A prediction model for N2 disease in T1 non–small cell lung cancer

Oncological feasibility of segmentectomy for inner-located lung cancer

Objective

Oncological feasibility of segmentectomy for internal non-small cell lung cancer (NSCLC) has not been assessed adequately. We assessed the oncological feasibility of segmentectomy for inner-located NSCLC by investigating surgical margins and patient prognosis after undergoing the procedure.

Methods

Of the 3555 patients who underwent resection for lung cancer between 2013 and 2019 at our institution, 659 patients who underwent segmentectomy for clinical stage 0 to stage1A NSCLC were included in this study. Patients were separated into 2 groups according to whether the tumor was in the inner or outer third of the lung area. Clinical characteristics and prognoses were retrospectively compared between the groups.

Results

Of the included 659 cases, 183 (27.8%) were inner-located, and 476 (72.2%) had outer-located NSCLC. The surgical margin was significantly shorter in the inner-located group than in the outer group (median, 16 vs 25 mm; P < .001). The 5-year recurrence-free survival and overall survival probabilities were 91.1%/91.8% (P = .530) and 94.1%/95.6% (P = .345) for inner/outer-located groups, respectively. Multivariate analysis showed that clinical stage IA2 or 3 (P = .043), lymphovascular invasion (P < .001), and surgical margins <20 mm (P = .017) were independent prognostic factors for recurrence-free survival. The location of the inner or outer tumors was not related to the prognosis.

Conclusions

For clinical stage 0 to stage1A NSCLC, tumor location in the inner two-thirds of the lung was not associated with prognosis after segmentectomy. Because one of the independent prognostic factors is margin distance, segmentectomy for inner-located NSCLC would be oncologically acceptable when an adequate surgical margin is secured.

Impact of intrapulmonary tumour location of non-small-cell lung cancer on surgical outcomes for segmentectomy

OBJECTIVES

While segmentectomy is considered a viable option for small peripheral non-small-cell lung cancer, its efficacy for central lesions remains uncertain. This study aimed to assess the oncological outcomes of segmentectomy for central lesions compared to peripheral ones.

METHODS

We retrospectively examined 338 clinical stage IA non-small-cell lung cancer patients who underwent thoracoscopic anatomical segmentectomy at our institution from January 2013 to December 2021. Patients were divided into 2 groups based on intrapulmonary tumour location: inner two-thirds (central group, n = 82) and outer one-third (peripheral group, n = 256).

RESULTS

The gender, body mass index, performance score, smoking, comorbidities and preoperative pulmonary function were similar in both groups. On computed tomography images, tumour diameter and consolidation-to-tumour ratio were comparable between the groups. The central group had significantly greater tumour-to-pleura distances [mm, 23 (18-27) vs 11 (8-14); P < 0.001], shorter margin distances [mm, 20 (15-20) vs 20 (20-20); P < 0.001] and larger resected lung volumes based on subsegment count [4 (3-6) vs 3 (3-5); P = 0.004] than the peripheral group. Surgery duration, bleeding, hospitalization or drainage period, mortality, readmission and pathological stage were equivalent between the groups. The central group showed significantly more postoperative pleural effusions (5% vs 1%; P = 0.03) than the peripheral group, with no adverse impact on postoperative pulmonary functions. During the follow-up period, local-only recurrence rates were 0% and 8% in the respective groups (Gray test P = 0.07), and total recurrence rates were 6% and 11% (Gray test P = 0.70), with no significant differences. Moreover, no significant inter-group difference in overall survival rates was observed (82% vs 93%; P = 0.15).

CONCLUSIONS

Segmentectomy may be a promising therapeutic option for early-stage non-small-cell lung cancer located in the inner two-thirds of the parenchyma.

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.

A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time

PURPOSE

Latent lymph node metastasis is a clinical concern in the surgical treatment of non-small cell lung cancer (NSCLC). The present study identified a simple tool, including the volume-doubling time (VDT), for evaluating the risk of nodal metastasis.

METHODS

We reviewed, retrospectively, 560 patients who underwent radical resection for cN0M0 NSCLC. The whole tumor VDT and solid component VDT (SVDT) for differentiating the histological type and adenocarcinoma subtype were analyzed and a nomogram was constructed using variables selected through a stepwise selection method. The model was assessed through a calibration curve and decision curve analysis (DCA).

RESULTS

Lymph node metastases were detected in 89 patients (15.9%). The SVDT tended to be longer in patients with adenocarcinoma (294.5 days, p < 0.0001) than in those with other histological types of NSCLC, but was shorter when the solid/micropapillary component was predominant (127.0 days, p < 0.0001). The selected variables (tumor location, solid component diameter, consolidation tumor ratio, SVDT, and carcinoembryonic antigen) demonstrated significant differences and were used for the nomogram. The calibration curve indicated consistency, and the DCA showed validity across most threshold ranges from 0 to 68%.

CONCLUSIONS

The established nomogram is a useful tool for the preoperative prediction of lymph node metastasis, and the SVDT was the most influential factor in the nomogram.

A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer

Systematic lymph node dissection has been widely accepted and turned into a standard procedure for lung cancer surgery. In recent years, the concept of "minimal invasive surgery (MIS)" has greatly changed the surgical paradigm of lung cancer. Previous studies revealed that excessive dissection of lymph nodes without metastases had uncertain clinical benefit. Meanwhile, it leads to the elevated risk of postoperative complications including chylothorax and laryngeal nerve injury. In addition, dissection of nonmetastatic lymph nodes may disturb systematic immunity, resulting in the secondary effect on primary tumor or latent metastases. The past decades have witnessed the innovative strategies such as lobe-specific lymph node dissection and selective lymph node dissection. On the basis of evolution of lymph node dissection strategy, we discuss the negative effects of excessive nonmetastatic lymph node dissection and summarize the recent advances in the optimized dissection strategies, hoping to provide unique perspectives on the future directions.

PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Prediction Models for Mediastinal Metastasis and Its Detection by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration in Potentially Operable Non-Small Cell Lung Cancer: A Prospective Study

BACKGROUND

Prediction models for mediastinal metastasis and its detection by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) have not been developed using a prospective cohort of potentially operable patients with non-small cell lung cancer (NSCLC).

RESEARCH QUESTION

Can mediastinal metastasis and its detection by EBUS-TBNA be predicted with prediction models in NSCLC?

STUDY DESIGN AND METHODS

For the prospective development cohort, 589 potentially operable patients with NSCLC were evaluated (July 2016-June 2019) from five Korean teaching hospitals. Mediastinal staging was performed using EBUS-TBNA (with or without the transesophageal approach). Surgery was performed for patients without clinical N (cN) 2-3 disease by endoscopic staging. The prediction model for lung cancer staging-mediastinal metastasis (PLUS-M) and a model for mediastinal metastasis detection by EBUS-TBNA (PLUS-E) were developed using multivariable logistic regression analyses. Validation was performed using a retrospective cohort (n = 309) from a different period (June 2019-August 2021).

RESULTS

The prevalence of mediastinal metastasis diagnosed by EBUS-TBNA or surgery and the sensitivity of EBUS-TBNA in the development cohort were 35.3% and 87.0%, respectively. In PLUS-M, younger age (< 60 years and 60-70 years compared with ≥ 70 years), nonsquamous histology (adenocarcinoma and others), central tumor location, tumor size (> 3-5 cm), cN1 or cN2-3 stage by CT, and cN1 or cN2-3 stage by PET-CT were significant risk factors for N2-3 disease. Areas under the receiver operating characteristic curve (AUCs) for PLUS-M and PLUS-E were 0.876 (95% CI, 0.845-0.906) and 0.889 (95% CI, 0.859-0.918), respectively. Model fit was good (PLUS-M: Hosmer-Lemeshow P = .658, Brier score = 0.129; PLUS-E: Hosmer-Lemeshow P = .569, Brier score = 0.118). In the validation cohort, PLUS-M (AUC, 0.859 [95% CI, 0.817-0.902], Hosmer-Lemeshow P = .609, Brier score = 0.144) and PLUS-E (AUC, 0.900 [95% CI, 0.865-0.936], Hosmer-Lemeshow P = .361, Brier score = 0.112) showed good discrimination ability and calibration.

INTERPRETATION

PLUS-M and PLUS-E can be used effectively for decision-making for invasive mediastinal staging in NSCLC.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT02991924; URL: www.

CLINICALTRIALS

gov.

Selective Mediastinal Lymph Node Dissection Strategy for Clinical T1N0 Invasive Lung Cancer: A Prospective, Multicenter, Clinical Trial

INTRODUCTION

We aimed to prospectively evaluate our previously proposed selective mediastinal lymph node (LN) dissection strategy for peripheral clinical T1N0 invasive NSCLC.

METHODS

This is a multicenter, prospective clinical trial in China. We set six criteria for predicting negative LN stations and finally guiding selective LN dissection. Consolidation tumor ratio less than or equal to 0.5, segment location, lepidic-predominant adenocarcinoma (LPA), negative hilar nodes (stations 10-12), and negative visceral pleural invasion (VPI) were used separately or in combination as predictors of negative LN status in the whole, superior, or inferior mediastinal zone. LPA, hilar node involvement, and VPI were diagnosed intraoperatively. All patients actually underwent systematic mediastinal LN dissection. The primary end point was the accuracy of the strategy in predicting LN involvement. If LN metastasis occurred in certain mediastinal zone that was predicted to be negative, it was considered as an "inaccurate" case.

RESULTS

A total of 720 patients were enrolled. The median number of LN dissected was 15 (interquartile range: 11-20). All negative node status in certain mediastinal zone was correctly predicted by the strategy. Compared with final pathologic findings, the accuracy of frozen section to diagnose LPA, VPI, and hilar node metastasis was 94.0%, 98.9%, and 99.6%, respectively. Inaccurate intraoperative diagnosis of LPA, VPI, or hilar node metastasis did not lead to inaccurate prediction of node-negative status.

CONCLUSIONS

This is the first prospective trial validating the specific mediastinal LN metastasis pattern in cT1N0 invasive NSCLC, which provides important evidence for clinical applications of selective LN dissection strategy.

Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET/CT radiomics

AIM

To establish and verify a 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics nomogram to predict mediastinal lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) patients preoperatively.

MATERIALS AND METHODS

This retrospective study enrolled 155 NSCLC patients (primary cohort, n=93; validation cohort, n=62). For each patient, 2,704 radiomic features were extracted from the primary lung cancer regions. Four procedures including the Mann-Whitney U-test, Spearman's correlation analysis, minimum redundancy-maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were utilised for determining essential features and establishing a radiomics signature. After that, a nomogram was established. The nomogram's potential was assessed based on its discrimination, calibration, and clinical usefulness. The radiomics signature and nomogram predictive performances were evaluated with respect to the area under the receiver operating characteristic curve (AUC), specificity, accuracy, and sensitivity.

RESULTS

The radiomics signature composed of eight selected features had good discriminatory performance of LNM versus non-LNM groups an AUC of 0.851 and 0.826 in primary and validation cohorts, respectively. The nomogram also indicated good discrimination with an AUC of 0.869 and 0.847 in the primary and validation cohorts, respectively. Furthermore, good calibration was demonstrated utilising the nomogram.

CONCLUSIONS

An ¹⁸F-FDG PET/CT-based radiomics nomogram that integrates the radiomics signature and age was promoted to predict mediastinal LNM within NSCLC patients, which could potentially facilitate individualised therapy for mediastinal LNM before treatment. The nomogram was beneficial in clinical practice, as illustrated by decision curve analysis.

Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method

BACKGROUND

Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically.

PURPOSE

To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method.

METHODS

In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively.

RESULTS

Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use.

CONCLUSION

This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.

Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer

It remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC. The clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete lymph node dissection were retrospectively analyzed for their association with the HNM or the MNM. After analyzing the variables, we developed multivariable logistic models with nomograms to estimate the risk of lymph node metastasis in different regions. The predictive efficacy was then validated in a validation cohort of 418 patients. It was confirmed that carcinoembryonic antigen (>5.75 ng/mL), CYFRA211 (>2.85 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, and vascular invasion were predictors of HNM, and carcinoembryonic antigen (>8.25 ng/mL), CYFRA211 (>2.95 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion were predictors of MNM. The validation of the prediction models based on the above results demonstrated good discriminatory power. Our predictive models are helpful in the decision-making process of specific therapeutic strategies for the regional lymph node metastasis in patients with clinical stage I NSCLC.

Correlation analysis between unenhanced and enhanced CT radiomic features of lung cancers presenting as solid nodules and their efficacy for predicting hilar and mediastinal lymph node metastases

Objectives

If hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer.

Methods

Two hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models.

Results

A total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p < 0.05). Among them, the shape features had the strongest correlation (mean r = 0.98). There were 5 features in the enhanced model and the unenhanced model, which had important predicting significance. The AUCs were 0.811 and 0.803, respectively. There was no significant difference in the predictive performance of the two models (DeLong's test, p = 0.05).

Conclusion

Our study models achieved higher accuracy for predicting hilar and mediastinal lymph node metastasis of solid nodule lung cancer and have some value in promoting the staging accuracy of lung cancer. Our results show that CT radiomics features have potential to predict hilar and mediastinal lymph node metastases in solid nodular lung cancer. In addition, enhanced and unenhanced CT radiomics models had comparable predictive power in predicting hilar and mediastinal lymph node metastases.

Construction and Evaluation of a Preoperative Prediction Model for Lymph Node Metastasis of cIA Lung Adenocarcinoma Using Random Forest

Background

Lymph node metastasis (LNM) is the main route of metastasis in lung adenocarcinoma (LA), and preoperative prediction of LNM in early LA is key for accurate medical treatment. We aimed to establish a preoperative prediction model of LNM of early LA through clinical data mining to reduce unnecessary lymph node dissection, reduce surgical injury, and shorten the operation time.

Methods

We retrospectively collected imaging data and clinical features of 1121 patients with early LA who underwent video-assisted thoracic surgery at the First Hospital of China Medical University from 2004 to 2021. Logistic regression analysis was used to select variables and establish the preoperative diagnosis model using random forest classifier (RFC). The prediction results from the test set were used to evaluate the prediction performance of the model.

Results

Combining the results of logistic analysis and practical clinical application experience, nine clinical features were included. In the random forest classifier model, when the number of nodes was three and the n-tree value is 500, we obtained the best prediction model (accuracy = 0.9769), with a positive prediction rate of 90% and a negative prediction rate of 98.69%.

Conclusion

We established a preoperative prediction model for LNM of early LA using a machine learning random forest method combined with clinical and imaging features. More excellent predictors may be obtained by refining imaging features.

Partitioning the lung field based on the depth ratio in three-dimensional space

Background

To explore the feasibility of the depth ratio method partitioning the lung parenchyma and the depth distribution of lung nodules in pulmonary segmentectomy.

Methods

Based on the measurement units, patients were allocated to the chest group, the lobar group, and the symmetrical 3 sectors group. In each unit, the center of the respective bronchial cross-section was set as the starting point (O). Connecting the O point with the center of the lesion (A) and extending to the endpoint (B) on the pleural, the radial line (OB) was trisected to divide the outer, middle, and inner regions. The depth ratio and relevant regional distribution were simultaneously verified using 2-dimensional (2D) coronal, sagittal, and axial computed tomography images and 3-dimensional (3D) reconstruction images.

Results

Two hundred and nine patients were included in this study. The median age was 53 (IQR, 44.5–62) years and 64 were males. The intra-group consistency of the depth ratio region partition was 100%. The consistency of the inter-group region partition differed among the three groups (Kappa values 0.511, 0.517, and 0.923). The chest group, lobar group, and symmetrical 3 sectors group had 69.4%, 26.3%, and 4.8% mediastinum disturbance, respectively (P<0.001).

Conclusions

The depth ratio method in the symmetrical 3 sectors of the lung maximally eliminated the disturbance of the mediastinal structures and more accurately trisected the lung parenchymal in 3D space. Sublobar resection based on subsegments strategy is feasible for outer 2/3 pulmonary nodules when depth ratio is used as the measurement method.

Definitions of Central Tumors in Radiologically Node-Negative, Early-Stage Lung Cancer for Preoperative Mediastinal Lymph Node Staging: A Dual-Institution, Multireader Study

BACKGROUND

Definitions for central lung cancer (CLC) have been ambiguous in guidelines, causing difficulty in selecting candidates for invasive mediastinal staging among patients with radiologically node-negative, early-stage lung cancer.

RESEARCH QUESTION

What is the optimal definition for CLC that is robust to interreader and institutional variation to select candidates for invasive mediastinal staging among those with clinical T1N0M0 lung cancer?

STUDY DESIGN AND METHODS

Two retrospective cohorts were evaluated for the associations of central lung cancer according to 13 definitions based on chest CT scan with occult nodal metastasis. Univariate and multivariate ordinal logistic regression analyses were performed with the pathologic N category as an ordinal outcome. Robust definitions, which retained statistical significance across multireader, dual-institutional datasets, were identified. For these definitions, binary diagnostic performance and interreader agreement were investigated.

RESULTS

In the two cohorts, 807 patients (median age, 63 years; interquartile range [IQR], 56-71 years; 410 women; 33 pN1, 48 pN2, and 1 pN3) and 510 patients (median age, 65 years; IQR, 58-71 years; 267 women; 33 pN1, 20 pN2, and no pN3) were included, respectively. Three definitions robust to interreader variation and dataset heterogeneity were identified: definition 7 (concentric lines arising from the midline, inner one-third, medial margin; adjusted OR, 2.01; 95% CI, 1.13-3.51; P = .02), definition 10 (location index-based inner one-third, center; adjusted OR, 3.60; 95% CI, 1.49-8.25; P = .003), and definition 12 (location index-based inner one-third, medial margin; adjusted OR, 3.57; 95% CI, 1.91-6.52; P < .001). Definition 12 showed higher interreader agreement than definition 7 (Cohen κ, 0.80 vs 0.66; P = .005). Nevertheless, the sensitivity and positive predictive value of the three definitions were < 50%.

INTERPRETATION

Three definitions exhibited robust associations with occult nodal metastasis. However, selecting candidates for invasive mediastinal staging solely based on a central tumor location would be suboptimal.

A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Background

Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Methods

A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram.

Results

The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768–0.796) in the primary cohort and 0.813 (95% CI, 0.787–0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful.

Conclusion

This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinical stage I NSCLC. Materials and Methods In this retrospective study conducted from May 2020 to October 2020 in a population with clinical stage I NSCLC, an internal cohort was adopted to establish a deep learning signature. Subsequently, the predictive efficacy and biologic basis of the proposed signature were investigated in an external cohort. A multicenter diagnostic trial (registration number: ChiCTR2000041310) was also performed to evaluate its clinical utility. Finally, on the basis of the N2 risk scores, the instructive significance of the signature in prognostic stratification was explored. The diagnostic efficiency was quantified with the area under the receiver operating characteristic curve (AUC), and the survival outcomes were assessed using the Cox proportional hazards model. Results A total of 3096 patients (mean age ± standard deviation, 60 years ± 9; 1703 men) were included in the study. The proposed signature achieved AUCs of 0.82, 0.81, and 0.81 in an internal test set (n = 266), external test cohort (n = 133), and prospective test cohort (n = 300), respectively. In addition, higher deep learning scores were associated with a lower frequency of EGFR mutation (P = .04), higher rate of ALK fusion (P = .02), and more activation of pathways of tumor proliferation (P < .001). Furthermore, in the internal test set and external cohort, higher deep learning scores were predictive of poorer overall survival (adjusted hazard ratio, 2.9; 95% CI: 1.2, 6.9; P = .02) and recurrence-free survival (adjusted hazard ratio, 3.2; 95% CI: 1.4, 7.4; P = .007). Conclusion The deep learning signature could accurately predict N2 disease and stratify prognosis in clinical stage I non-small cell lung cancer. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

Segmentectomy for inner location small-sized non-small-cell lung cancer: Is it feasible?

BACKGROUND

The efficacy of segmentectomy for inner small-sized non-small-cell lung cancer (NSCLC) remains unknown. We aimed to elucidate whether segmentectomy for inner small-sized NSCLC, defined using novel three-dimensional measuring method, yields feasible oncological outcomes compared to segmentectomy for outer lesions.

METHODS

We retrospectively analyzed patients with small-sized (<2cm) cN0 NSCLC who underwent segmentectomy between January 2007 and December 2020. Tumor centrality ratio, which was measured by using three dimensional reconstruction software, was evaluated, with the location of tumor origin confirmed pathologically. Cases with a ratio below and above 2/3 were allocated to the 'Inner group' and 'Outer group', respectively. Oncological outcomes were compared between the two groups.

RESULTS

Our cohort was divided into the 'Inner group' (n=75) and 'Outer group' (n=127). The proximal distance from a tumor exceeded 20 mm in all cases. Tumor centrality ratio was associated with the pathological origin of a tumor. The rate of unforeseen positive lymph node metastasis was significantly higher in the 'Inner group' (p=0.04). There were no significant differences in the 5-year recurrence free survival (RFS; 91% versus 87%, p=0.67). Univariate analysis identified age, consolidation/tumor ratio, the presence of ground-glass-opacity (GGO) and lymphovascular invasion, but not tumor centrality, as significant prognostic factors for RFS. In the multivariate analysis, the presence of GGO and lymphovascular invasion remained significant.

CONCLUSIONS

Regarding oncological outcomes, segmentectomy with a safety proximal distance could be feasible, even for inner small-sized NSCLC. Tumor invasiveness, not tumor centrality, may influence tumor recurrence. (242 words).

Predicting pathological lymph node status in clinical stage IA peripheral lung adenocarcinoma

OBJECTIVES

Even with current diagnostic technology, it is difficult to accurately predict pathological lymph node status (PLNS). This study aimed to develop a prediction model of PLNS in peripheral adenocarcinoma with a dominant solid component, based on clinical and radiological factors on thin-section computed tomography, to identify patients to whom wedge resection or other local therapies could be applied.

METHODS

Of 811 patients enrolled in a prospective multi-institutional study (JCOG0201), 420 patients with clinical stage IA peripheral lung adenocarcinoma having a dominant solid component were included. Multivariable logistic regression was performed to develop a model based on clinical and centrally reviewed radiological factors. Leave-one-out cross-validation and external validation analyses were performed, using independent data from 221 patients. Sensitivity, specificity and concordance statistics were calculated to evaluate diagnostic performance.

RESULTS

The formula for calculating the probability of pathological lymph node metastasis included the following variables: tumour diameter (including ground-glass opacity), consolidation-to-tumour ratio and density of solid component. The concordance statistic was 0.8041. When the cut-off value associated with the risk of incorrectly predicting negative pathological lymph node metastasis (pN-) was 4.9%, diagnostic sensitivity and specificity in predicting PLNS were 95.7% and 46.0%, respectively. The concordance statistic for the external validation set was 0.7972, and diagnostic sensitivity and specificity in predicting PLNS were 95.4% and 40.5%, respectively.

CONCLUSIONS

The proposed model is clinically useful and successfully predicts pN- in patients with clinical stage IA peripheral lung adenocarcinoma with a dominant solid component.

Complex sleeve lobectomy has the same surgical outcome when compared with conventional lobectomy in patients with lung cancer

OBJECTIVES

No significant data are available to assess whether complex sleeve lobectomy (complex-SL) can be considered comparable to conventional lobectomy (CL) in terms of surgical outcome. The purpose of this study was to compare surgical and oncological outcomes of complex-SL with CL in patients with lung cancer.

METHODS

Between 2000 and 2015, a total of 568 patients who underwent open CL (defined as resection of only 1 lobe) and 187 patients who underwent SL were analysed. The SL group was divided into 2 subgroups: standard-SL (bronchial SL, n = 106) and complex-SL (n = 81) (defined as bronchial sleeve resection together with another surgical intervention: bronchovascular SL, n = 40; vascular SL, n = 26; atypical bronchoplasty with resection of more than 1 lobe, n = 12; bronchial SL + chest wall resection, n = 3).

RESULTS

The complex-SL group had more patients with chronic obstructive pulmonary disease (COPD) (25.9% vs 12.5%, P = 0.001), neoadjuvant treatment (39.5% vs 12.0%, P < 0.001), advanced-stage non-small-cell lung cancer (53.2% vs 33.1%, P = 0.001) and low preoperative forced expiratory volume in 1 s (77.2% vs 84.3%, P = 0.004) than the CL group. The overall surgical mortality (in-hospital or 30-day) was 2.6% (n = 20); it was 2.8% for CL and 2.8% for complex-SL. Postoperative complications occurred in 34.9% of the CL group and 39.5% of the complex-SL group (P = 0.413). The pulmonary complication rate was similar between the groups (24.1% for CL, 27.2% for complex-SL, P = 0.552). The 5-year survival in the CL group was 57.1%, and in the complex-SL group it was 56.2% (P = 0.888). Multivariate analysis showed that TNM stage (P < 0.001) and N status (P < 0.001) were significant and independent negative prognostic factors for survival.

CONCLUSIONS

Complex-SL had a comparable outcome to CL, although the complex-SL group had more patients with advanced-stage NSCLC, low preoperative forced expiratory volume in 1 s and COPD.

A Prediction Model to Optimize Invasive Mediastinal Staging Procedures for Non-Small Cell Lung Cancer in Patients With a Radiologically Normal Mediastinum: The Quebec Prediction Model

BACKGROUND

Current guideline-recommended criteria for invasive mediastinal staging in patients with a radiologically normal mediastinum fail to identify a significant proportion of patients with occult mediastinal disease (OMD), despite it leading to a large number of invasive staging procedures.

RESEARCH QUESTION

Which variables available before surgery predict the probability of OMD in patients with a radiologically normal mediastinum?

STUDY DESIGN AND METHODS

We identified all cTxN0/N1M0 non-small cell lung cancer tumors staged by CT imaging and PET with CT imaging in our institution between 2014 and 2018 who underwent gold standard surgical lymph node dissection or were demonstrated to have OMD before surgery by invasive mediastinal staging techniques and divided them into a derivation and an independent validation cohort to create the Quebec Prediction Model (QPM), which allows calculation of the probability of OMD.

RESULTS

Eight hundred three patients were identified (development set, n = 502; validation set, n = 301) with a prevalence of OMD of 9.1%. The developed prediction model included largest mediastinal lymph node size (P < .001), tumor centrality (P = .23), presence of cN1 disease (P = .29), and lesion standardized uptake value (P = .09). Using a calculated probability of more than 10% as a threshold to identify OMD, this model had a sensitivity, specificity, positive predictive value, and negative predictive value in the derivation cohort of 73.9% (95% CI, 58.9%-85.7%), 81.1% (95% CI, 77.2%-84.6%), 28.3% (95% CI, 23.4%-33.8%), and 96.8% (95% CI, 95.0%-98.1%), respectively. It performed similarly in the validation cohort (P = .77, Hosmer-Lemeshow test; P = .5163, Pearson χ² and unweighted sum-of-squares statistics; and P = .0750, Stukel score test) and outperformed current guideline-recommended criteria in identifying patients with OMD (area under the receiver operating characteristic curve [AUC] for American College of Chest Physicians guidelines criteria, 0.65 [95% CI, 0.59-0.71]; AUC for European Society of Thoracic Surgeons guidelines criteria, 0.60 [95% CI, 0.54-0.67]; and AUC for the QPM, 0.85 [95% CI, 0.80-0.90]).

INTERPRETATION

The QPM allows the clinician to integrate available information from CT and PET imaging to minimize invasive staging procedures that will not modify management, while also minimizing the risk of unforeseen mediastinal disease found at surgery.

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.

Predicting Lymph Node Metastasis in Non-small Cell Lung Cancer: Prospective External and Temporal Validation of the HAL and HOMER Models

BACKGROUND

Two models, the Help with the Assessment of Adenopathy in Lung cancer (HAL) and Help with Oncologic Mediastinal Evaluation for Radiation (HOMER), were recently developed to estimate the probability of nodal disease in patients with non-small cell lung cancer (NSCLC) as determined by endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). The objective of this study was to prospectively externally validate both models at multiple centers.

RESEARCH QUESTION

Are the HAL and HOMER models valid across multiple centers?

STUDY DESIGN AND METHODS

This multicenter prospective observational cohort study enrolled consecutive patients with PET-CT clinical-radiographic stages T1-3, N0-3, M0 NSCLC undergoing EBUS-TBNA staging. HOMER was used to predict the probability of N0 vs N1 vs N2 or N3 (N2|3) disease, and HAL was used to predict the probability of N2|3 (vs N0 or N1) disease. Model discrimination was assessed using the area under the receiver operating characteristics curve (ROC-AUC), and calibration was assessed using the Brier score, calibration plots, and the Hosmer-Lemeshow test.

RESULTS

Thirteen centers enrolled 1,799 patients. HAL and HOMER demonstrated good discrimination: HAL ROC-AUC = 0.873 (95%CI, 0.856-0.891) and HOMER ROC-AUC = 0.837 (95%CI, 0.814-0.859) for predicting N1 disease or higher (N1|2|3) and 0.876 (95%CI, 0.855-0.897) for predicting N2|3 disease. Brier scores were 0.117 and 0.349, respectively. Calibration plots demonstrated good calibration for both models. For HAL, the difference between forecast and observed probability of N2|3 disease was +0.012; for HOMER, the difference for N1|2|3 was -0.018 and for N2|3 was +0.002. The Hosmer-Lemeshow test was significant for both models (P = .034 and .002), indicating a small but statistically significant calibration error.

INTERPRETATION

HAL and HOMER demonstrated good discrimination and calibration in multiple centers. Although calibration error was present, the magnitude of the error is small, such that the models are informative.

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.

A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics

Background

Accurately predicting the risk level for a lymph node metastasis is critical in the treatment of non-small cell lung cancer (NSCLC). This study aimed to construct a novel nomogram to identify patients with a risk of lymph node metastasis in T1–2 NSCLC based on positron emission tomography/computed tomography (PET/CT) and clinical characteristics.

Methods

From January 2011 to November 2017, the records of 318 consecutive patients who had undergone PET/CT examination within 30 days before surgical resection for clinical T1–2 NSCLC were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed. The model was confirmed using bootstrap resampling, and an independent validation cohort contained 156 patients from June 2017 to February 2020 at another institution.

Results

Six factors [age, tumor location, histology, the lymph node maximum standardized uptake value (SUVmax), the tumor SUVmax and the carcinoembryonic antigen (CEA) value] were identified and entered into the nomogram. The nomogram developed based on the analysis showed robust discrimination, with an area under the receiver operating characteristic curve of 0.858 in the primary cohort and 0.749 in the validation cohort. The calibration curve for the probability of lymph node metastasis showed excellent concordance between the predicted and actual results. Decision curve analysis suggested that the nomogram was clinically useful.

Conclusions

We set up and validated a novel and effective nomogram that can predict the risk of lymph node metastasis for individual patients with T1–2 NSCLC. This model may help clinicians to make treatment recommendations for individuals.

Geometrical Measurement of Central Tumor Location in cT1N0M0 NSCLC Predicts N1 but Not N2 Upstaging

BACKGROUND

In patients with non-small cell lung cancer (NSCLC) and normal mediastinum, the central tumor location predicts occult nodal disease (both N1 and N2). We evaluated a novel definition of central location based on a geometrical measurement of the tumor location within the lung that could predict N2, N1, or both.

METHODS

This retrospective study included patients with confirmed NSCLC, radiologically and metabolically staged T1 N0 M0, who underwent invasive mediastinal staging and/or lung resection. The central tumor location was measured considering 2 ratios. The inner margin ratio (IMR) and outer margin ratio (OMR) were both calculated as the distance from the inner margin of the lung to both margins of the tumor (inner [IMR], outer [OMR]) divided by the lung width. Optimal cutoffs for IMR and OMR were calculated. Tumors with values lower than the cutoffs were considered central. Prevalences of N1 and N2 upstaging were estimated and bivariate logistic regression analysis was performed to predict the odds of N1 and N2 upstaging using IMR and OMR cutoffs.

RESULTS

A total of 209 patients were included. The prevalence of N1 and N2 upstaging was 11% and 5.3%, respectively. Cutoffs of 0.5 for IMR and 0.64 for OMR were estimated. Both ratios predicted N1 upstaging (adjusted odds ratio [95% confidence interval]: 4.2 [1.5-12]; P < .007; area under the curve, 0.65) but did not predict N2 upstaging.

CONCLUSIONS

Central tumor location can be assessed by means of IMR and OMR and predicts N1 upstaging in patients with radiologically and metabolically T1 N0 M0 tumors. This is important for the selection of patients for therapies that require N0 tumors.

Recent and Current Advances in FDG-PET Imaging within the Field of Clinical Oncology in NSCLC: A Review of the Literature

Lung cancer is the leading cause of cancer-related deaths around the world, the most common type of which is non-small-cell lung cancer (NSCLC). Computed tomography (CT) is required for patients with NSCLC, but often involves diagnostic issues and large intra- and interobserver variability. The anatomic data obtained using CT can be supplemented by the metabolic data obtained using fluorodeoxyglucose F 18 (FDG) positron emission tomography (PET); therefore, the use of FDG-PET/CT for staging NSCLC is recommended, as it provides more accuracy than either modality alone. Furthermore, FDG-PET/magnetic resonance imaging (MRI) provides useful information on metabolic activity and tumor cellularity, and has become increasingly popular. A number of studies have described FDG-PET/MRI as having a high diagnostic performance in NSCLC staging. Therefore, multidimensional functional imaging using FDG-PET/MRI is promising for evaluating the activity of the intratumoral environment. Radiomics is the quantitative extraction of imaging features from medical scans. The chief advantages of FDG-PET/CT radiomics are the ability to capture information beyond the capabilities of the human eye, non-invasiveness, the (virtually) real-time response, and full-field analysis of the lesion. This review summarizes the recent advances in FDG-PET imaging within the field of clinical oncology in NSCLC, with a focus on surgery and prognostication, and investigates the site-specific strengths and limitations of FDG-PET/CT. Overall, the goal of treatment for NSCLC is to provide the best opportunity for long-term survival; therefore, FDG-PET/CT is expected to play an increasingly important role in deciding the appropriate treatment for such patients.

Resectable IIIA-N2 Non-Small-Cell Lung Cancer (NSCLC): In Search for the Proper Treatment

Locally advanced non-small cell lung cancer accounts for one third of non-small cell lung cancer (NSCLC) at the time of initial diagnosis and presents with a wide range of clinical and pathological heterogeneity. To date, the combined multimodality approach involving both local and systemic control is the gold standard for these patients, since occult distant micrometastatic disease should always be suspected. With the rapid increase in treatment options, the need for an interdisciplinary discussion involving oncologists, surgeons, radiation oncologists and radiologists has become essential. Surgery should be recommended to patients with non-bulky, discrete, or single-level N2 involvement and be included in the multimodality treatment. Resectable stage IIIA patients have been the subject of a number of clinical trials and retrospective analysis, discussing the efficiency and survival benefits on patients treated with the available therapeutic approaches. However, most of them have some limitations due to their retrospective nature, lack of exact pretreatment staging, and the involvement of heterogeneous populations leading to the awareness that each patient should undergo a tailored therapy in light of the nature of his tumor, its extension and his performance status.

Age-different extent of resection for clinical IA non-small cell lung cancer: analysis of nodal metastasis

Whether age has any impact on the risk of lymph node (LN) metastasis in patients with early-stage non-small cell lung cancer (NSCLC) remains controversial. Therefore, we aimed to objectively compare the risk of LN metastasis between elderly and young patients so as to justify for age-different extent of surgical resection for treating these patients. We retrospectively collected clinical data of patients undergoing lobectomy or segmentectomy with systematic hilar and mediastinal LN dissection for clinical stage IA peripheral NSCLC from January 2015 to December 2018. Both multivariate logistic regression analysis and propensity score-matched (PSM) analysis were applied to compare the risk of LN metastasis between elderly (>65 years old) and young (≤65 years old) patients. We finally included a total of 590 patients for analysis (142 elderly patients and 448 young patients). In the analysis of unmatched cohorts, young patients tended to have higher rates of hilar/intrapulmonary LN (13.4% VS 9.2%) and mediastinal LN metastasis (10.5% VS 6.3%) than elderly patients. In the multivariate analysis, age was found to be an independent predictor of both hilar/intrapulmonary (Odds ratio(OR) = 2.065, 95%confidence interval(CI): 1.049–4.064, P = 0.036) and mediastinal (OR = 2.400, 95%CI: 1.083–5.316, P = 0.031) LN metastasis. Moreover, in the analysis of well-matched cohorts generated by PSM analysis, young patients had significantly higher rates of hilar/intrapulmonary (18.8% VS 9.4%, P = 0.039) and mediastinal LN metastasis (17.1% VS 6.0%, P = 0.008) than elderly patients. Therefore, age remains to be an independent predictor of LN metastasis in early-stage NSCLC and age-different extent of surgical resection may be justified for these patients.

Development and validation of a preoperative noninvasive predictive model based on circular tumor DNA for lymph node metastasis in resectable non-small cell lung cancer

Background

Clinical lymph node staging in resectable non-small cell lung cancer (NSCLC) patients not only indicates prognosis, but also determines primary treatment strategy. The demand of noninvasive tool for preoperative lymph node metastasis prediction remains significant. This study aimed to develop and externally validate a preoperative noninvasive predictive model based on circular tumor DNA (ctDNA) for the lymph node metastasis in resectable NSCLC patients.

Methods

Resectable NSCLC patients in TRACERx cohort were included as training group. Potential preoperative noninvasively accessible predictors were incorporated into the development of a nomogram via multivariate logistic regression. The predictive model was externally validated by a similar cohort from our hospital.

Results

Overall, 58 patients from TRACERx cohort were included as training group and 37 patients from our hospital were included as external validation group. Variant allele frequency (VAF) level of ctDNA was significantly associated with lymph node metastasis (OR: 4.89, 95% CI: 1.22–19.54, P=0.03). The predictive model incorporating age, tumor size and VAF demonstrated satisfactory discrimination and calibration in both training group (AUC =0.77, 95% CI: 0.65–0.90, P=0.001) and external validation group (AUC =0.84, 95% CI: 0.70–0.99, P=0.005).

Conclusions

High VAF level in preoperative ctDNA may indicate lymph node metastasis of resectable NSCLC. And a preoperative noninvasive predictive model based on ctDNA for the lymph node metastasis in resectable NSCLC patients was developed and externally validated with satisfactory discrimination and calibration.

A Prediction Model to Help with Oncologic Mediastinal Evaluation for Radiation: HOMER

Rationale: When stereotactic ablative radiotherapy is an option for patients with non–small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2|3) disease is important.

Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2|3 disease.

Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound–guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2|3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals.

Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography–computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2|3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P < 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34).

Conclusions: This prediction model can estimate the probability of N0, N1, and N2|3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.

Preoperative predictors of lymph node metastasis in clinical T1 adenocarcinoma

Background

The subcategory “solid component of tumor” is a new criterion of tumor categories in the updated eighth edition of the TNM classification. Nevertheless, the predictors of lymph node metastasis among patients with clinical T1 adenocarcinoma, based on the TNM classification 8^th edition, remain unclear. This study aimed to identify the preoperative predictors of lymph node metastasis in clinical T1 adenocarcinoma by comparing clinicopathological characteristics between the groups with and without lymph node metastasis.

Methods

We performed a retrospective observational single-center study at the Sendai Kousei Hospital. From January 2012 to September 2019, we included 515 patients who underwent curative lobectomy or segmentectomy and mediastinal lymph node dissection among those with clinical T1 adenocarcinoma according to the UICC-TNM staging 8^th edition. They were divided into two groups: those with lymph node metastasis (positive group) and those without (negative group). The clinicopathological factors were retrospectively analyzed and compared between the groups.

Results

In univariate analysis, carcinoembryonic antigen (>5.0 ng/mL) (P=0.0007), maximum standardized uptake (>3.5) (P<0.0001), clinical T factor (T1c) (P<0.0001), and consolidation tumor ratio (>0.85) (P<0.0001) were significant predictors of lymph node metastasis. Multivariate analysis revealed that maximum standardized uptake SUVmax (>3.5) (odds ratio =10.4, P<0.0001) was independently associated with lymph node metastasis. In univariate analysis, carcinoembryonic antigen (>5.0) (P=0.048) was the only predictor of lymph node metastasis among patients of cT1b, while no parameters were identified as significant predictors among patients of cT1c.

Conclusions

SUVmax and CEA are useful preoperative predictors of lymph node metastases in patients with clinical T1 adenocarcinoma, stratified to T1b and T1c, based on the 8^th TNM classification.

CT-based radiomics signature for the stratification of N2 disease risk in clinical stage I lung adenocarcinoma

Background

Risk stratification of N2 disease is vital for selecting candidates to receive invasive mediastinal staging modalities. In this study, we aimed to stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma using radiomics analysis.

Methods

Two datasets of patients with clinical stage I lung adenocarcinoma who underwent lung resection were included (training dataset, 880; validation dataset, 322). Using PyRadiomics, 1,078 computed tomography (CT)-based radiomics features were extracted after semi-automated lung nodule segmentation. In order to predict N2 status, a radiomics signature was constructed after selecting the optimal radiomics feature subset by sequentially applying minimum-redundancy-maximum-relevance and least absolute shrinkage and selection operator (LASSO) techniques. Its performance was validated in the validation dataset.

Results

The incidences of N2 metastasis were 8.4% and 7.1% in the training and validation datasets, respectively. Unsupervised cluster analysis revealed that radiomics features significantly correlated with lymph node status and pathological subtypes. For N2 disease prediction, five radiomics features were selected to establish the radiomics signature, which showed a significantly better predictive performance than clinical factors (P<0.001). The area under the receiver operating characteristic curve was 0.81 (0.77-0.86) and 0.69 (0.63-0.75) for radiomics signature and clinical factors, respectively, in the training dataset, and 0.82 (0.71-0.92) and 0.64 (0.52-0.75), respectively, in the validation dataset.

Conclusions

The established CT-based radiomics signature could stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma, thus assisting clinicians in making patient-specific mediastinal staging strategy.

Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

Background

Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information.

Methods

We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models.

Results

Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models.

Conclusion

In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

The utility of endosonography for mediastinal staging of non-small cell lung cancer in patients with radiological N0 disease

OBJECTIVES

Recent practice guidelines recommend endosonography for patients with radiological N0 non-small cell lung cancer (NSCLC) when the primary tumors are >3 cm in diameter or centrally located. However, any role for endosonography remains debatable. We evaluated the utility of endosonography in patients with radiological N0 NSCLC based on tumor centrality, diameter and histology.

MATERIALS AND METHODS

Patients who underwent staging endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) with or without transesophageal bronchoscopic ultrasound-guided fine needle aspiration (EUS-B-FNA) for radiological N0 NSCLC were retrospectively investigated using prospectively collected endosonography data. The radiological N0 stage was defined by node diameter as evident on computed tomography images and ¹⁸F-FDG uptake using integrated positron emission tomography-computed tomography.

RESULTS

In total of 168 patients, the median size of the primary tumor was 39 mm, and 41 % of tumors were centrally located. The prevalence of occult mediastinal metastases was 11.3 % (19/168). The sensitivity of endosonography in terms of diagnosing occult mediastinal metastases was only 47 % (9/19); 6 of 10 patients with false-negative endosonography data exhibited metastases in accessible nodes. The diagnostic performance of endosonography did not differ by tumor centrality or diameter. Patients with adenocarcinoma histology showed higher prevalence of occult mediastinal metastases and higher false-negative results in endosonography compared with those with non-adenocarcinoma histology.

CONCLUSION

Not all patients with radiological N0 NSCLC benefit from endosonography, given the low prevalence of occult mediastinal metastases and the poor sensitivity of endosonography in this population. The strategy of invasive mediastinal staging needs to be tailored considering the histology of the tumor in this population.

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.

Risk analyses of N2 lymph-node metastases in patients with T1 non-small cell lung cancer: a multi-center real-world observational study in China

PURPOSE

N2 lymph-node metastases occur in approximately 6-17% of the patients with T1-2 non-small cell lung cancer (NSCLC). However, the clinical characteristics of N2 patients are not fully understood.

METHODS

This retrospective, multi-center analysis included T1 NSCLC patients receiving surgical resection during a period from Jan 2nd, 2014 to Dec 27th, 2017. The diagnosis was pathologically verified in all cases. Univariate and multivariate logistic regression analyses were conducted to analyze the factors that are associated with pN2 lymph-node metastases.

RESULTS

A total of 10,885 patients (48.4% men; 84.7% adenocarcinoma) were included in the analysis. The mean age was 59.0 ± 9.9 years. The mean tumor size was 1.8 ± 0.8 cm. Of the patients, 3260 (29.9%) were smokers or ex-smokers. Lymph-node metastases were verified in 1808 (16.6%) patients, and 1167 (10.7%) patients had N2 lymph-node metastases. The multivariate analyses indicated that larger tumor size, lower differentiation, CEA level ≥ 5 ng/mL, vascular invasion (+), and pleural involvement (+) were associated with higher percentages of N2 lymph-node metastases (p < 0.001 for all).

CONCLUSIONS

This study demonstrated the significant association between N2 lymph-node metastases and tumor size and differentiation, CEA levels, and status of vascular invasion and pleural involvement.

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.

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.

Lymph node metastasis in Chinese patients with clinical T1 non-small cell lung cancer: A multicenter real-world observational study

Background

Approximately 8.3–15.9% of patients with clinical stage I non‐small cell lung cancer are subsequently shown to have lymph node metastasis. However, the clinical characteristics of patients with lymph node metastasis in China are not fully understood.

Methods

This is a multicenter retrospective analysis of pathological T1 non‐small cell lung cancer patients who underwent surgical resection from 2 January 2014 to 27 December 2017. Clinical and pathological information was collected with the assistance of the Large‐scale Data Analysis Center of Cancer Precision Medicine‐LinkDoc database. The clinical and pathological factors associated with lymph node metastasis were analyzed by univariate and multivariate logistic regression.

Results

A total of 10 885 participants (51.6% women; 15.3% squamous cell carcinoma) were included in the analysis. The median age was 60.0 years (range 12.9–86.6 years). A total of 1159 patients (10.6%) had metastases in mediastinal nodes (N2), and 640 patients (5.9%) had metastasis in pulmonary lymph nodes (N1). Most patients had T1b lung cancer (4766, 43.8%). Of the patients, 3260 (29.9%) were current or former smokers. The univariate and multivariate analyses showed that younger age, squamous cell carcinoma, poor differentiation, larger tumor size, carcinoembryonic antigen level ≥5 ng/mL, and vascular invasion (+) were significantly associated with higher percentages of lymph node metastases (P < 0.001 for all).

Conclusion

This real‐world study showed the significant association of lymph node metastasis with age, tumor size, histology and differentiation, carcinoembryonic antigen levels, and status of vascular invasion. Female patients with T1a adenocarcinoma in the right upper lobe barely had lymph node metastasis.

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.

Young age increases risk for lymph node positivity but decreases risk for non-small cell lung cancer death

Non-small cell lung cancer (NSCLC) prognosis and risk of lymph node positivity (LN+) are reference points for reasonable treatments. The aim of the current study was to investigate the effect of age on LN+ and NSCLC death. Data from the Surveillance, Epidemiology, and End Results (SEER) registry were used to identify 82,253 patients with NSCLC diagnosed between 1988 and 2008. All the patients underwent standard lung cancer surgery with lymph node examination. Demographic and clinicopathological parameters were extracted and compared among each age group. Impact of age on LN+ and NSCLC death was evaluated by the Cochran-Armitage trend test and logistic univariate and multivariate analyses for all T stages. Overall, 22,711 (27.60%) patients of the entirety had lymph node metastasis and 28,968 (35.22%) patients died of NSCLC within 5 years. With the increase in age, LN+ rates decreased regardless of T stages (P<0.001), whereas NSCLC-specific mortality increased in stages T1-T3 (P<0.001). Controlling other covariates in multivariable logistic regression, age remained an independent risk factor for LN+ in all T stages (P<0.05) and in stages T1-T3 (P<0.05). Our SEER analysis demonstrated a higher rate of LN+ and lower mortality in younger patients with NSCLC, after accounting for other covariates.

Incidence of occult pN2 disease following resection and mediastinal lymph node dissection in clinical stage I lung cancer patients

Objectives

Early clinical stage (T1 and T2) non-small cell lung cancer (NSCLC) is commonly treated with anatomic lung resection and lymph node sampling or dissection. The aims of this study were to evaluate the incidence and the distribution of occult N2 disease according to tumour location and the short- and long-term outcomes.

Methods

We performed a retrospective review of patients with clinical stage I NSCLC who underwent anatomic lung resection and lymphadenectomy. Mediastinal lymphadenectomy (ML) was defined as resection of at least 2 mediastinal stations, always including station 7 lymph nodes. Patients who had a lobe-specific lymphadenectomy were excluded.

Results

One thousand six hundred and sixty-seven consecutive patients met inclusion criteria and were included. Overall, 9% (146/1667) of the patients had occult pN2 disease. At multivariable analysis, adenocarcinoma histology and vascular invasion were independently associated with greater risk of occult pN2 disease. In left and right upper lobe tumours, station 7 nodes were involved in 5 and 13% of pN2 positive cases, respectively. Station 5 and station 2/4 nodes were involved in 29 and 18% of left and right lower lobe pN2 tumours, respectively. There was no postoperative mortality, and postoperative morbidity was 28%. The median overall survival was 77.4 months. N0 patients had a median overall survival of 83.7 months vs 48.0 months and 37.9 months in N1 and N2 populations, respectively ( P  < 0.001).

Conclusions

Sixteen percent of pN2 patients had mediastinal lymph node metastasis beyond the lobe-specific lymphatic drainage. We recommend a complete lymphadenectomy be performed, even in clinical stage I NSCLC.

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.

Clinicopathological Characteristics of Lung Adenocarcinoma with Unexpected Lymph Node Metastasis

PURPOSE

The objective is to demonstrate the clinicopathological characteristics of patients with unexpected node-positive lung adenocarcinoma and to analyze predictive factors of unexpected disease.

METHODS

We reviewed 225 patients with lung adenocarcinoma who underwent curative-intent operation between January 2008 and December 2014. Unexpected node-positive diseases were defined as cases with hilar or mediastinal lymph nodes metastasis in spite of both negative significant enlargement of lymph nodes on preoperative chest computed tomography (CT) and negative fluorodeoxyglucose (FDG) uptake in lymph nodes on preoperative positron emission tomography (PET)/CT. We retrospectively analyzed clinical features of these patients and evaluated associated factors for unexpected diseases.

RESULTS

There were 41 patients (18%) with unexpected node-positive disease, consisting of 16 (39%) unexpected pN1 and 25 (61%) unexpected pN2 diseases. The most common predominant subtype was papillary (22 patients; 54%), and 17 patients (41%) had micropapillary component in the tumors. Younger age (p <0.01), left side (p <0.01), larger tumor size (p <0.01), and having a micropapillary component (p <0.01) were significant associated factors of unexpected diseases in multivariate analysis.

CONCLUSION

Histological findings of the primary tumor are often important because they can provide predictive information for lymph nodes status. Having a micropapillary component was one of the significant predictors of unexpected node-positive diseases.

A Model Predicting Lymph Node Status for Patients with Clinical Stage T1aN0-2M0 Nonsmall Cell Lung Cancer

Background:

Lymph node status of patients with early-stage nonsmall cell lung cancer has an influence on the choice of surgery. To assess the lymph node status more correspondingly and accurately, we evaluated the relationship between the preoperative clinical variables and lymph node status and developed one model for predicting lymph node involvement.

Methods:

We collected clinical and dissected lymph node information of 474 patients with clinical stage T1aN0-2M0 nonsmall cell lung cancer (NSCLC). Logistic regression analysis of clinical characteristics was used to estimate independent predictors of lymph node metastasis. The prediction model was validated by another group.

Results:

Eighty-two patients were diagnosed with positive lymph nodes (17.3%), and four independent predictors of lymph node disease were identified: larger consolidation size (odds ratio [OR] = 2.356, 95% confidence interval [CI]: 1.517–3.658, P < 0.001,), central tumor location (OR = 2.810, 95% CI: 1.545–5.109, P = 0.001), abnormal status of tumor marker (OR = 3.190, 95% CI: 1.797–5.661, P < 0.001), and clinical N1–N2 stage (OR = 6.518, 95% CI: 3.242–11.697, P < 0.001). The model showed good calibration (Hosmer–Lemeshow goodness-of-fit, P < 0.766) with an area under the receiver operating characteristics curve (AUC) of 0.842 (95% [CI]: 0.797–0.886). For the validation group, the AUC was 0.810 (95% CI: 0.731–0.889).

Conclusions:

The model can assess the lymph node status of patients with clinical stage T1aN0-2M0 NSCLC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision-making procedure.