Occult mediastinal lymph node metastasis in NSCLC patients diagnosed as clinical N0-1 by preoperative integrated FDG-PET/CT and CT: Risk factors, pattern, and histopathological study

MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer

Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This study collected clinicopathological variables from the pulmonary nodule and lung cancer database of the First Affiliated Hospital of Chongqing Medical University, where patients with early-stage (cT1) solitary lung cancer were evaluated from 2018.11 to 2022.10. The random forest model and Shapley Additive Explanations (SHAP) were used to investigate the importance of clinical features in the feature selection part. Random Forest, Gradient Boosting, and AdaBoost classifiers were applied to build the final model, and the predictive discrimination of each model was compared based on the receiver operating characteristics (ROC) curve and precision and recall curve. With the evaluation of feature importance, 9 features were used to construct the prediction model finally. The Random Forest model yielded an average precision of 0.93 with an area under the curve (AUC) of 0.92 (95% CI: 0.88-0.94) compared with the Gradient Boosting and AdaBoost classifiers in the internal validation dataset, yielding an average precision of 0.87 and 0.91 with AUCs of 0.87 (95% CI: 0.84-0.93) and 0.90 (95% CI: 0.86-0.92), respectively. In addition, the Random Forest classifier performed best in 5 other 5 diagnostic indices. Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user-friendly tool assisting in the metastasis prediction of cT1 solid lung cancer.

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.

Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data

PURPOSE

Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients.

METHODS AND MATERIALS

Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers.

RESULTS

A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group.

CONCLUSIONS

The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Machine learning predictive models and risk factors for lymph node metastasis in non-small cell lung cancer

BACKGROUND

The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients.

METHODS

Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform.

RESULTS

We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform ( https://wubopredict.shinyapps.io/dynnomapp/ ).

CONCLUSION

The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Survival benefit of adjuvant chemotherapy after resection of Stage I lung adenocarcinoma containing micropapillary components

BACKGROUND

The usefulness of postoperative adjuvant chemotherapy (ACT) for patients with stage I lung adenocarcinoma with micropapillary (MIP) components remains unclear. We analyzed whether postoperative ACT could reduce recurrence in patients with stage I lung adenocarcinoma with MIP components, thereby improving their overall survival (OS) and disease-free survival (DFS).

METHODS

Data for patients with pathologically confirmed stage I lung adenocarcinoma with MIP components from January 2012 to December 2018 were retrospectively analyzed. OS and DFS were analyzed in groups and subgroups.

RESULTS

Overall, 259 patients were enrolled. Patients who received ACT in stage IA showed significantly better survival than did those with no-adjuvant chemotherapy (NACT); (5-year OS 89.4% vs. 73.6%, p < 0.001; 5-year DFS 87.2% vs. 66.0%, p = 0.008). A difference was also observed for in-stage IB patients (5-year OS 82.0% vs. 51.8%, p = 0.001; 5-year DFS 76.0% vs. 41.11 %, p = 0.004). In subgroup analysis based on the proportion of MIP components, patients with 1%-5% MIP components had a significantly better prognosis in the ACT group than in the NACT group (5-year OS 82.4% vs. 66.0%, p = 0.005; 5-year DFS 76.5% vs. 49.1%, p = 0.032). A similar difference was observed for patients with MIP ≥5% (5-year OS 80.7% vs. 47.8%, p = 0.009; 5-year DFS 73.11% vs. 43.5%, p = 0.007).

CONCLUSION

Among patients with stage I lung adenocarcinoma with MIP components, those who received ACT showed significant survival benefits compared to those without ACT. Patients with lung adenocarcinoma with MIP components could benefit from ACT when the MIP was ≥1%.

The role of adenocarcinoma subtypes and immunohistochemistry in predicting lymph node metastasis in early invasive lung adenocarcinoma

BACKGROUND

Identifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma.

METHODS

In this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer-Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

RESULTS

The overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779-0.908) in the training cohort and 0.838 (95% CI: 0.748-0.927) in the validation cohort. the P-value of the Hosmer-Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration.

CONCLUSIONS

The column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.

Development of a nomogram for predicting survival in clinical T1N0M1 lung adenocarcinoma: a population-based study

OBJECTIVE

This study aimed to establish a prognostic model for clinical T1N0M1 (cT1N0M1) lung adenocarcinoma patients to evaluate the prognosis of patients in terms of overall survival (OS) rate and cancer-specific survival (CSS) rate.

METHODS

Data of patients with metastatic lung adenocarcinoma from 2010 to 2016 were collected from the Surveillance, Epidemiology and End Results database. Multivariate Cox regression analysis was conducted to identify relevant prognostic factors and used to develop nomograms. The receiver operating characteristic (ROC) curve and calibration curve are used to evaluate the predictive ability of the nomograms.

RESULTS

A total of 45610 patients were finally included in this study. The OS and CSS nomograms were constructed by same clinical indicators such as age (<60 years or ≥60 years), sex (female or male), race (white, black, or others), surgery, radiation, chemotherapy, and the number of metastatic sites, based on the results of statistical Cox analysis. From the perspective of OS and CSS, surgery contributed the most to the prognosis. The ROC curve analysis showed that the survival nomograms could accurately predict OS and CSS. According to the points obtained from the nomograms, survival was estimated by the Kaplan-Meier method, then cT1N0M1 patients were divided into three groups: low-risk group, intermediate-risk group, and high-risk group, and the OS ( P  < 0.001) and CSS ( P  < 0.001) were significantly different among the three groups.

CONCLUSION

The nomograms and risk stratification model provide a convenient and reliable tool for individualized evaluation and clinical decision-making of patients with cT1N0M1 lung adenocarcinoma.

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.

Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([¹⁸F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [¹⁸F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.

Type of lymphadenectomy does not influence survival in pIa NSCLC patients who underwent VATS lobectomy: Results from the national VATS group database

INTRODUCTION

Stage Ia presents an optimal survival rate after surgical resection, but the type of lymphadenectomy to use in these patients is still debated. The aim of this study is evaluate if one type of lymphadenectomy adopted influences survival in patients who underwent VATS lobectomy for stage Ia NSCLC.

METHODS

Clinical and pathological data from pIa patients in the prospective VATS Italian nationwide registry were reviewed and analysed. Patients and tumour characteristics,type of lymphadenectomy (sampling or radical nodal dissection,MRLD), were collected and correlated to Overall Survival(OS) and Disease free Survival(DFS). The Kaplan-Meier product-limit method was used to estimate OS and DFS and the log-rank test was adopted to evaluate the differences between groups. A propensity match was performed to reduce bias due to the retrospective study design.

RESULTS

The final analysis was conducted on 2039 patients, 179 died during follow-up,recurrence rate was 13%. MRLD was performed in 1287(63.1%)patients. The univariable analysis identified as favourable prognostic factors for OS the female sex(p = 0.023), low ECOG-score(0.008),low SUVmax(p < 0.001), GGO appearance(p < 0.001), pT < 2 cm(p = 0.002) and low tumour grading(p = 0.002). The multivariable analysis confirmed as independent prognostic factors low ECOG-score(p = 0.012), low SUVmax(p < 0.001) and low tumour grading(p < 0.001). Analysing survival in patients with solid/sub-solid nodules and after propensity score matching for pTdimension and number of N2 resected lymphnodes, no OS differences were present comparing sampling vs MRLD.

CONCLUSION

Survival in pIa patients seems to be determined by patient and tumour characteristics such as performance status,grading and SUVmax. Type of lymphadnectomy did not seem to be correlated with OS in these patients.

Retention index of FDG-PET/CT SUVmax of the primary tumor in non-small cell lung cancer as a predictor of lymph node metastasis: a retrospective study

Background

Accurate staging of non-small cell lung cancer is key in treatment planning and prediction of prognosis. We investigated the correlation between the maximum standardized uptake value (SUVmax) retention index (RI) of the primary tumor and lymph node metastasis in non-small cell lung carcinoma. We also evaluated the tendencies according to the histological types.

Methods

We retrospectively evaluated 218 non-small cell lung cancer (NSCLC) tumors from 217 patients who underwent preoperative fluorodeoxyglucose-positron emission tomography/computed tomography (PET/CT) followed by lung surgery and lymph node resection between July 2015 and August 2020. All primary tumors were calculated as the SUVmax at 50 min (SUVmax_early [SUVmax_e]) and 120 min (SUVmax_delayed [SUVmax_d]), and RI. The clinicopathological factors of interest were compared based on lymph node metastasis status and NSCLC histopathological subtype.

Results

The median SUVmax_e and SUVmax_d of the primary tumors were 3.3 and 4.2, respectively, and the median RI was 0.25. The RI was significantly higher in the pN(+) (n = 44) group (0.30) compared to the pN0 (n = 174) group (0.24) (p = 0.01). In patients with adenocarcinoma (n = 145), the RI was also significantly higher in the pN(+) (n = 29) group (0.29) compared to the pN0 (n = 116) group (0.16) (p < 0.01). A high RI of the primary tumor was an independent risk factor for lymph node metastasis, particularly in patients with adenocarcinoma (odds ratio: 12.30, p < 0.05).

Conclusions

The RI of primary NSCLC tumors can help predict lymph node metastases, particularly in patients with adenocarcinoma.

Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas

OBJECTIVES

The aims of the study were to integrate characteristics of computed tomography (CT), texture, and hematological parameters and to establish predictive models for lymph node (LN) metastasis in lung adenocarcinoma.

METHODS

A total of 207 lung adenocarcinoma cases with confirmed postoperative pathology and preoperative CT scans between February 2017 and April 2019 were included in this retrospective study. All patients were divided into training and 2 validation cohorts chronologically in the ratio of 3:1:1. The χ2 test or Fisher exact test were used for categorical variables. The Shapiro-Wilk test and Mann-Whitney U test were used for continuous variables. Logistic regression and machine learning algorithm models based on CT characteristics, texture, and hematological parameters were used to predict LN metastasis. The performance of the multivariate models was evaluated using a receiver operating characteristic curve; prediction performance was evaluated in the validation cohorts. Decision curve analysis confirmed its clinical utility.

RESULTS

Logistic regression analysis demonstrated that pleural thickening (P = 0.013), percentile 25th (P = 0.033), entropy gray-level co-occurrence matrix 10 (P = 0.019), red blood cell distribution width (P = 0.012), and lymphocyte-to-monocyte ratio (P = 0.049) were independent risk factors associated with LN metastasis. The area under the curve of the predictive model established using the previously mentioned 5 independent risk factors was 0.929 in the receiver operating characteristic analysis. The highest area under the curve was obtained in the training cohort (0.777 using Naive Bayes algorithm).

CONCLUSIONS

Integrative predictive models of CT characteristics, texture, and hematological parameters could predict LN metastasis in lung adenocarcinomas. These findings may provide a reference for clinical decision making.

Clinicopathological and computed tomographic features associated with occult lymph node metastasis in patients with peripheral solid non-small cell lung cancer

OBJECTIVES

To investigate the value of combining clinicopathological characteristics with computed tomographic (CT) features of tumours for predicting occult lymph node metastasis (OLNM) in peripheral solid non-small cell lung cancer (PS-NSCLC).

METHODS

The study included 478 NSCLC clinically N0 (cN0) patients who underwent lobectomy and systemic lymph node dissection from January 2014 to August 2019. Patients were classified into OLNM and negative lymph node metastasis (NLNM) groups. The CT features of non-metastatic and metastatic lymph nodes with a largest short-diameter > 5 mm were compared in the OLNM group. Thereafter, the clinicopathological characteristics and CT morphological features of tumours were compared between both groups. Multivariable logistic regression analysis and receiver-operating characteristic curve were developed.

RESULTS

CT images detected 103 metastatic and 705 non-metastatic lymph nodes, and no significant differences in CT features of lymph nodes were found in all 161 OLNM patients (P > 0.05). For both groups, sex, carcinoembryonic antigen and pathological type differed significantly (all P < 0.05), while tumour size, necrosis, calcification, vascular convergence, pleural involvement, and the shortest interval of tumour-pleura differed significantly on CT images (all P < 0.05). Multivariable logistic regression analysis showed that carcinoembryonic antigen > 5.00 ng/ml, adenocarcinoma, absence of vascular convergence, and pleural involvement of Type II (one linear or cord-like pleural tag or tumour abut to the pleura with a broad base observed on both lung and mediastinal window images) were independent predicting factors of OLNM.

CONCLUSIONS

CT findings of lymph nodes can provide limited value and integrating clinicopathological characteristics with the CT morphological features of tumours is helpful in predicting OLNM in patients with PS-NSCLC.

What Should Thoracic Surgeons Consider during Surgery for Ground-Glass Nodules?: Lymph Node Dissection

Thoracic surgeons need to be aware of several important points regarding intraoperative lymph node dissection during surgery for non-small cell lung cancer with ground-glass opacities. The first point relates to the need for lymph node dissection during sublobar resection. Since even patients undergoing sublobar resection may benefit from lymph node dissection, it should be selectively performed according to adequate indications, which require further study. Second, there seems to be no difference in postoperative morbidity between systematic sampling and systematic dissection, but the survival benefit from systematic dissection remains unclear. The results of randomized controlled trials on this topic are conflicting, and their evidence is jeopardized by a high risk of bias in terms of the study design. Therefore, further randomized controlled trials with a sound design should investigate this issue. Third, more favorable survival outcomes tend to be positively associated with the number of examined lymph nodes. Minimum requirements for the number of examined lymph nodes in non-small cell lung cancer should be defined in the future. Finally, lobe-specific lymph node dissection does not have a negative prognostic impact. It should not be routinely performed, but it can be recommended in selected patients with smaller, less invasive tumors. Results from an ongoing randomized controlled trial on this topic should be awaited.

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.

Semiquantitative assessment of fluorodeoxyglucose uptake in primary tumours on dynamic PET/computed tomography for lymph node metastasis evaluation in patients with lung cancer: a prospective study

OBJECTIVE

To semiquantitatively estimate fluorine-18-fluorodeoxyglucose (FDG) uptake in primary lung cancer cells using dynamic and dual-time-point (DTP) PET/computed tomography (PET/CT) to obtain a diagnostic index for lymph node metastasis.

METHODS

Forty-five patients with lung cancer underwent dynamic and DTP PET/CT examinations. All primary lesions and lymph node metastases were evaluated pathologically. At each time phase, we assessed the maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV) and total lesion glycolysis (TLG) of the primary tumours. We investigated the relationship between semiquantitative index and the presence of lymph node metastasis for each case and for all cases satisfying indications for segmentectomy. In cases with lymph node metastasis, we assessed the SUVmax of pathologically proven metastatic lymph nodes and nonmetastatic lymph nodes in each dynamic phase for evaluating temporal change.

RESULTS

Among 45 patients, 15 had 17 lymph node metastasis. SUVmax, MTV and TLG of primary tumours at each time phase were significantly associated with lymph node metastasis (P < 0.05). In receiver operating characteristic analysis, dynamic second and third phases showed high diagnostic ability for lymph node metastasis. The temporal change in SUVmax in the dynamic phase between primary tumours and metastatic lymph nodes were significantly different (P = 0.065). The temporal change in SUVmax was significantly lower in nonmetastatic lymph nodes than in primary tumours and metastatic lymph nodes (P < 0.0001).

CONCLUSIONS

Semiquantitative assessment of FDG uptake in dynamic second and third phases and the assessment of temporal changes in SUVmax on dynamic PET/CT scans were important predictors in diagnosing lymph node metastasis.

The predictive power of artificial intelligence on mediastinal lymphnode metastasis

OBJECTIVE

The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.

METHODS

We enrolled 301 surgical resections of patients with clinical stage N0-1 lung adenocarcinoma, who received positron emission tomography preoperatively between 2015 and 2019. We randomly assigned the patients into two groups: the training (n = 201) and validation groups (n = 100). The training group was used to obtain basic data for learning by artificial intelligence, whereas the validation group was used to verify the constructed algorithm. We used an automatic machine learning platform, to create artificial intelligence model. For comparison, multivariate analysis was performed in the training group, whereas for calculating and verifying the prediction accuracy rate, significant predicting factors were applied to the validation group.

RESULTS

Of the 301 patients, 41 patients were diagnosed as mediastinal lymph node metastasis. In multivariate analysis, the maximum standardized uptake value was an individual predictive factor. The accuracy rate of artificial intelligence model was 84%, and the specificity was 98% which were higher than those of the maximum standardized uptake value (61% and 57%). However, in terms of sensitivity, artificial intelligence model remarked low at 12%.

CONCLUSIONS

An artificial intelligence-based diagnostic algorithm showed remarkable specificity compared with the maximum standardized uptake value. Although this model is not ready to practical use and the result was preliminary because of poor sensitivity, artificial intelligence could be able to complement the shortcomings of existing diagnostic modalities.

How do we Classify a Central Tumor? Results of a Multidisciplinary Survey from the SEPAR Thoracic Oncology Area

INTRODUCTION

In patients with non-small cell lung cancer (NSCLC) and normal mediastinal imaging tests, centrally located tumors have greater occult mediastinal involvement. Clinical guidelines, therefore, recommend invasive mediastinal staging in this situation. However, definitions of centrality in the different guidelines are inconsistent. The SEPAR Thoracic Oncology area aimed to evaluate the degree of familiarity with various concepts related to tumor site among professionals who see patients with NSCLC in Spain.

METHODS

A questionnaire was distributed to members of Spanish medical societies involved in the management of NSCLC, structured according to the 3 aspects to be evaluated: 1) uniformity in the definition of central tumor location; 2) uniformity in the classification of lesions that extend beyond dividing lines; and 3) ability to delineate lesions in the absence of dividing lines.

RESULTS

A total of 430 participants responded. The most voted definition of centrality was «lesions in contact with hilar structures» (49.7%). The lines most often chosen to delimit the hemitorax were concentric hilar lines (89%). Most participants (92.8%) classified tumors according to the side of the dividing line that contained most of their volume. Overall, 78.6% were able to correctly classify a central lesion in the absence of dividing lines.

CONCLUSIONS

In our survey, the most widely accepted definition of centrality is not one of the proposals specified in the clinical guidelines. The results reflect wide variability in the classification of tumor lesions.

Occult lymph node metastasis in radiologic stage I non-small cell lung cancer: The role of endobronchial ultrasound

RATIONALE

The use of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is currently recommended for staging non-small cell lung cancer (NSCLC) in centrally located tumors, tumors >3 cm, or with radiologic evidence of lymph node (LN) metastasis. Current guidelines do not recommend staging EBUS-TBNA in patients with stage I NSCLC who do not have any of the aforementioned conditions.

OBJECTIVE

We hypothesize that using EBUS-TBNA is useful for detecting occult metastasis in radiologic stage I NSCLC.

METHODS

In this single-center, retrospective study, charts of patients ≥18 years old who underwent staging EBUS-TBNA from January 2005 to May 2019 were reviewed. Only patients with combined positron-emission tomography and computed tomography (PET/CT) scans consistent with radiologic stage I NSCLC were included. Identified variables included: age, gender, personal history of any cancer, smoking history, tumor location, tumor centrality, tumor size, tumor PET activity, histopathologic type of NSCLC, and LN biopsy results. Patients whose LN samples showed a diagnosis other than NSCLC were excluded. The association between LN positivity, and each of the variables was assessed using Pearson's correlation for categorical variables, and logistic regression analysis for continuous variables.

RESULTS

From the 2,892 initially screened patients, 188 were included. Of those, 13 (6.9%; 95% CI, 4%-11%) had a malignancy-positive LN biopsy. The number needed to test (NNT) in order to detect one case of any occult metastasis was 15. Among the included variables, a significant association was found between LN positivity and tumor centrality, with central tumors found in 61.5% of patients with positive LN (n = 8) (p < 0.01). This association stayed significant after adjusting for age, gender, smoking history, tumor size, tumor location, and PET activity (p = 0.015). Among patients with malignancy-positive LN biopsies, five (38.5%; 95% CI, 17.6%-64.6%) were upstaged to N1, and eight (61.5%; 95% CI, 35.4%-82.4%) were upstaged to N2, with NNT of 23 to detect one case of occult N2 metastasis. Subgroup analysis comparing LN-positive patients based on their N stage did not show statistically significant association with any of the variables.

CONCLUSION

Based on our results and along with the existing evidence, EBUS-TBNA should be recommended as part of the routine staging in all patients with radiologic stage I NSCLC.

Validation of the 8th Edition Nodal Staging and Proposal of New Nodal Categories for Future Editions of the TNM Classification of Non-Small Cell Lung Cancer

BACKGROUND

The International Association for the Study of Lung Cancer (IASLC) N classifications, which depend on the location and involvement of the lymph nodes, provide accurate prognoses. This study validated the efficiency of classifications using a single-institution dataset and proposed a modified system based on 5-level N1 node dissection.

METHODS

From January 2005 to December 2014, 1851 patients with completely resected non-small cell lung cancer were reviewed. According to the IASLC recommendations, N1 is further subdivided into N1a (single) and N1b (multiple), N2 is divided into N2a1 (single station without N1), N2a2 (single station with N1), and N2b (multiple station). Additionally, we evaluated dividing N0 into N0a (with level 13/14 examination) and N0b (without level 13/14 examination), and N1 into N1a* (only level 13/14 positive) and N1b* (level 10-12 positive). Overall survival was also compared.

RESULTS

Multivariate analysis showed that the N classifications recommended by the IASLC and those proposed and evaluated by this study could both significantly predict the prognoses of patients (p < 0.001, respectively). There was no significant difference in survival between N1b and N1a (hazard ratio [HR] 1.049, p = 0.83) and N2a1 and N1b (HR 1.314, p = 0.261); however, there were significant differences between N0a and N0b (HR 1.778, p < 0.001) and N1a* and N1b* (HR 2.014, p = 0.019). The survival curve of N1a* overlapped N0b (HR 0.997, p = 0.991), and N2a1 overlapped N1b* (HR 0.842, p = 0.444).

CONCLUSION

More detailed nodal information is required to facilitate future revisions of N staging.

Computed Tomography Radiomics for Residual Positron Emission Tomography-Computed Tomography Uptake in Lymph Nodes after Treatment

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the pathologic complete response of LNs showing residual uptake after neoadjuvant concurrent chemoradiotherapy (NeoCCRT) in patients with non-small cell lung cancer (NSCLC). Two hundred and thirty-seven patients who underwent NeoCCRT for stage IIIa NSCLC were included. Two hundred fifty-two CT radiomics features were extracted from LNs showing remaining positive FDG uptake upon restaging PET-CT. A multivariable logistic regression analysis of radiomics features and clinicopathologic characteristics was used to develop a prediction model. Of the 237 patients, 135 patients (185 nodes) met our inclusion criteria. Eighty-seven LNs were proven to be malignant (47.0%, 87/185). Upon multivariable analysis, metastatic LNs were significantly prevalent in females and patients with adenocarcinoma (odds ratio (OR) = 2.02, 95% confidence interval (CI) = 0.88-4.62 and OR = 0.39, 95% CI = 0.19-0.77 each). Metastatic LNs also had a larger maximal 3D diameter and higher cluster tendency (OR = 9.92, 95% CI = 3.15-31.17 and OR = 2.36, 95% CI = 1.22-4.55 each). The predictive model for metastasis showed a discrimination performance with an area under the receiver operating characteristic curve of 0.728 (95% CI = 0.654-0.801, p value < 0.001). The radiomics approach allows for the noninvasive detection of metastases in LNs with residual FDG uptake after the treatment of NSCLC patients.

An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

Background: Lymph node metastasis (LNM) status is of key importance for the decision-making on treatment and survival prediction. There is no reliable method to precisely evaluate the risk of LNM in NSCLC patients. This study aims to develop and validate a dynamic nomogram to evaluate the risk of LNM in small-size NSCLC.

Methods: The NSCLC ≤ 2 cm patients who underwent initial pulmonary surgery were retrospectively reviewed and randomly divided into a training cohort and a validation cohort as a ratio of 7:3. The training cohort was used for the least absolute shrinkage and selection operator (LASSO) regression to select optimal variables. Based on variables selected, the logistic regression models were developed, and were compared by areas under the receiver operating characteristic curve (AUCs) and decision curve analysis (DCA). The optimal model was used to plot a dynamic nomogram for calculating the risk of LNM and was internally and externally well-validated by calibration curves.

Results: LNM was observed in 12.0% (83/774) of the training cohort and 10.1% (33/328) of the validation cohort (P = 0.743). The optimal model was used to plot a nomogram with six variables incorporated, including tumor size, carcinoembryonic antigen, imaging density, pathological type (adenocarcinoma or non-adenocarcinoma), lymphovascular invasion, and pleural invasion. The nomogram model showed excellent discrimination (AUC = 0.895 vs. 0.931) and great calibration in both the training and validation cohorts. At the threshold probability of 0–0.8, our nomogram adds more net benefits than the treat-none and treat-all lines in the decision curve.

Conclusions: This study firstly developed a cost-efficient dynamic nomogram to precisely and expediently evaluate the risk of LNM in small-size NSCLC and would be helpful for clinicians in decision-making.

Tumor volume is more reliable to predict nodal metastasis in non-small cell lung cancer of 3.0 cm or less in the greatest tumor diameter

Background

In this study, we sought to evaluate the correlation between TV, GTD, and lymph node metastases in NSCLC patients with tumors of GTD ≤ 3.0 cm.

Methods

We retrospectively analyzed the characteristics of clinicopathologic variables for lymph node involvement in 285 NSCLC patients with tumors of GTD ≤ 3.0 cm who accepted curative surgical resection. The TVs were semi-automatically measured by a software, and optimal cutoff points were obtained using the X-tile software. The relationship between GTD and TV were described using non-linear regression. The correlation between GTD, TV, and N stages was analyzed using the Pearson correlation coefficient. The one-way ANOVA was used to compare the GTD and TV of different lymph node stage groups.

Results

The relationship between GTD and TV accorded with the exponential growth model: y = 0.113e^1.455x (y = TV, x = GTD). TV for patients with node metastases (4.78 cm³) was significantly greater than those without metastases (3.57 cm³) (P < 0.001). However, there were no obvious GTD differences in cases with or without lymph node metastases (P = 0.054). We divided all cases into three TV groups using the two cutoff values (0.9 cm³ and 3.9 cm³), and there was an obvious difference in the lymphatic involvement rate between the groups (P < 0.001). The tendency to metastasize was greater with higher TV especially when the TV was > 0.9–14.2 cm³ (P = 0.010).

Conclusions

For NSCLC tumors with GTD ≤ 3.0 cm, TV is a more sensitive marker than GTD in predicting the positive lymph node metastases. The likelihood for metastasis increases with an increasing TV especially when GTD is > 2.0–3.0 cm.

Independent risk factors for lymph node metastasis in 2623 patients with Non-Small cell lung cancer

PURPOSE

this study attempts to identify the independent risk factors that can predict lymph node metastasis for the patients with non-small cell lung cancer (NSCLC), and guide doctor adoption of individualized treatment for such patients.

MATERIALS AND METHODS

This study was approved by the Hospital's Ethics Committee and all patients had signed informed consent forms. We retrospectively reviewed NSCLC patients who had undergone surgical resection from December 2008 to December 2013.The statistical significance of evaluation variables and lymph node metastasis was determined with Pearson's Chi-square test. The risk factors of lymph node metastasis were determined through univariate and multivariate logistic regression analysis. And for the age and tumor diameter factors, optimal cutoff points were determined with a receiver operating characteristic analysis.

RESULTS

In the present study, a total of 2623 patients were included in the study, and 779 patients with lymph node metastasis. Three independent risk factors were identified: age, tumor diameter and Ki-67 index. We found that <65 years of age (Adjusted-OR:1.921), ≥2.85 cm of tumor diameter (Adjusted-OR:3.141), and 5%~25% in Ki-67 group (Adjusted-OR:2.137),≥25% (Adjusted-OR:3.341) were significant. Also we found that 307 patients with lymph node metastasis and the lymph node metastasis rate was 51.0%, when the age<65 years, Ki-67 index≥25%, and the tumor diameter≥2.85 cm. On the contrary, there were only 2 patients with lymph node metastasis, and the rate of lymph node metastasis was 5.1%.

CONCLUSION

Identifying three independent risk factors that predict lymph node metastasis in non-small cell patients, Among NSCLC patients in whom all three predictors were identified, and over a half of the patients showed lymph node metastasis.

Development and evaluation of a venous computed tomography radiomics model to predict lymph node metastasis from non-small cell lung cancer

The objective of this study was to develop a venous computed tomography (CT)-based radiomics model to predict the lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). A total of 411 consecutive patients with NSCLC underwent tumor resection and lymph node (LN) dissection from January 2018 to September 2018 in our hospital. A radiologist with 20 years of diagnostic experience retrospectively reviewed all CT scans and classified all visible LNs into LNM and non-LNM groups without the knowledge of pathological diagnosis. A logistic regression model (radiomics model) in classification of pathology-confirmed NSCLC patients with and without LNM was developed on radiomics features for NSCLC patients. A morphology model was also developed on qualitative morphology features in venous CT scans. A training group included 288 patients (99 with and 189 without LNM) and a validation group included 123 patients (42 and 81, respectively). The receiver operating characteristic curve was performed to discriminate LNM (+) from LNM (-) for CT-reported status, the morphology model and the radiomics model. The area under the curve value in LNM classification on the training group was significantly greater at 0.79 (95% confidence interval [CI]: 0.77-0.81) by use of the radiomics model (build by best 10 features in predicting LNM) compared with 0.51 by CT-reported LN status (P < .001) or 0.66 (95% CI: 0.64-0.68) by morphology model (build by tumor size and spiculation) (P < .001). Similarly, the area under the curve value on the validation group was 0.73 (95% CI: 0.70-0.76) by the radiomics model, compared with 0.52 or 0.63 (95% CI: 0.60-0.66) by the other 2 (both P < .001). A radiomics model shows excellent performance for predicting LNM in NSCLC patients. This predictive radiomics model may benefit patients to get better treatments such as an appropriate surgery.

DNA methylation markers that correlate with occult lymph node metastases of non-small cell lung cancer and a preliminary prediction model

Background

Lymph node (LN) metastasis status is the most important prognostic factor and determines treatment strategy. Methylation alteration is an optimal candidate to trace the signal from early stage tumors due to its early existence, multiple loci and stability in blood. We built a diagnostic tool to screen and identify a set of plasma methylation markers in early stage occult LN metastasis.

Methods

High-throughput targeted methylation sequencing was performed on tissue and matched plasma samples from a cohort of 119 non-small cell lung cancer (NSCLC) patients with a primary lesion of less than 3.0 cm in diameter. The methylation profiles were compared between patients with and without occult LN metastases. We carried out a set of machine-learning analyses on our discovery cohort to evaluate the utility of cell free DNA methylation profiles in early detection of LN metastasis. Two preliminary prognostic models predictive of LN metastasis were built by random forest with differentially methylated markers shared by plasma and tissue samples and markers present either in plasma or tissue samples respectively. The performance of these models was then evaluated using receiver operating characteristic (ROC) statistics derived from ten-fold cross validation repeated ten times.

Results

Within this cohort, 27 cases (27/119, 22.7%) were found to have occult LN metastases found by pathological examination. Compared with those without metastases, 878 and 52 genes were differentially methylated in terms of tissue (MTA3, MIR548H4, HIST3H2A, etc.) and plasma (CIRBP, CHGB, FCHO1, etc.) respectively. 19 of these genes (ICAM1, EPH4, COCH, etc.) were overlapped. We selected 22 pairs of cases with or without occult LN metastasis by matching gender, age, smoking history and tumor histology to build and test the plasma model. The AUC of the preliminary prediction model using markers shared by plasma and tissue samples and markers present either in plasma or tissue samples is 88.6% (95% CI, 87.8–89.4%) and 74.9% (95% CI, 72.2–77.6%) respectively.

Conclusions

We identified a set of specific plasma methylation markers for early occult LN metastasis of NSCLC and established a preliminary non-invasive blood diagnostic tool.

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

BACKGROUND

We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC).

METHODS

Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS

A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively.

CONCLUSIONS

All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

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

INTRODUCTION

In patients with stage IA lung adenocarcinoma (ADC), sublobar resection and tumor spread through air spaces (STAS) are associated with high rates of locoregional recurrence, half of which occur within the regional lymph nodes (LNs). Our objectives were to investigate the association between occult LN metastasis (ONM) and STAS and to assess their prognostic value in patients with clinical stage IA lung ADC.

METHODS

The association between STAS and ONM was analyzed in patients who underwent lobectomy and LN dissection for clinical stage IA lung ADC (n = 809). Multivariable logistic regression analysis was carried out to identify predictors of ONM. Site-specific recurrence by surgical procedure was investigated in patients with pathologic node-negative disease (n = 1055) using a competing risk approach.

RESULTS

ONM was identified in 129 patients (16%)-one-third of ONMs were located only in intrapulmonary nodes. STAS was more common in patients with ONM than in those without ONM (67% versus 39%; p < 0.001) and in patients with multiple ONMs than in those with a single ONM (86%-89% versus 60%-67%). STAS was a significant predictor of ONM (p = 0.004) on multivariable analysis, independent of tumor size, maximum standardized uptake value, and lymphovascular invasion. In patients with STAS-positive ADC (high ONM risk), the risk of recurrence in the treated lobe and regional LNs increased as the extent of resection decreased (recurrence risk: lobectomy < segmentectomy < wedge resection). In patients with STAS-negative ADC, the risk of locoregional recurrence did not differ by procedure type.

CONCLUSIONS

Presence of STAS predicts ONM in patients with clinical stage IA lung ADC and can help stratify risk of recurrence by extent and type of resection.

Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

OBJECTIVES

To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients.

METHODS

This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models.

RESULTS

The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both<0.001).

CONCLUSION

A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.

Preoperative Risk Factors for Pathologic N2 Metastasis in Positron Emission Tomography-Computed Tomography-Diagnosed N0-1 Non-Small Cell Lung Cancer

Background

Accurate mediastinal lymph node staging is vital for the optimal therapy and prognostication of patients with lung cancer. This study aimed to determine the preoperative risk factors for pN2 disease, as well as its incidence and long-term outcomes, in patients with clinical N0–1 non-small cell lung cancer.

Methods

We retrospectively analyzed patients who were treated surgically for primary non-small cell lung cancer from November 2005 to December 2014. Patients staged as clinical N0–1 via chest computed tomography (CT) and positron emission tomography (PET)-CT were divided into two groups (pN0–1 and pN2) and compared.

Results

In a univariate analysis, the significant preoperative risk factors for pN2 included a large tumor size (p=0.083), high maximum standard uptake value on PET (p<0.001), and central location of the tumor (p<0.001). In a multivariate analysis, central location of the tumor (p<0.001) remained a significant preoperative risk factor for pN2 status. The 5-year overall survival rates were 75% and 22.9% in the pN0–1 and pN2 groups, respectively, and 50% and 78.2% in the patients with centrally located and peripherally located tumors, respectively. In a Cox proportional hazard model, central location of the tumor increased the risk of death by 3.4-fold (p<0.001).

Conclusion

More invasive procedures should be considered when pre-operative risk factors are identified in order to improve the efficacy of diagnostic and therapeutic plans and, consequently, the patient’s prognosis.

Differential Relapse Patterns for Non-small Cell Lung Cancer Subtypes Adenocarcinoma and Squamous Cell Carcinoma: Implications for Radiation Oncology

AIMS

Curative-intent (radical) radiotherapy aims to control local disease and cure non-small cell lung cancer (NSCLC). The predominant subtypes of NSCLC are adenocarcinoma and squamous cell carcinoma (SCC). The radiotherapy paradigm offered to patients does not differ according to these two subtypes. Relapse patterns and disease control rates for adenocarcinoma and SCC treated with radical radiotherapy were determined.

MATERIALS AND METHODS

A radical radiotherapy database covering the period from 2004 to June 2016 was examined to determine the first sites of relapse and the actuarial local and distant control rates.

RESULTS

In total, 537 patients with known pathological subtype were treated over the period. In 39 (7%), the site of first relapse was uncertain. Of the remainder, 203 (41%) had adenocarcinoma and 295 (59%) had SCC. At a median follow-up of 16.4 months, 58% had relapsed. There was a difference in relapse patterns (chi-squared test P < 0.0005), with a higher rate of first relapse locally in SCC (42% of all patients versus 24%) and a higher rate of first relapse in the brain for adenocarcinoma (14% versus 3%). The actuarial local control rate was worse for SCC (hazard ratio 0.6, 95% confidence interval 0.5-0.9, P = 0.002). The brain metastasis-free survival was worse for adenocarcinoma (hazard ratio 4.1, 95% confidence interval 2.2-7.5, P < 0.0001).

CONCLUSION

There is a difference in relapse patterns between NSCLC histological subtypes, indicating that these are distinct entities. This may have implications for follow-up policy and strategies to improve disease control.

Lung Adenocarcinoma has a Higher Risk of Lymph Node Metastasis than Squamous Cell Carcinoma: A Propensity Score-Matched Analysis

BACKGROUND

Controversy still exists in which subtype of non-small-cell lung cancer [squamous cell carcinoma (SCC) or adenocarcinoma] is more likely to have lymph node (LN) metastasis. The aim of this study is to compare the pattern of LN metastasis in two cohorts of matched patients surgically treated for SCC or adenocarcinoma.

METHODS

A retrospective analysis of patients undergoing lobectomy or segmentectomy with systematic lymphadenectomy without preoperative treatment for lung SCC or adenocarcinoma was conducted in this study. Data for analysis consisted of age, gender, tumor size, lobe-specific tumor location, tumor location (peripheral or central), and pathologic findings. We conducted the propensity score-matched (PSM) analysis to eliminate potential bias effects of possible confounding factors.

RESULTS

From January 2015 to December 2016 in our department, we finally included a total of 387 patients (including 63 patients with SCC and 324 patients with adenocarcinoma) for analysis. For the unmatched cohort, there was no sufficient evidence of significantly different number of positive LNs (P = 0.90) and rate of LN metastasis (P = 0.23) between SCC patients and adenocarcinoma patients. However, potential confounding factors, for example gender, tumor size, tumor location, tumor differentiation, and total number of dissected LNs, were significantly different between patients with SCC and those with adenocarcinoma. In the analysis of matched cohort after PSM analysis, those above confounding factors were comparable between the two groups. However, patients with adenocarcinoma had significantly more mean positive LNs (2.2 and 0.7; P = 0.008) and a higher rate of LN metastasis (53% and 29%; P = 0.016) than those with SCC.

CONCLUSIONS

Lung adenocarcinoma had a higher risk of LN metastasis than SCC, suggesting that different therapeutic modalities may be indicated for the two different subtypes of lung cancer.

Computed Tomography Histogram Approach to Predict Lymph Node Metastasis in Patients With Clinical Stage IA Lung Cancer

BACKGROUND

Quantitative computed tomography (CT) histogram analysis of tumors is reported to help distinguish between invasive and less invasive lung cancers. This study aimed to clarify whether CT histogram analysis of tumors can be used to classify patients with clinical stage 0 to IA non-small cell lung cancer according to pathologic lymph node (pN) status.

METHODS

Predictive factors associated with pN metastasis were identified from the derivation dataset including 629 patients with clinical stage 0 to IA non-small cell lung cancer who underwent complete resection with lymph node dissection (surgeries between 2008 and 2013). The validation dataset including 238 patients (surgeries between 2014 and 2015) were subsequently reevaluated. Clinicosurgical factors, including CT histogram analysis of tumors (CT value percentiles 2.5, 25, 50, 75, and 97.5, skewness, and kurtosis) were assessed.

RESULTS

Seventy-three patients (12%) in the derivation cohort and 35 patients (15%) in the validation cohort had positive nodes. The pN status significantly affected survival in the entire population: 5-year overall survival of 93.1% vs 71.1% and 5-year disease-free survival of 85.9% vs 43.1% for negative vs positive (both P < .001). On multivariate analysis in the derivation cohort, the 75th percentile CT value (P < .001), age (P = .003), and comorbidities (P = .006) were significantly associated with pN metastasis. The area under the curve and the cutoff level of the 75th percentile CT value relevant to pN metastasis were 0.729 and 1.5 HU, respectively, and the threshold value provided accuracy of 71% for the validation cohort.

CONCLUSIONS

Histogram analysis of CT imaging metrics of tumors contributes to noninvasive prediction of pN metastasis in patients with clinical stage 0 to IA non-small cell lung cancer.

Occult mediastinal lymph node metastasis in FDG-PET/CT node-negative lung adenocarcinoma patients: Risk factors and histopathological study

BACKGROUND

The aim of this study was to investigate predictive factors of occult mediastinal lymph node metastasis (MLNM) in preoperative 18 F-fluorodeoxy-glucose PET/CT node-negative lung adenocarcinoma patients.

METHODS

We reviewed the clinical data and PET/CT parameters of 360 consecutive pulmonary adenocarcinoma patients who were scheduled to undergo anatomical pulmonary resection and systemic mediastinal node dissection. The nodal metastasis was pathologically defined and all resected tumors were classified according to the 2011 IASLC/ATS/ERS classification. Univariate and multivariate analysis were conducted to evaluate the associations between clinicopathological variables and MLNM.

RESULTS

Of all 360 patients, 54 (15.0%) had pathological N2 diseases. The serum CEA level, nodule type, hilar nodal SUVmax, tumor SUVmax, size, location and histologic subtype were associated with MLNM significantly on univariate analysis. On multivariate analysis, CEA ≥ 5.0 ng/mL (P < 0.001), solid nodule (P = 0.012), tumor SUVmax ≥ 3.7 (P < 0.027), hilar nodal SUVmax ≥ 2.0 (P < 0.001) and centrally located tumor (P = 0.035) were independent risk factors for MLNM. The area under the ROC curve (AUC) for tumor SUVmax and hilar nodal SUVmax in predicting MLNM was 0.764 and 0.730, respectively, and the combined use of five factors yielded a higher AUC of 0.885.

CONCLUSION

Increased primary tumor and hilar lymph node SUVmax, solid nodule, centrally located tumor and increased CEA level predicted the increased risk of mediastinal lymph node metastasis. Combined use of these factors improved the diagnostic capacity for predicting N2 disease preoperatively. Invasive mediastinal staging should be considered for patients with these risk factors, even those with a negative mediastinum on PET/CT.

Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer

BACKGROUND

We characterized the performance characteristics of guideline-recommended invasive mediastinal staging (IMS) for lung cancer and developed a prediction model for nodal disease as a potential alternative approach to staging.

METHODS

We conducted a prospective cohort study of adults with suspected/confirmed non-small cell lung cancer without evidence of distant metastatic disease (by computed tomography/positron emission tomography) who underwent nodal evaluation by IMS and/or at the time of resection. The true-positive rate was the proportion of patients with true nodal disease selected to undergo IMS based on guideline recommendations, and the false-positive rate was the proportion of patients without true nodal disease selected to undergo IMS. Logistic regression was used to predict nodal disease using radiographic predictors.

RESULTS

Among 123 eligible subjects, 31 (25%) had pathologically confirmed nodal disease. A guideline-recommended invasive staging strategy had a true-positive rate and false-positive rate of 100% and 65%, respectively. The prediction model fit the data well (goodness-of-fit test, p = 0.55) and had excellent discrimination (optimism corrected c-statistic, 0.78; 95% confidence interval, 0.72 to 0.89). Exploratory analysis revealed that use of the prediction model could achieve a false-positive rate of 44% and a true-positive rate of 97%.

CONCLUSIONS

A guideline-recommended strategy for IMS selects all patients with true nodal disease and most patients without nodal disease for IMS. Our prediction model appears to maintain (within a margin of error) the sensitivity of a guideline-recommended invasive staging strategy and has the potential to reduce the use of invasive procedures.

Which definition of a central tumour is more predictive of occult mediastinal metastasis in nonsmall cell lung cancer patients with radiological N0 disease?

Background

Guidelines recommend invasive mediastinal staging for centrally located tumours, even in radiological N0 nonsmall cell lung cancer (NSCLC). However, there is no uniform definition of a central tumour that is more predictive of occult mediastinal metastasis.

Methods

A total of 1337 consecutive patients with radiological N0 disease underwent invasive mediastinal staging. Tumours were categorised into central and peripheral by seven different definitions.

Results

About 7% (93 out of 1337) of patients had occult N2 disease, and they had significantly larger tumour size and more solid tumours on computed tomography. After adjustment for patient- and tumour-related characteristics, only the central tumour definition of the inner one-third of the hemithorax adopted by drawing concentric lines arising from the midline significantly predicted occult N2 disease (adjusted OR 2.13, 95% CI 1.17–3.87; p=0.013). This association was maintained after excluding patients with pure ground-glass nodules (adjusted OR 2.54, 95% CI 1.37–4.71; p=0.003) or only including those with solid tumours (adjusted OR 2.30, 95% CI 1.08–4.88; p=0.030).

Conclusions

We suggest that a central tumour should be defined using the inner one-third of the hemithorax adopted by drawing concentric lines from the midline. This is particularly useful for predicting occult N2 disease in patients with NSCLC.

Central tumours defined as located in the inner one-third of the hemithorax adopted by drawing concentric lines from the midline are associated with occult mediastinal metastasis in patients with NSCLC and radiological N0 diseasehttp://ow.ly/scg630nbRmY

Centrally located lung cancer and risk of occult nodal disease: an objective evaluation of multiple definitions of tumour centrality with dedicated imaging software

Introduction

Current guidelines recommend invasive mediastinal staging in patients with centrally located radiographic stage T1N0M0 nonsmall cell lung cancer (NSCLC). The lack of a specific definition of a central tumour has resulted in discrepancies among guidelines and heterogeneity in practice patterns.

Methods

Our objective was to study specific definitions of tumour centrality and their association with occult nodal disease. Pre-operative chest computed tomography scans from patients with clinical (c) T1N0M0 NSCLC were processed with a dedicated software system that divides the lungs in thirds following vertical and concentric lines. This software accurately assigns tumours to a specific third based both on the location of the centre of the tumour and its most medial aspect, creating eight possible definitions of central tumours.

Results

607 patients were included in our study. Surgery was performed for 596 tumours (98%). The overall pathological (p) N disease was: 504 (83%) N0, 56 (9%) N1, 47 (8%) N2 and no N3. The prevalence of N2 disease remained relatively low regardless of tumour location. Central tumours were associated with upstaging from cN0 to any N (pN1/pN2). Two definitions were associated with upstaging to any N: concentric lines, inner one-third, centre of the tumour (OR 3.91, 95% CI 1.85–8.26; p<0.001) and concentric lines, inner two-thirds, most medial aspect of the tumour (OR 1.91, 95% CI 1.23–2.97; p=0.004).

Conclusions

We objectively identified two specific definitions of central tumours. While the rate of occult mediastinal disease was relatively low regardless of tumour location, central tumours were associated with upstaging from cN0 to any N.

Astragalus polysaccharide combined with 10-hydroxycamptothecin inhibits metastasis in non-small cell lung carcinoma cell lines via the MAP4K3/mTOR signaling pathway

Non‑small cell lung carcinoma (NSCLC) is a life‑threatening malignancy. The level of the cell growth regulator mitogen‑activated protein kinase kinase kinase kinase 3 (MAP4K3) has been shown to be correlated with a high risk of NSCLC recurrence and poor recurrence‑free survival rate. The present study examined the effects of Astragalus polysaccharide (APS) and 10‑hydroxycamptothecin (HCPT), which are associated with marked suppression and dephosphorylation of the MAP4K3/mammalian target of rapamycin (mTOR) signaling pathway, in the H1299 NSCLC cell line. APS and HCPT decreased H1299 cell viability, induced apoptosis and altered the cell cycle stages, as evaluated using an 3‑(4,5‑dimethylthiazol‑2‑yl)‑2,5‑diphenyltetrazolium bromide assay and flow cytometric analysis. Furthermore, APS increased the expression of apoptosis‑associated genes B‑cell lymphoma 2 (Bcl‑2) and Bcl‑2‑associated X protein (BAX), of proteases cysteine‑aspartic acid protease (caspase)‑3 and ‑9, and of cytochrome c. HCPT promoted autophagy in H1299 cells, with concomitant suppression of the expression of MAP4K3 and downregulation of mTOR signaling. Notably, combination treatment with the two agents reduced the migration and invasion of H1299 cells compared with the single treatments. It was also demonstrated that the overexpression of MAP4K3 promoted the migration and invasion of H1299 cells, and that the kinase activity was essential to this. These findings suggested that MAP4K3 may be an attractive target for the treatment of NSCLC.

Comparison of GLUT-1, SGLT-1, and SGLT-2 expression in false-negative and true-positive lymph nodes during the 18F-FDG PET/CT mediastinal nodal staging of non-small cell lung cancer

INTRODUCTION

Although positron emission tomography (PET) with 2-deoxy-2-[fluorine-18]fluoro-d-glucose integrated with computed tomography (CT), (¹⁸F-FDG PET/CT), has recently improved the mediastinal nodal staging of non-small cell lung cancer (NSCLC), this method can show false negativity. We immunohistochemically investigated the expression of glucose transporters (GLUT-1, SGLT-1, and SGLT-2) in false negative and true positive mediastinal nodes via ¹⁸F-FDG PET/CT.

METHODS

We investigated patients with clinically-diagnosed N0/pathological N2 diseases and patients with clinically-diagnosed N2/pathological N2 disease. The patients who were included in this study were evaluated using ¹⁸F-FDG PET/CT followed by surgical resection between January 2004 and December 2015. The expression of GLUT-1, SGLT-1, and SGLT-2 in the metastatic mediastinal lymph nodes, and clinicopathological variables such as primary tumor size, lymph node size, histological type, and SUV_max of the primary lesion, were compared between false negative nodes and true positive nodes.

RESULTS

The total number of PET false negative metastatic mediastinal lymph nodes was 22 in the 17 patients who were clinical N0/pathological N2, and the number of PET true positives was 15 in the 11 patients who were clinical N2/pathological N2. GLUT-1 expression was positive in five false negative nodes and 10 true positive nodes. SGLT-2 expression was positive in 12 false negative nodes and one true positive node, whereas both false negative and true positive nodes showed no SGLT-1 staining. Univariate analysis showed that the reduced expression of GLUT-1 (P = 0.015), and overexpression of SGLT-2 (P = 0.004) were the significant causative factors for false negative nodes. Multivariate analysis also showed that the reduced expression of GLUT-1 (P = 0.012) and overexpression of SGLT-2 (P = 0.006) were the significant causative factors for false negative nodes.

CONCLUSION

It suggests that the reduced expression of GLUT-1 and overexpression of SGLT-2 are associated with false-negative lymph node metastases in NSCLC.

Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma

OBJECTIVE

The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma.

MATERIALS AND METHODS

A total of 492 patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor. A radiomics signature was generated by use of the relief-based feature method and the support vector machine classification method. A ROC regression curve was drawn for the predictive performance of radiomics features. Multivariate logistic regression models based on clinicopathologic and radiomics features were compared for discriminating mediastinal LN metastasis.

RESULTS

Clinical variables (sex, tumor diameter, tumor location) and predominant subtype were risk factors for pathologic mediastinal LN metastasis. The accuracy of radiomics signature for predicting mediastinal LN metastasis was 91.1% in ROC analysis (AUC, 0.972; sensitivity, 94.8%; specificity, 92%). Radiomics signature (Akaike information criterion [AIC] value, 80.9%) showed model fit superior to that of the clinicohistopathologic model (AIC value, 61.1%) for predicting mediastinal LN metastasis.

CONCLUSION

The radiomics signature of a primary tumor based on CT scans can be used for quantitative and noninvasive prediction of occult mediastinal LN metastasis of lung adenocarcinoma.

A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma

BACKGROUND

Some clinical N0 lung adenocarcinomas have been pathologically diagnosed as N1 or N2. To improve the preoperative diagnostic accuracy of lymph node disease, we developed a prediction model for lymph node metastasis in cT1 N0 M0 lung adenocarcinoma based on computed tomography texture analysis and clinical characteristics to estimate the probability of lymph node metastasis.

METHODS

The records of 501 consecutive patients with cT1 N0 M0 lung adenocarcinoma who underwent computed tomography scan and pulmonary resection with systematic lymph nodes dissection or lymph nodes sampling were reviewed. Each nodule was manually segmented, and its computerized texture features were extracted. Multivariate logistic regression with fivefold validation was used to estimate independent predictors and build the prediction model. The prediction model was then externally validated. A nomogram was developed based on logistic regression results.

RESULTS

Among 501 patients, 41 were diagnosed with positive lymph nodes (8.18%). Four independent predictors were identified: the skewness and 90th percentile of computed tomography number, nodule compactness, and carcinoembryonic antigen level. This model showed good calibration (Hosmer-Lemeshow test, p = 0.337), with an area under the curve of 0.883 (95% confidence interval, 0.842 to 0.924; p < 0.001). The area under the curve was 0.808 (95% confidence interval, 0.735 to 0.880) when validated with independent data.

CONCLUSIONS

A model based on computerized textures and carcinoembryonic antigen level can assess the lymph node status of patients with cT1 N0 M0 lung adenocarcinoma preoperatively, which could assist surgeons in making subsequent clinical decisions.

The dosimetric effects of limited elective nodal irradiation in volumetric modulated arc therapy treatment planning for locally advanced non-small cell lung cancer

OBJECTIVE

Contemporary radiotherapy guidelines for locally advanced non-small cell lung carcinoma (LA-NSCLC) recommend omitting elective nodal irradiation, despite the fact that evidence supporting this came primarily from older reports assessing comprehensive nodal coverage using 3D conformal techniques. Herein, we evaluated the dosimetric implications of the addition of limited elective nodal irradiation (LENI) to standard involved field irradiation (IFI) using volumetric modulated arc therapy (VMAT) planning.

METHOD

Target volumes and organs-at-risk (OARs) were delineated on CT simulation images of 20 patients with LA-NSCLC. Two VMAT plans (IFI and LENI) were generated for each patient. Involved sites were treated to 60 Gy in 30 fractions for both IFI and LENI plans. Adjacent uninvolved nodal regions, considered high risk based on the primary tumor site and extent of nodal involvement, were treated to 51 Gy in 30 fractions in LENI plans using a simultaneous integrated boost approach.

RESULTS

All planning objectives for PTVs and OARs were achieved for both IFI and LENI plans. LENI resulted in significantly higher esophagus Dmean (15.3 vs. 22.5 Gy, p < 0.01), spinal cord Dmax (34.9 vs. 42.4 Gy, p = 0.02) and lung Dmean (13.5 vs. 15.9 Gy, p = 0.02), V20 (23.0 vs. 27.9%, p = 0.03), and V5 (52.6 vs. 59.4%, p = 0.02). No differences were observed in heart parameters. On average, only 32.2% of the high-risk nodal volume received an incidental dose of 51 Gy when untargeted in IFI plans.

CONCLUSION

The addition of LENI to VMAT plans for LA-NSCLC is feasible, with only modestly increased doses to OARs and marginal expected increase in associated toxicity.

Risk Factors for Predicting Occult Lymph Node Metastasis in Patients with Clinical Stage I Non-small Cell Lung Cancer Staged by Integrated Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography

BACKGROUND

Lymph nodes in patients with non-small cell lung cancer (NSCLC) are often staged using integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). However, this modality has limited ability to detect micrometastases. We aimed to define risk factors for occult lymph node metastasis in patients with clinical stage I NSCLC diagnosed by preoperative integrated FDG-PET/CT.

METHODS

We retrospectively reviewed the records of 246 patients diagnosed with clinical stage I NSCLC based on integrated FDG-PET/CT between April 2007 and May 2015. All patients were treated by complete surgical resection. The prevalence of occult lymph node metastasis in patients with clinical stage I NSCLC was analysed according to clinicopathological factors. Risk factors for occult lymph node metastasis were defined using univariate and multivariate analyses.

RESULTS

Occult lymph node metastasis was detected in 31 patients (12.6 %). Univariate analysis revealed CEA (P = 0.04), SUV_max of the primary tumour (P = 0.031), adenocarcinoma (P = 0.023), tumour size (P = 0.002) and pleural invasion (P = 0.046) as significant predictors of occult lymph node metastasis. Multivariate analysis selected SUV_max of the primary tumour (P = 0.049), adenocarcinoma (P = 0.003) and tumour size (P = 0.019) as independent predictors of occult lymph node metastasis.

CONCLUSIONS

The SUV_max of the primary tumour, adenocarcinoma and tumour size were risk factors for occult lymph node metastasis in patients with NSCLC diagnosed as clinical stage I by preoperative integrated FDG-PET/CT. These findings would be helpful in selecting candidates for mediastinoscopy or endobronchial ultrasound-guided transbronchial needle aspiration.

Risk factors of lymph node metastasis in patients with non-small cell lung cancer ≤ 2 cm in size: A monocentric population-based analysis

Aim

This study was designed to determine the risk factors of lymph node metastasis in non‐small cell lung cancer (NSCLC) patients with tumors ≤ 2 cm, using the Shanghai Chest Hospital Lung Cancer Database.

Methods

Five hundred and eighteen patients with NSCLC ≤ 2 cm were included in this study, and were classified into lymph node‐positive and lymph node‐negative groups. Univariate and multivariate logistic regression analyses were performed to select the independent risk factors for lymph node metastasis in NSCLC patients.

Results

No evidence of metastasis was found in tumors ≤ 1 cm, all positive results were in tumors sized 1–2 cm. Imaging characteristics, including solid and part‐solid nodules, were strongly associated with lymph node metastasis (odds ratio [OR] 24.959, 95% confidence interval [CI] 5.999–103.835, P < 0.001; OR 12.559, 95% CI 3.564–44.259, P < 0.001) and subgroup logistic analysis (OR 21.384, 95% CI 5.058–90.407, P < 0.001; OR 11.632, 95% CI 3.290–41.126, P < 0.001). Greater lymph node metastasis was observed in non‐adeno non‐squamous carcinoma. The presence of pleural invasion and carcinoembryonic antigen levels indicated lymph node dissection. Similar results were revealed in subgroup analysis in tumors ≤ 2 to > 1 cm.

Conclusion

Size had a great impact on lymph node metastasis, especially tumors of 1–2 cm. Preoperative imaging, non‐adeno non‐squamous carcinoma, pleural invasion, and carcinoembryonic antigen all indicated lymph node dissection. There was no discrepancy between N1 and N2 positive lymph nodes.

A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL

RATIONALE

Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies.

OBJECTIVES

To develop a clinical prediction model for estimating the prN2/3.

METHODS

We used the AQuIRE (American College of Chest Physicians Quality Improvement Registry, Evaluation, and Education) registry to identify patients with NSCLC with clinical radiographic stage T1-3, N0-3, M0 disease that had EBUS-TBNA for staging. The dependent variable was the presence of N2 or N3 disease (vs. N0 or N1) as assessed by EBUS-TBNA. Univariate followed by multivariable logistic regression analysis was used to develop a parsimonious clinical prediction model to estimate prN2/3. External validation was performed using data from three other hospitals.

MEASUREMENTS AND MAIN RESULTS

The model derivation cohort (n = 633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher positron emission tomography-computed tomography N stage were associated with a higher prN2/3. Area under the receiver operating characteristic curve was 0.85 (95% confidence interval, 0.82-0.89), model fit was acceptable (Hosmer-Lemeshow, P = 0.62; Brier score, 0.125). We externally validated the model in 722 patients. Area under the receiver operating characteristic curve was 0.88 (95% confidence interval, 0.85-0.90). Calibration using the general calibration model method resulted in acceptable goodness of fit (Hosmer-Lemeshow test, P = 0.54; Brier score, 0.132).

CONCLUSIONS

Our prediction rule can be used to estimate prN2/3 in patients with NSCLC. The model has the potential to facilitate clinical decision making in the staging of NSCLC.

Impact of maximum standardized uptake value of non-small cell lung cancer on detecting lymph node involvement in potential stereotactic body radiotherapy candidates

BACKGROUND

The retrospective study investigated the association between the maximum standardized uptake value (SUV_max) of primary tumor and lymph node involvement in potential stereotactic body radiotherapy (SBRT) candidates.

METHODS

A total of 185 patients with clinical stage I NSCLC were enrolled in the current study. All patients underwent lobectomy with systematic lymph node dissection following preoperative FDG-PET/CT scanning. The association between clinicopathological variables and lymph node involvement was analyzed by univariate and multivariate analysis. Spearman's correlation test was used to evaluate the correlation between them. Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curve.

RESULTS

Among these patients, 22.1% had occult lymph node involvement, 15.1% were N1 and 7.0% were N2. Greater tumor size (P=0.007), elevated CEA (P=0.006), central location (P=0.002), higher SUV_max (P<0.001), solid nodule type (P=0.002), visceral pleural invasion (P=0.001) and presence of micropapillary and solid patterns (P=0.002) were significantly associated with lymph node involvement. In multivariate analysis, lymph node involvement was associated with central location (OR 5.784, 95% CI: 1.584-21.114, P=0.008), SUV_max (increase of 1 unite, OR 1.147, 95% CI: 1.035-1.272, P=0.009) and visceral pleural invasion (OR 3.044, 95% CI: 1.369-6.769, P=0.006). ROC area under the curve of SUV_max for lymph node involvement was 0.770 (95% CI: 0.698-0.841), the sensitivity and specificity were 85.4% and 63.2%, respectively. Spearman's correlation test showed that SUV_max of tumor mostly depended on tumor size and nodule type.

CONCLUSIONS

SUV_max of primary tumor was a predictor of lymph node involvement for potential SBRT candidates. Centrally located tumor and visceral pleural invasion were related to higher rate of nodal metastasis. Lobectomy and systemic lymph node dissection should be performed in these patients, instead of SBRT.