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

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.

Senescence cell signature associated with poor prognosis, epithelial-mesenchymal transition, solid histology, and spread through air spaces in lung adenocarcinoma

Cellular senescence is involved in critical processes in tumor progression. Despite this potential relationship, the relationship between tumor cell senescence, prognostic significance, spread through air spaces (STAS), and tumor histology has not been investigated in lung adenocarcinoma (LUAD). We used the LUAD PanCancer Atlas dataset to assess senescence cell signature (SCS) based on the SenMayo gene set. We examined the relationship between SCS, prognostic significance, STAS, and tumor histology. This relationship was confirmed in independent LUAD datasets by validation using immunohistochemical senescence markers. In the LUAD PanCancer Atlas dataset, patients with high SCS expression had a higher prevalence of solid histology and STAS patterns than those with low SCS expression. In the independent LUAD datasets, high p21 expression and low HMGB1 expression were correlated with solid histology or STAS patterns. SCS level was also independent prognostic factor in four different LUAD datasets. The HMGB1 expression was an independent prognostic factor in the independent LUAD dataset in multivariate analysis. The expression of p21 and the presence of solid histology were linked to the epithelial-mesenchymal transition (EMT) phenotype. In LUAD cell lines, inducing senescence with a DNA-damaging agent led to an increase in EMT marker expression. Our findings suggest a strong link between senescence, EMT, and solid histology, offering valuable insight into how cancer cell senescence may promote tumor progression through particular pathways.

Could less be enough: sublobar resection vs lobectomy for clinical stage IA non-small cell lung cancer patients with visceral pleural invasion or spread through air spaces

BACKGROUND

While recent randomized controlled trials have demonstrated that sublobar resection (SLR) is non-inferior to lobectomy, the comparative efficacy of these procedures remains uncertain for early-stage non-small cell lung cancer (NSCLC; ≤3 cm) exhibiting invasive features postoperatively, such as visceral pleural invasion (VPI) or spread through air spaces (STAS).

MATERIALS AND METHODS

To identify eligible studies, a comprehensive search of PubMed, Embase, MEDLINE, the Cochrane Library, and Web of Science was conducted through 25 July 2024. Studies were screened according to predefined criteria in accordance with PRISMA guidelines. The primary endpoints were 5-year overall survival (OS) and recurrence-free survival (RFS). Hazard ratios (HR) and 95% confidence intervals (CI) were used to perform a meta-analysis.

RESULTS

The final analysis included 14 retrospective studies and 1 randomized controlled trial, encompassing a total of 8054 patients with NSCLC (tumors ≤3 cm) exhibiting VPI or STAS. The meta-analysis revealed that SLR was associated with impaired 5-year OS (HR: 1.25; 95% CI: 1.10-1.41) and slightly inferior RFS (HR: 1.25; 95% CI: 0.99-1.58) compared to lobectomy for pT2a (VPI) NSCLC patients with tumor ≤3 cm. Similarly, SLR was associated with significantly worse 5-year OS (HR: 2.58; 95% CI: 1.92-3.45) and 5-year RFS (HR: 2.42; 95% CI: 1.69-3.46) compared to lobectomy for stage IA NSCLC patients with STAS. Subgroup analysis revealed that statistically significant differences in 5-year OS (HR: 1.13; 95% CI: 0.92-1.38) and 5-year RFS (HR: 0.87; 95% CI: 0.56-1.36) were not observed between the SLR and lobectomy groups for pT2a (VPI) NSCLC patients with tumor ≤2 cm. Additionally, no statistically significant survival difference was observed between the segmentectomy and lobectomy groups for NSCLC patients (≤3 cm) with VPI (5-year OS: HR: 1.16; 95% CI: 0.89-1.52; 5-year RFS: HR: 1.07; 95% CI: 0.88-1.30) or STAS (5-year OS: HR: 3.88; 95% CI: 0.82-18.31; 5-year RFS: HR: 1.64; 95% CI: 0.70-3.80).

CONCLUSIONS

For early-stage (≤3 cm) NSCLC with VPI or STAS, SLR was associated with worse survival outcomes compared to lobectomy. However, segmentectomy achieved survival outcomes comparable to those of lobectomy. For pT2a (VPI) NSCLC patients with tumor ≤2 cm, the differences in survival outcomes between SLR and lobectomy were not statistically significant.

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

BACKGROUND

This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.

METHODS

We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).

RESULTS

The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).

CONCLUSIONS

The prediction model based on radiomics and genomics features has a good prediction performance for STAS.

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.

The International Association for the Study of Lung Cancer (IASLC) Staging Project for Lung Cancer: Recommendation to Introduce Spread Through Air Spaces as a Histologic Descriptor in the Ninth Edition of the TNM Classification of Lung Cancer. Analysis of 4061 Pathologic Stage I NSCLC

INTRODUCTION

Spread through air spaces (STAS) consists of lung cancer tumor cells that are identified beyond the edge of the main tumor in the surrounding alveolar parenchyma. It has been reported by meta-analyses to be an independent prognostic factor in the major histologic types of lung cancer, but its role in lung cancer staging is not established.

METHODS

To assess the clinical importance of STAS in lung cancer staging, we evaluated 4061 surgically resected pathologic stage I R0 NSCLC collected from around the world in the International Association for the Study of Lung Cancer database. We focused on whether STAS could be a useful additional histologic descriptor to supplement the existing ones of visceral pleural invasion (VPI) and lymphovascular invasion (LVI).

RESULTS

STAS was found in 930 of 4061 of the pathologic stage I NSCLC (22.9%). Patients with tumors exhibiting STAS had a significantly worse recurrence-free and overall survival in both univariate and multivariable analyses involving cohorts consisting of all NSCLC, specific histologic types (adenocarcinoma and other NSCLC), and extent of resection (lobar and sublobar). Interestingly, STAS was independent of VPI in all of these analyses.

CONCLUSIONS

These data support our recommendation to include STAS as a histologic descriptor for the Ninth Edition of the TNM Classification of Lung Cancer. Hopefully, gathering these data in the coming years will facilitate a thorough analysis to better understand the relative impact of STAS, LVI, and VPI on lung cancer staging for the Tenth Edition TNM Stage Classification.

Pro: "Is Spread Through Air Spaces an In Vivo Phenomenon or an Inducible Artifact?"

In this PRO-CON debate, you will read very different perspectives about a simple question regarding an observation under the microscope: What is the significance of tumor cells in the air spaces of the lung parenchyma beyond the tumor edge of a resected lung cancer? An important underlying question is whether this entire PRO-CON debate is a mere academic exercise or whether spread through air spaces (STAS), as currently defined, describes a clinically useful phenomenon. The journey of STAS began with a complete paradigm shift to reverse the thinking that all air space tumor cells beyond the edge of lung cancers are an artifact. This led to a new concept where STAS could be separated from artifacts with a definition that has proven to be clinically useful. As with any major change in thinking, it is understandable that there would be some disagreement with this paradigm shift. Nevertheless, after a decade since it was described, many pathologists and clinicians around the world have found STAS to provide important information about the behavior of lung cancer. Numerous PRO-STAS articles supporting the usefulness of STAS have been published with clinical data on many thousands of patients from numerous institutions all over the world. In contrast, for the CON-STAS articles, widespread international representation and data are limited. It is now difficult to ignore the numerous reports and is reasonable to consider how to use the presence of STAS in clinical decisions. Hopefully, this PRO-CON debate will further stimulate clinical and scientific investigations aimed at a better understanding of STAS.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BACKGROUND

Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy.

METHODS

We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3.

FINDINGS

A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001).

INTERPRETATION

IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

2021 WHO Classification of Lung Cancer: Molecular Biology Research and Radiologic-Pathologic Correlation

The 2021 World Health Organization (WHO) classification system for thoracic tumors (including lung cancer) contains several updates to the 2015 edition. Revisions for lung cancer include a new grading system for invasive nonmucinous adenocarcinoma that better reflects prognosis, reorganization of squamous cell carcinomas and neuroendocrine neoplasms, and description of some new entities. Moreover, remarkable advancements in our knowledge of genetic mutations and targeted therapies have led to a much greater emphasis on genetic testing than that in 2015. In 2015, guidelines recommended evaluation of only two driver mutations, ie, epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) fusions, in patients with nonsquamous non-small cell lung cancer. The 2021 guidelines recommend testing for numerous additional gene mutations for which targeted therapies are now available including ROS1, RET, NTRK1-3, KRAS, BRAF, and MET. The correlation of imaging features and genetic mutations is being studied. Testing for the immune biomarker programmed death ligand 1 is now recommended before starting first-line therapy in patients with metastatic non-small cell lung cancer. Because 70% of lung cancers are unresectable at patient presentation, diagnosis of lung cancer is usually based on small diagnostic samples (ie, biopsy specimens) rather than surgical resection specimens. The 2021 version emphasizes differences in the histopathologic interpretation of small diagnostic samples and resection specimens. Radiologists play a key role not only in evaluation of tumor and metastatic disease but also in identification of optimal biopsy targets. ©RSNA, 2024 Test Your Knowledge questions in the supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

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.

Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface

BACKGROUND

The preoperative determination of visceral pleural invasion (VPI) in patients with malignant solitary pulmonary nodules (SPNs) is essential for determining the surgical range and selecting adjuvant chemotherapy.

OBJECTIVES

This study aimed to systematically investigate risk factors of VPI in patients with SPN and construct a preoperative predictive model for such patients.

DESIGN

This is a retrospective study. The clinical, radiological, and pathological characteristics of study subjects were reviewed, and the groups with and without VPI were compared.

METHODS

Multivariate logistic analysis was utilized to identify independent risk factors for VPI. Moreover, a predictive nomogram was constructed to assess the likelihood of VPI occurrence.

RESULTS

Of the 364 enrolled cases, SPNs adjacent to the pleura with VPI were found in 110 (30.2%) patients. By incorporating four preoperative variables, including tumor diameter (>2 cm), maximum computed tomography value (>200 Hu), air bronchogram sign, and age, a preoperative predictive nomogram was constructed. The nomogram demonstrated good discriminative ability, with a C-index of 0.736 (95% CI (0.662-0.790)). Furthermore, our data indicated that the air bronchogram sign (odd ratio (OR) 1.81, 95% CI (0.99-3.89), p = 0.048), a maximum diameter >2 cm (OR 24.48, 95% CI (8.43-71.07), p < 0.001), pathological type (OR 5.01, 95% CI (2.61-9.64), p < 0.001), and Ki-67 >30% (OR 2.95, 95% CI (1.40-6.21), p = 0.004) were overall independent risk factors for VPI.

CONCLUSION

This study investigated the risk factors for VPI in malignant SPNs touching the pleural surface. Additionally, a nomogram was developed to predict the likelihood of VPI in such patients, facilitating informed decision-making regarding surgical approaches and treatment protocols.

High-risk characteristics of pathological stage I lung adenocarcinoma after resection: patients for whom adjuvant chemotherapy should be performed

BACKGROUND

The objective of the present study was to identify patients with pathologic stage I lung adenocarcinoma (LUAD) who are at high risk of recurrence and assess the efficacy of adjuvant chemotherapy (ACT) in these individuals.

METHODS

A retrospective study was conducted on 1504 patients with pathologic stage I LUAD who underwent surgical resection at Shanghai Pulmonary Hospital and Sun Yat-sen University Cancer Center. Cox proportional hazard regression analyses were performed to identify indicators associated with a high risk of recurrence, while the Kaplan-Meier method and Log-rank test were employed to compare recurrence-free survival (RFS) and overall survival (OS) between patients with ACT and those without it.

RESULTS

Four independent indicators, including age (≥62 years), visceral pleural invasion (VPI), predominant pattern (micropapillary/solid), and lymphovascular invasion (LVI), were identified to be significantly related with RFS. Subsequently, patients were classified into high-risk and low-risk groups by LVI, VPI, and predominant pattern. The administration of ACT significantly increased both RFS (P < 0.001) and OS (P = 0.03) in the high-risk group (n = 250). Conversely, no significant difference was observed in either RFS (P = 0.45) or OS (P = 0.063) between ACT and non-ACT patients in the low-risk group (n = 1254).

CONCLUSIONS

Postoperative patients with stage I LUAD with factors such as LVI, VPI, and micropapillary/solid predominant pattern may benefit from ACT.

The Value of Micro-CT in the Diagnosis of Lung Carcinoma: A Radio-Histopathological Perspective

Micro-computed tomography (micro-CT) is a relatively new imaging modality and the three-dimensional (3D) images obtained via micro-CT allow researchers to collect both quantitative and qualitative information on various types of samples. Micro-CT could potentially be used to examine human diseases and several studies have been published on this topic in the last decade. In this study, the potential uses of micro-CT in understanding and evaluating lung carcinoma and the relevant studies conducted on lung and other tumors are summarized. Currently, the resolution of benchtop laboratory micro-CT units has not reached the levels that can be obtained with light microscopy, and it is not possible to detect the histopathological features (e.g., tumor type, adenocarcinoma pattern, spread through air spaces) required for lung cancer management. However, its ability to provide 3D images in any plane of section, without disturbing the integrity of the specimen, suggests that it can be used as an auxiliary technique, especially in surgical margin examination, the evaluation of tumor invasion in the entire specimen, and calculation of primary and metastatic tumor volume. Along with future developments in micro-CT technology, it can be expected that the image resolution will gradually improve, the examination time will decrease, and the relevant software will be more user friendly. As a result of these developments, micro-CT may enter pathology laboratories as an auxiliary method in the pathological evaluation of lung tumors. However, the safety, performance, and cost effectiveness of micro-CT in the areas of possible clinical application should be investigated. If micro-CT passes all these tests, it may lead to the convergence of radiology and pathology applications performed independently in separate units today, and the birth of a new type of diagnostician who has equal knowledge of the histological and radiological features of tumors.

CT-based radiomics model to predict spread through air space in resectable lung cancer

BACKGROUND

Spread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS.

METHOD

There were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10-fold cross-validation.

RESULTS

The incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non-STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10-fold cross-validation had an AUC of 0.834.

CONCLUSION

CT-based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.

A Novel Systematic Oxidative Stress Score Predicts the Survival of Patients with Early-Stage Lung Adenocarcinoma

This study aimed to construct an effective nomogram based on the clinical and oxidative stress-related characteristics to predict the prognosis of stage I lung adenocarcinoma (LUAD). A retrospective study was performed on 955 eligible patients with stage I LUAD after surgery at our hospital. The relationship between systematic-oxidative-stress biomarkers and the prognosis was analyzed. The systematic oxidative stress score (SOS) was established based on three biochemical indicators, including serum creatinine (CRE), lactate dehydrogenase (LDH), and uric acid (UA). SOS was an independent prognostic factor for stage I LUADs, and the nomogram based on SOS and clinical characteristics could accurately predict the prognosis of these patients. The nomogram had a high concordance index (C-index) (0.684, 95% CI, 0.656-0.712), and the calibration curves for recurrence-free survival (RFS) probabilities showed a strong agreement between the nomogram prediction and actual observation. Additionally, the patients were divided into two groups according to the cut-off value of risk points based on the nomogram, and a significant difference in RFS was observed between the high-risk and low-risk groups (p < 0.0001). SOS is an independent prognostic indicator for stage I LUAD. These things considered, the constructed nomogram based on SOS could accurately predict the survival of those patients.

A clinical risk model for assessing the survival of patients with stage IA-IIA non-small cell lung cancer after surgery

BACKGROUND

The survival of patients with stage IA-IIA non-small cell lung cancer (NSCLC) after surgery is heterogeneous. This study aimed to construct a prognostic risk model to predict the overall survival (OS) of these patients.

METHODS

Data from patients (n=9,914) from the Surveillance Epidemiology and End Results (SEER) database were analyzed. The cases were randomly divided into the training and the validation groups. Patients from the Shanghai Pulmonary Hospital (n=270) were also included as an external cohort. Independent significant factors affecting survival in the training cohort were used to construct a nomogram. The precision was evaluated using the concordance index (C-index) and calibration plots. The X-tile software was used to confirm the optimal cut-off value to classify the patients.

RESULTS

Sex, age at diagnosis, tumor size, visceral pleura invasion (VPI), tumor grade, and the number of examined lymph nodes were deemed independent prognostic factors and were selected to establish the nomogram. The C-indices of the nomogram for predicting OS were 0.671 [95% confidence interval (CI): 0.653-0.689] in the training group, and 0.668 (95% CI: 0.650-0.687) and 0.707 (95% CI: 0.651-0.763) in the validation and the testing groups, respectively. The cut-off value of risk points was 106.0, which stratified the patients into high-risk and low-risk groups. The high-risk patients had shorter 5-year OS than low-risk patients (P<0.001).

CONCLUSIONS

The established nomogram could evaluate the survival in patients with stage IA-IIA NSCLC after surgery and may provide prognostic information for clinicians to make decisions in the management of adjuvant therapy.