve.

Results

A total of 73 patients were included. STAS was found in 52 patients (71.2%). Histological grade 3 (P = 0.035) and absence of lepidic pattern (P = 0.022) were independently associated with the presence of STAS. The median recurrence-free survival (RFS) was 48.06 months [95% confidence interval (CI) 33.58 months to not reached]. STAS-positive patients had shorter median RFS [39.23 months (95% CI 29.34-49.12 months)] than STAS-negative patients (not reached) (P = 0.04). STAS-positive patients with a distance from the edge of the tumor to the farthest STAS ≥1.5 mm had an even shorter median RFS [37.63 months (95% CI 28.14-47.11 months)]. For every 1 mm increase in distance, the risk of mortality increased by 1.26 times (P = 0.04).

Conclusions

Histological grade 3 and absence of lepidic pattern were independently associated with the presence of STAS. STAS was associated with a higher risk of recurrence. The distance from the edge of the tumor to the farthest STAS also had an impact on overall survival.

•

Lung adenocarcinoma patients with STAS had higher risk of recurrence.

•

Patients with STAS and a distance from the edge of the tumor to the farthest STAS ≥1.5 mm had an even shorter RFS.

•

The distance from the edge of the tumor to the farthest STAS also had an impact on overall survival.

Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

Background

The efficacy of sublobar resection and selective lymph node dissection is gradually being accepted by thoracic surgeons for patients within early-stage non-small cell lung cancer (NSCLC). Nevertheless, there are still some NSCLC patients develop lymphatic metastasis at clinical T1 stage. Lung adenocarcinoma with a micropapillary (MP) component poses a higher risk of lymph node metastasis and recurrence even when the MP component is not predominant. Our study aimed to explore the genetic features and occult lymph node metastasis (OLNM) risk factors in patients with a non-predominant micropapillary component (NP-MPC) in a large of patient’s cohort with surgically resected lung adenocarcinoma.

Methods

Between January 2019 and December 2021, 6418 patients who underwent complete resection for primary lung adenocarcinoma at the Qilu Hospital of Shandong University. In our study, 442 patients diagnosed with lung adenocarcinoma with NP-MPC with a tumor size ≤3 cm were included. Genetic alterations were analyzed using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Abnormal protein expression of gene mutations was validated using immunohistochemistry. A nomogram risk model based on clinicopathological parameters was developed to predict OLNM. This model was invalidated using the calibration plot and concordance index.

Results

In our retrospective cohort, the incidence rate of the micropapillary component was 11.17%, and OLNM was observed in 20.13% of the patients in our study. ARMS-PCR suggested that EGFR exon 19 del was the most frequent alteration in NP-MCP patients compared with other gene mutations (frequency: 21.2%, P<0.001). Patients harboring exon 19 del showed significantly higher risk of OLNM (P< 0.001). A nomogram was developed based on five risk parameters, which showed good calibration and reliable discrimination ability (C-index = 0.84) for evaluating OLNM risk.

Conclusions.

Intense expression of EGFR exon 19 del characterizes lung adenocarcinoma in patients with NP-MCP and it’s a potential risk factor for OLNM. We firstly established a nomogram based on age, CYFRA21-1 level, tumor size, micropapillary and solid composition, that was effective in predicting OLNM among NP-MCP of lung adenocarcinoma measuring ≤ 3 cm.

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.

Prognostic impact of spread through air spaces in lung adenocarcinoma

OBJECTIVE

Spread through air spaces (STAS) is a pattern of invasion present in some adenocarcinomas (ADC). The goal of this study was to assess the impact of STAS in patients treated with different types of surgical resections and on the clinical outcome in patients with ADC of different diameters and with different degrees of nodal involvement.

METHODS

A total of 109 patients were reviewed. Complete surgical resection with systematic nodal dissection was achieved in all patients. The median follow-up was 65 months (3-90 months).

RESULTS

STAS was observed in 70 cases (64.2%); 13 patients (18.5%) had lymph node involvement (N1 and N2). Overall survival and progression-free survival were higher in patients without STAS (P = 0.042; P = 0.027). The presence of STAS in tumours ≤2 cm was a predictor of worse progression-free survival following sublobar resection compared to major resections (P = 0.011). Sublobar resection of N0 STAS-positive tumours was associated with worse long-term survival compared to a major resection (P = 0.04). Statistical analyses showed that age >70 years and recurrence were independent variables for survival; smoking pack-years >20, sublobar resection and nodal involvement were independent variables for recurrence; and smoking pack-years >20 were independent variables for a history of cancer and pleural invasion for local recurrence.

CONCLUSIONS

STAS seems to play a role in long-term survival, particularly for patients with N0 and tumours smaller than 2 cm. Further studies are necessary to validate this hypothesis.

Preoperative risk factors of lymph node metastasis in clinical N0 lung adenocarcinoma of 3 cm or less in diameter

BACKGROUND

Lung adenocarcinoma is the most common subtype of non-small cell lung cancer. The surgical strategy of lymph node dissection is controversial because many more patients are diagnosed at an early stage in clinical practice.

METHODS

We retrospectively reviewed 622 clinical N0 lung adenocarcinoma patients with 3 cm or less in tumor size who underwent lobectomy or segmentectomy combined with lymph node dissection in our hospital from January 2017 to December 2019. We performed univariate and multivariate analyses to identify preoperative risk factors of lymph node metastasis.

RESULTS

Lymph node metastasis was found in 60 out of 622 patients. On univariate analysis, lymph node metastasis was linked to smoking history, preoperative CEA level, tumor size, tumor location (peripheral or central), consolidation/tumor ratio, pleural invasion, and pathologic type. However, only the preoperative CEA level, tumor size, and consolidation/tumor ratio were independent risk factors in multivariate analysis. The ROC curve showed that the cutoff value of tumor size was 1.7 cm. There was no lymph node metastasis in patients without risk factors.

CONCLUSIONS

The preoperative CEA level, tumor size, and consolidation/tumor ratio were independent risk factors of lymph node metastasis in clinical N0 lung adenocarcinoma with tumor size ≤ 3 cm. The lymph node metastasis rate was extremely low in clinical N0 lung adenocarcinoma patients without risk factors and lymph node dissection should be avoided in these patients to reduce surgical trauma.

Clinicopathological Impact of the Spread through Air Space in Non-Small Cell Lung Cancer: A Meta-Analysis

This study aimed to elucidate the clinicopathological significance of spread through air space (STAS) in non-small cell lung cancer (NSCLC) through a meta-analysis. Using 47 eligible studies, we obtained the estimated rates of STAS in various histological subtypes of NSCLC and compared the clinicopathological characteristics and prognosis between NSCLC with and without STAS. The estimated STAS rate was 0.368 (95% confidence interval [CI], 0.336–0.0.401) in patients with NSCLC. Furthermore, the STAS rates for squamous cell carcinoma and adenocarcinoma were 0.338 (95% CI, 0.273–0.411) and 0.374 (95% CI, 0.340–0.409), respectively. Among the histological subtypes of adenocarcinoma, micropapillary-predominant tumors had the highest rate of STAS (0.719; 95% CI, 0.652–0.778). The STAS rates of solid- and papillary-predominant adenocarcinoma were 0.567 (95% CI, 0.478–0.652) and 0.446 (95% CI, 0.392–0.501), respectively. NSCLCs with STAS showed a higher visceral pleural, venous, and lymphatic invasion than those without STAS. In addition, anaplastic lymphoma kinase mutations and ROS1 rearrangements were significantly more frequent in NSCLCs with STAS than in those without STAS. The presence of STAS was significantly correlated with worse overall and recurrence-free survival (hazard ratio, 2.119; 95% CI, 1.811–2.480 and 2.372; 95% CI, 2.018–2.788, respectively). Taken together, the presence of STAS is useful in predicting the clinicopathological significance and prognosis of patients with NSCLC.

Lung cancer with PET/CT-defined occult nodal metastasis yields favourable prognosis and benefits from adjuvant therapy: a multicentre study

PURPOSE

To investigate the surgical prognosis and efficacy of adjuvant therapy in non-small cell lung cancer (NSCLC) with occult lymph node metastasis (ONM) defined by positron emission tomography/computed tomography (PET/CT).

METHODS

A total of 3537 NSCLC patients receiving surgical resection were included in this study. The prognosis between patients with ONM and evident nodal metastasis, ONM patients with and without adjuvant therapy was compared, respectively.

RESULTS

ONM was associated with significantly better prognosis than evident nodal metastasis whether for patients with N1 (5-year OS: 56.8% versus 52.3%, adjusted p value = 0.267; 5-year RFS: 44.7% versus 33.2%, adjusted p value = 0.031) or N2 metastasis (5-year OS: 42.8% versus 32.3%, adjusted p value = 0.010; 5-year RFS: 31.3% versus 21.6%, adjusted p value = 0.025). In ONM population, patients receiving adjuvant therapy yielded better prognosis comparing to those without adjuvant therapy (5-year OS: 50.1% versus 33.5%, adjusted p value < 0.001; 5-year RFS: 38.4% versus 22.1%, adjusted p value < 0.001).

CONCLUSIONS

ONM defined by PET/CT identifies a unique clinical subtype of lung cancer, ONM is a favorable prognostic factor whether for pathological N1 or N2 NSCLC and adjuvant therapy could provide additional survival benefits for ONM patients.

Preoperative CT-based peritumoral and tumoral radiomic features prediction for tumor spread through air spaces in clinical stage I lung adenocarcinoma

OBJECTIVES

This study aims to develop and evaluate preoperative CT-based peritumoral and tumoral radiomic features to predict tumor spread through air space (STAS) status in clinical stage I lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

From June 2018 to December 2019, a retrospective diagnostic investigation was done. Patients with pathologically confirmed STAS status (N = 256) were eventually enrolled. The development cohort consisted of 191 patients (74.6%) chosen randomly in a 7:3 ratio, whereas the validation group consisted of 65 patients (25.4%). The performance of models was assessed using receiver operating characteristic analysis, accuracy, sensitivity, specificity, negative predictive values, and positive predictive values.

RESULTS

The STAS positive status was found in 85 (33.2%) of the 256 patients (female: 53.2%; median [IQR] age: 62.0, [53.0-79.0] years), while the STAS negative status was found in 171 patients (66.8%) (female:50.6%; median [IQR] age: 62.0, [53.0-87.0] years). The combined TRS and PRS-15 mm model had an AUC of 0.854 (95% CI, 0.799-0.909) in the development cohort and 0.870 (95% CI, 0.781-0.958) in the validation cohort, indicating that the tumor radiomic signature (TRS) model and different peritumoral radiomic signature (PRS) models were used to build the optimal gross radiomic signature (GRS) model. The radiomic nomogram achieves superior discriminatory performance than GRS and clinical and radiological signatures (CRS), with an AUC of 0.871 (95% CI, 0.820-0.922) in the development cohort and AUC of 0.869 (95% CI, 0.776-0.961) in the validation cohort. Based on the Akaike information criterion (AIC) and decision curve analysis (DCA), the radiomic nomogram provided greater clinical predictive capacity than clinical or any radiomic signatures alone.

CONCLUSION

In conclusion, we discovered that peritumoral characteristics were substantially related to STAS status. This study revealed the unit of radiomic signature and clinical signatures may have a better performance in STAS status.

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.

Spread through air spaces is a common phenomenon of pulmonary metastasized tumours regardless of origins

OBJECTIVES

Spread through air spaces (STAS) is a unique pattern of invasion in primary lung cancers. However, little is known about STAS in pulmonary metastases (PMs). This study was to investigate the incidence of STAS among PMs and the association between STAS and clinicopathological characteristics of PMs.

METHODS

A total of 127 patients who underwent metastasectomy at our institution from June 2009 to December 2019 were retrospectively analysed. Survival analysis was performed in 40 patients with PM from colorectal cancer (CRC).

RESULTS

STAS was identified in 33.1% of patients (42 of 127) with PMs. STAS was found in PMs of various primary cancers, including CRC, breast cancer, renal cell carcinoma, cholangiocarcinoma and osteogenic and soft tissue sarcoma, but the incidence varies. PMs originating from epithelial tissue showed higher incidence of STAS than those from mesenchymal tissue (45% vs 11%, P < 0.001). Elder age (P = 0.006) and primary sites (P < 0.001) were significantly correlated with STAS. In patients with PMs from CRC, the presence of STAS was an independent predictor of shorter recurrence-free survival (hazard ratio = 10.25, P = 0.002) and poor overall survival (hazard ratio = 4.75, P = 0.047) by multivariable analysis.

CONCLUSIONS

STAS might be a lung-specific tumour invasion pattern and STAS is commonly observed in PMs of different origins. The incidence of STAS was significantly higher in PMs originating from epithelial tissues than those from mesenchymal tissues. Presence of STAS was an independent predictor of poor prognosis in patients with PM from CRC.

Spread Through Air Spaces (STAS) in Non-Small Cell Lung Carcinoma: Evidence Supportive of an In Vivo Phenomenon

Tumor spread through air spaces (STAS) is associated with locoregional recurrence in patients undergoing limited resection (LR) for non-small cell lung carcinoma (NSCLC). We hypothesized that the observation of STAS in both the initial LR specimen and the additional resection specimen from the same patient, processed using different knives, would provide evidence that STAS is an in vivo phenomenon contributing to locoregional recurrence. We retrospectively identified patients with NSCLC (9 adenocarcinoma, 1 squamous cell carcinoma) who underwent LR, had STAS in the LR specimen, and underwent additional resection (lobectomy or LR). The LR and additional resection specimens from each patient were processed at different times using different tissue-processing knives. All specimens were analyzed for STAS. All 10 patients underwent LR with negative margins (R0). All additional resection specimens had STAS: 8 patients had STAS clusters in their completion lobectomy specimens, and 2 had STAS in their additional LR specimens. In 2 patients, STAS was found in the completion lobectomy specimen only after extensive sampling (>10 sections) from the staple line adjacent to the initial LR. The presence of STAS in both the LR and the additional resection specimen processed using different knives supports the concept that STAS is an in vivo phenomenon, rather than an artifact from tissue processing. This observation indicates that occult STAS tumor cells can be present in the lung tissue of the remaining unresected lobe after LR and supports the concept that STAS is a contributing factor for locoregional recurrence following LR.

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma

While next-generation sequencing (NGS) is used to guide therapy in patients with metastatic lung adenocarcinoma (LUAD), use of NGS to determine pathologic LN metastasis prior to surgery has not been assessed. To bridge this knowledge gap, we performed NGS using MSK-IMPACT in 426 treatment-naive patients with clinical N2-negative LUAD. A multivariable logistic regression model that considered preoperative clinical and genomic variables was constructed. Most patients had cN0 disease (85%) with pN0, pN1, and pN2 rates of 80%, 11%, and 9%, respectively. Genes altered at higher rates in pN-positive than in pN-negative tumors were STK11 (p = 0.024), SMARCA4 (p = 0.006), and SMAD4 (p = 0.011). Fraction of genome altered (p = 0.037), copy number amplifications (p = 0.001), and whole-genome doubling (p = 0.028) were higher in pN-positive tumors. Multivariable analysis revealed solid tumor morphology, tumor SUVmax, clinical stage, SMARCA4 and SMAD4 alterations were independently associated with pathologic LN metastasis. Incorporation of clinical and tumor genomic features can identify patients at risk of pathologic LN metastasis; this may guide therapy decisions before surgical resection.

Predictive Value of Folate Receptor-Positive Circulating Tumor Cells for the Preoperative Diagnosis of Lymph Node Metastasis in Patients with Lung Adenocarcinoma

Noninvasive assessments of the risk of lymph node metastasis (LNM) in patients with lung adenocarcinoma (LAD) are of great value for selecting individualized treatment options. However, the diagnostic accuracies of different preoperative LN evaluation methods in routine clinical practice are not satisfactory. Here, an assessment to detect folate receptor (FR)-positive circulating tumor cells (CTCs) based on ligand-targeted enzyme-linked polymerization is established. FR-positive CTCs have the potential to improve the specificity and sensitivity of diagnosing LNM in lung cancer patients. The addition of CTC level improved the diagnostic efficiency of the initial prediction model that comprises other clinical parameters. A nomogram for predicting preoperative LNM is established, which showed good prediction and calibration capacities and achieved an average area under the curve of 0.786. Good correlations are observed between the CTC level and nodal classifications, such as the number of positive LNs and the ratio of the number of positive LNs to removed LNs (LN ratio or LNR). The ligand-targeted enzyme-linked polymerization-assisted assessment of CTCs enables noninvasive detection and has a useful predictive value for the preoperative diagnosis of LNM in patients with LAD.

Integration of clinicopathological and mutational data offers insight into lung cancer with tumor spread through air spaces

Background

Tumor spread through air spaces (STAS) was defined as a unique tumor invasion pattern in adenocarcinoma (ADC) by The World Health Organization Classification of Lung Tumors in 2015. Since then, STAS had been shown to be associated with local recurrence and poor survival results, as the typical signature and potential mechanisms of STAS remained unclear. Our objectives were to comprehensively demonstrate the clinicopathological and genetic signatures in STAS-positive lung cancer patients.

Methods

The clinicopathological and gene alteration characteristics of 878 STAS-positive lung cancer patients were presented. Associations between parameters were evaluated using the Chi-square test, Fisher’s exact test, and logistic regression. The capture-based targeted next generation sequencing (NGS) with a platform of 68 lung cancer-related genes was conducted in 139 cases, and the mutational spectrum was summarized.

Results

STAS was identified in 391 female and 481 male patients, of which ADC accounted for the majority of cases (92.6%). The concomitant solid or micropapillary subtype was observed in 92.12% patients with ADC. Poorly differentiated histological subtypes were more frequent and negatively correlated with tumor size in smaller tumor cases (P=0.036, Pearson’s R=−0.075). Furthermore, in the subgroup of nodules within 3 cm, the distribution of the solid and micropapillary subtypes were significantly frequent in lymph node-positive patients (P<0.001). Tumor protein p53 (TP53) alterations were more frequent in smoking patients (27.6%, P=0.007), human epidermal growth factor receptor 2 (HER2) alterations were more common in female (10.8%, P=0.025), while Kirsten rat sarcoma viral oncogene (KRAS) (20.3%, P=0.024) and TP53 (45.9%, P=0.003) were more prevalent in males.

Conclusions

Poorly differentiated histological subtypes likely played a crucial role in promoting the invasiveness of STAS, especially in small tumor-size cases. Epidermal growth factor receptor (EGFR), TP53, KARS, anaplastic lymphoma kinase (ALK), and ROS proto-oncogene 1 (ROS1) were the five most frequent alterations in STAS-positive ADC.

Micropapillary Predominance Is a Risk Factor for Brain Metastasis in Resected Lung Adenocarcinoma

BACKGROUND

Histologic subtyping offers some prognostic value in lung adenocarcinoma. We thus hypothesized that histologic subtypes may be useful for risk stratification of brain metastasis (BM). In this study, we aimed to investigate the impact of histologic subtypes on the risk for BM in patients with resected lung adenocarcinoma.

PATIENTS AND METHODS

Of 1099 consecutive patients who had undergone curative-intent surgery (2000-2014), 448 patients who had undergone complete resection for lung adenocarcinoma were included in this study. Correlated clinical variables and BM-free survival were analyzed.

RESULTS

Micropapillary predominance was significantly associated with higher risk of BM after complete resection in univariate analyses (P < .001). In addition, multivariate analyses showed that micropapillary predominance was an independent risk factor for BM (hazard ratio = 2.727; 95% confidence interval, 1.260-5.900; P = .011), along with younger age and advanced pathologic stage. Unlike the other subtypes, an increase in the percentage of the micropapillary subtype was positively correlated with an increase in BM frequency. Patients with micropapillary adenocarcinoma showed significantly poorer brain metastasis-free survival compared with those with non-micropapillary adenocarcinoma (3 years, 78.2% vs. 95.6%; 5 years, 67.3% vs. 94.3%; P < .001).

CONCLUSION

The current study demonstrated a significant correlation between micropapillary subtype and higher risk of BM in patients with resected lung adenocarcinoma. This routine histologic evaluation of resected adenocarcinoma may provide useful information for the clinician when considering postoperative management in patients with lung adenocarcinoma. Histologic subtyping offer some prognostic value in lung adenocarcinoma. Because brain metastasis is critical and often refractory to systemic chemotherapy, early detection is clinically important to achieve effective local treatment. We retrospectively analyzed the association between histologic subtypes and occurrence of brain metastasis and found a significant association between micropapillary predominance and higher risk for brain metastasis. Our findings may be relevant when considering postoperative management.

Gross Specimen Handling Procedures Do Not Impact the Occurrence of Spread Through Air Spaces (STAS) in Lung Cancer

Spread Through Air Spaces (STAS) is a form of invasion characterized by neoplastic cell dissemination in the lung parenchyma surrounding the outer edge of the tumor. Its possible artifactual origin is widely debated in the literature. The aim of this study is to investigate the potential impact of gross sampling procedures in causing STAS. A prospective series of 51 surgical lung specimens was collected (35 adenocarcinomas, 68.6%; 13 squamous cell carcinomas, 25.5%; 2 large-cell neuroendocrine carcinomas, 3.9%; 1 atypical carcinoid, 2%). The fresh tissue was sectioned with a new and clean blade for each cut, to obtain a tissue slice comprising the upper lung parenchyma, the tumor, and the lower parenchyma. This slice was cut in half and separately processed. The same procedure was repeated in the residual (specular) specimen after formalin fixation. STAS was identified in 33/51 (64.7%) cases, the predominant pattern being cluster formation (29 cases, 87.9%), the remaining 4 cases having single-cell invasion. Comparing STAS detection in upper and lower lung parenchyma areas (ie, before and after the blade crossed the tumor), no significant preferential STAS distribution was observed, indeed being almost overlapping (60.6% and 63.6% for fresh and 61.3% and 65.6% for fixed tissues, respectively). There was no difference between STAS occurrence in freshly cut and fixed corresponding samples. These findings indicate that STAS is not a pathologist-related artifactual event because of knife transportation of tumor cells during gross specimen handling and support the notion that it is a phenomenon preexisting to surgical tissue processing.

Lymphatic drainage of lung cancer follows an intersegmental pathway within the visceral pleura

OBJECTIVES

Lung cancer tumors are known to be highly lymphophilic. There are two different pattern of lymphatic drainage of the lung: one peribronchial lymphatic pathway, and another one within the visceral pleura which appears to be more intersegmental than the peribronchial pathway. We aimed to assess the prevalence of an intersegmental pathway in the lymphatic drainage of lung tumors within the visceral pleura and determine potential influential factors.

METHODS

In this prospective study, we included all patients for whom a major pulmonary resection (lobar) was indicated and performed for suspected or proven lung cancer. An immediate ex-vivo evaluation of the surgical specimen after resection was conducted by trans-pleural injection of blue dye within the tumor. The pathways followed by the lymphatic vessels under the visceral pleura were assessed to define the occurrence of an intersegmental pathway, which was defined by the presence of blue dye within the lymphatic vessel crossing to a neighboring pulmonary segment, distinct from the tumorous segment.

RESULTS

Fifty-three patients met the inclusion criteria and were assessed over a three-year period. Lymphatic drainage within the visceral pleura followed an intersegmental pathway in 35 of 53 patients (66 %). When the lymphatic drainage of the tumor was intersegmental, it drained in a single other segment in 21/35 cases and two or more in 14/35 cases. Logistic regression with multivariate analysis showed a peripheral location of the tumor to be a risk factor for the intersegmental pathway of visceral pleura lymphatic drainage (OR = 0.87 [079-0.95], p = 0.003).

CONCLUSION

These results confirm that lymphatic drainage of lung cancer in the visceral pleura appears to largely follow an intersegmental pathway, especially when the tumor is peripheral, close to the visceral pleura.

Clinical impact and utility of positron emission tomography on occult lymph node metastasis and survival: radical surgery for stage I lung cancer

OBJECTIVE

The surgical result of early-staged lung cancer is not satisfactory due to unexpected postoperative lymph node metastasis and recurrence. This study aimed to investigate which preoperative factors-including the standard uptake value max (SUV_max) of positron emission tomography-could predict occult lymph node metastasis and survival.

METHODS

We retrospectively analyzed data from 598 patients with clinical stage I lung cancer who underwent surgery, and examined their preoperative clinical characteristics.

RESULTS

A total of 1586 patients had surgery for primary lung cancer between 2006 and 2019; 598 patients with clinical stage I lung cancer were the study inclusion; occult lymph node metastasis was detected in 102 (17.1%). Univariable and multivariable analyses showed that SUV_max ≥ 3 (P < 0.001), clinical invasive tumor size ≥ 2 cm (P = 0.009), and carcinoembryonic antigen > 5 (P = 0.03) were associated with significant risk factors rated (%) for occult lymph node metastasis, as follows: high-risk group (three factors), moderate-risk group (two factors) and low-risk group (one factor or none) corresponding to 32.2 (28/87), 22.8 (41/180) and 7.3 (19/262), respectively (P < 0.001). The 5-year overall survival rates (%) of patients without lymph node metastasis holding SUV_max 6 or over were as poor as those of patients with lymph node metastasis (72.0% vs 64.1%; P = 0.56).

CONCLUSIONS

We might consider wedge resection or segmentectomy, omitting lymphadenectomy, for the low-risk group; adjuvant therapy is indicated for patients without lymph node metastasis having SUV_max 6 or over.

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.