Novel approach for predicting occult lymph node metastasis in peripheral clinical stage I lung adenocarcinoma

Low expression of lysosome-related genes KCNE1, NPC2, and SFTPD promote cancer cell proliferation and tumor associated M2 macrophage polarization in lung adenocarcinoma

Background

Recent research has shown that lysosomes play a critical role in the onset and progression of malignancy by regulating tumor cell death through several mechanisms. Nevertheless, the involvement of lysosome-associated genes (LSAGs) in lung adenocarcinoma (LUAD) is still not well understood.

Methods

LSAGs were identified in malignant lung epithelial cells, as well as biologically and functionally annotated by the comprehensive integration of single-cell and bulk RNA-sequencing data. Prognostic characterization of LSAGs was established, of which the accuracy and reliability were assessed by one-way Cox and LASSO regression. Correlations between LSAG properties and immune cell infiltration, chemotherapy, and immunotherapy were analyzed by integrated omics data. Finally, we characterized the expression of three LSAGs (KCNE1, NPC2, and SFTPD) in malignant lung epithelium and assessed their impact on tumor malignancy related phenotypes.

Results

We identified 18 LSAGs associated with prognosis, of which 3 LSAGs were used to construct prognostic models. High-risk patients had worse survival and the model predicted it better than other clinical indicators. Based on the functional enrichment analyses, LSAGs were associated with binding and molecular activity functions, inhibition of DNA damage repair and tumor growth, IL7 signaling pathway, and glycolysis. M0 macrophages and M1 macrophages were substantially enriched in high-risk patients. Conversely, there was a considerable enrichment of resting dendritic cells and M2 macrophages in patients at low risk. We also found that risk scores predicted the outcome of immunotherapy. In vitro, we found that KCNE1, NPC2, and SFTPD were lowly expressed in malignant epithelial cells and patients with low expression of KCNE1, NPC2, and SFTPD had a higher percentage of M2 macrophage infiltration. Overexpression of KCNE1, NPC2, and SFTPD suppressed the proliferation and invasion of malignant cells, and M0 macrophages remarkably reduced M2 macrophage polarization and cellular secretion of pro-tumor cytokines.

Conclusions

We used three LASGs-KCNE1, NPC2, and SFTPD-to develop and validate a predictive signature for LUAD patients. Furthermore, we found that low expression of KCNE1, NPC2, and SFTPD promotes lung cancer cell proliferation and invasion and M2 macrophage polarization. Our study may provide fresh perspectives for customized immunotherapy.

Is tumor diameter a risk factor for occult N1 metastasis in patients with peripheral non-small cell lung cancer which is smaller than 3 cm?

The optimum treatment option is surgery for clinical early stage non-small cell lung cancer. Despite all non-invasive and invasive staging effort, occult lymph-node metastasis can be detected in pathological staging. Here, we investigated whether there was any correlation between tumor diameter and occult lymph-node metastasis in N1 stations. Data of patient with non-small cell lung cancer clinical stage 1A were reviewed retrospectively. Those with tumor diameter smaller than 3 cm and pN0-pN1 in pathological staging were included in the study. Overall survival (OS) was calculated by Kaplan-Meier and survival differences between pN0 and pN1 groups were investigated by Log-Rank methods. Cut-off value of tumor diameter for lymph-node metastasis was investigated by Receiver-Operating Characteristics test. Significance between pN0-pN1 and other categorical groups was investigated with Pearson Chi-square or Fisher's exact tests. A total of 257 patients meet to criteria included in the study. Fifty-five (21.4%) of the patients were females. The mean age was 62.7 ± 8.5 and median tumor diameter was 20 mm (Range: 2-30 mm). We detected occult lymph-node metastasis at the N1 stations (pN1) in 33 patients (12.8%) in histopathological examination of resected specimens and lymph-node dissection materials. The cut-off value of tumor diameter was calculated as 21.5 mm for occult lymph-node metastasis by Receiver-Operating Characteristics analysis (Area Under Curved: 70.1%, p = 0.004). There was a significant correlation between pN1 positivity and high tumor diameter (p = 0.02). However, we did not find a correlation between the lymph-node metastasis and age, gender, tumor histopathology, tumor localization, and visceral pleural invasion. Tumor diameter may be an indicator for occult lymph-node metastasis in patients with clinical stage-1A non-small cell lung cancer. This result should be considered in patient with mass which larger than 21.5 mm and planned stereotactic body radiotherapy instead of surgery.

Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer

Objective

This study aimed to establish a predictive model for occult lymph node metastasis (LNM) in patients with clinical stage I-A non-small cell lung cancer (NSCLC) based on contrast-enhanced CT.

Methods

A total of 598 patients with stage I-IIA NSCLC from different hospitals were randomized into the training and validation group. The "Radiomics" tool kit of AccuContour software was employed to extract the radiomics features of GTV and CTV from chest-enhanced CT arterial phase pictures. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to reduce the number of variables and develop GTV, CTV, and GTV+CTV models for predicting occult lymph node metastasis (LNM).

Results

Eight optimal radiomics features related to occult LNM were finally identified. The receiver operating characteristic (ROC) curves of the three models showed good predictive effects. The area under the curve (AUC) value of GTV, CTV, and GTV+CTV model in the training group was 0.845, 0.843, and 0.869, respectively. Similarly, the corresponding AUC values in the validation group were 0.821, 0.812, and 0.906. The combined GTV+CTV model exhibited a better predictive performance in the training and validation group by the Delong test (p<0.05). Moreover, the decision curve showed that the combined GTV+CTV predictive model was superior to the GTV or CTV model.

Conclusion

The radiomics prediction models based on GTV and CTV can predict occult LNM in patients with clinical stage I-IIA NSCLC preoperatively, and the combined GTV+CTV model is the optimal strategy for clinical application.

Tumour location predicts occult N1 nodal metastasis in clinical stage I non-small-cell lung cancer

OBJECTIVES

Pathological lymph node metastases are often observed in patients with clinical N0 lung cancer. Identifying preoperative predictors of occult hilar nodal metastasis (OHNM) is important in determining the surgical procedure in patients with clinical stage I non-small-cell lung cancer. This study aimed to determine the frequency and predictors of OHNM by tumour location in these patients.

METHODS

Between April 2007 and May 2019, data of patients who underwent lobectomy or segmentectomy for clinical stage I pure-solid non-small-cell lung cancer were retrospectively reviewed. The ratio of the distance from the pulmonary hilum to the proximal side of the tumour to the distance from the pulmonary hilum to the visceral pleural surface through the centre of the tumour, named 'distance ratio (DR)', was calculated. The relationship of the DR with clinicopathological findings and prognosis was discussed.

RESULTS

A total of 357 patients were enrolled. OHNM frequency was 14.6%. Patients were divided into 2 groups based on whether the DR was ≤0.67 (central type) or >0.67 (peripheral type). The frequency of OHNM was significantly higher in the DR ≤0.67 group (21.5% vs 7.4%; P < 0.001). Multivariable analysis revealed that DR was the only independent preoperative predictor of OHNM (odds ratio, 3.63; 95% confidence interval, 1.83-7.18; P < 0.001).

CONCLUSIONS

The frequency of OHNM was significantly lower in peripheral-type lung cancer; therefore, tumour location was the most important preoperative predictor of OHNM.

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study

Background

Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non–small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use.

Objective

This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms.

Methods

We developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short axis diameter of lymph node >10 mm was regarded as a metastatic node) and clinician’s evaluation. Since the NLP model may extract features with mistakes, we also calculated the concordance correlation between the predicted probabilities of models using NLP-extracted features and gold standard features to explore the influence of NLP-driven automatic extraction.

Results

Experimental results show that the random forest models achieved the best performances with 0.792 area under the receiver operating characteristic curve (AUC) value and 0.456 average precision (AP) value for pN2 LNM prediction and 0.768 AUC value and 0.524 AP value for pN1&N2 LNM prediction. And all machine learning models outperformed the size criteria and clinician’s evaluation. The concordance correlation between the random forest models using NLP-extracted features and gold standard features is 0.950 and improved to 0.984 when the top 5 important NLP-extracted features were replaced with gold standard features.

Conclusions

The LNM models developed can achieve competitive performance using only limited EMR data such as CT reports and tumor markers in comparison with the clinician’s evaluation. The multiturn question answering NLP model can extract features effectively to support the development of LNM prediction models, which may facilitate the clinical application of predictive models.

Mandatory Nodal Evaluation During Resection of Clinical T1a Non-Small-Cell Lung Cancers

BACKGROUND

Recommendations for intraoperative lymph node evaluation are uniform regardless of whether a primary tumor is clinical T1a or T2a according to TNM 8^th edition for Stage I Non-Small-Cell lung cancers (NSCLC). We quantified nodal disease risk in patients with T1a disease (≤1cm).

METHODS

The National Cancer Database was queried for clinical T1aN0M0 primary NSCLCs ≤1cm undergoing lobectomy with mediastinal nodal evaluation from 2004-2014. Nodal disease risk was analyzed as a function of demographics and tumor characteristics.

RESULTS

Among 2,157 cases, 6.7% had occult nodal disease: 5.1% occult N1 and 1.6% N2. Adenocarcinoma (7.5%), large cell carcinoma (25%), and poor differentiation (11.8%) or undifferentiated/anaplastic (25.0%) had high rates of combined pN1 and N2 disease (p<0.001). In univariable analysis, odds of pathologic N1, N2, or N1/N2 nodal disease with respect to N0 was greatest for large cell carcinoma (ref. adenocarcinoma Odds Ratio (OR): 4.31, 3.62, 4.12 respectively; all p<0.05), and anaplastic grade (OR: 10.71, 13.09, 11.55). Bronchoalveolar adenocarcinomas had the lowest odds (OR 0.41, 0.11, 0.32) and squamous cell carcinoma had lower odds for N2 (OR 0.29, all p<0.05). In multivariable analysisonly bronchoalveolar adenocarcinomas had lower odds of pathologic N2 and N1/N2 disease with respect to N0. Worsening grade remained significant for pathologic N1 and N1/N2 disease (both p<0.05).

CONCLUSIONS

A significant rate (6.7%) of occult nodal disease is present in primary NSCLCs ≤1cm. Risk increases with certain histology and worsening grade. We recommend mandatory systematic hilar and mediastinal nodal evaluation for T1a NSCLC tumors for accurate staging and adjuvant therapy.

Integrative nomogram of intratumoral, peritumoral, and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas

Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60–0.88), 0.72 (95% CI 0.57–0.87) and 0.64 (95% CI 0.48–0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61–0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61–0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66–0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Managing screening-detected subsolid nodules-the Asian perspective

The broad application of low-dose computed tomography (CT) screening has resulted in the detection of many small pulmonary nodules. In Asia, a large number of these detected nodules with a radiological ground glass pattern are reported as lung adenocarcinomas or premalignant lesions, especially among female non-smokers. In this review article, we discuss controversial issues and conditions involving these subsolid pulmonary nodules that we often face in Asia, including a lack or insufficiency of current guidelines; the roles of preoperative biopsy and imaging; the location of lesions; appropriate selection of localization techniques; the roles of dissection and sampling of frozen sections and lymph nodes; multifocal lesions; and the roles of non-surgical treatment modalities. For these complex issues, we have tried to present up-to-date evidence and our own opinions regarding the management of subsolid nodules. It is our hope that this article helps surgeons and physicians to manage the complex issues involving ground glass nodules (GGNs) in a balanced manner in their daily practice and provokes further discussion towards better guidelines and/or algorithms.

F-18 fluorodeoxyglucose uptake in lymph nodes and sonographic features on endobronchial ultrasonography predict lymph node metastasis in lung cancer patients

Background

Sonographic findings of lymph nodes on endobronchial ultrasonography (EBUS) images have been reported to be useful to predict lymph node metastasis (LNM) in lung cancer patients. F-18 fluorodeoxyglucose (FDG) uptake in lymph nodes was also found to be useful. In this study, we aimed to clarify whether a combination of sonographic features and maximum standardized uptake values of lymph nodes (LN-SUVmax) is useful for predicting LNM in lung cancer patients.

Methods

From January 2014 to December 2019, a total of 147 lymph nodes from 104 patients with lung cancer, who underwent preoperative EBUS and FDG-positron emission tomography (PET)/computed tomography (CT) followed by surgery were retrospectively assesses. The characteristics of the patients, LN-SUVmax, and sonographic findings of lymph nodes were reviewed. Predictive factors associated with LNM were identified using the logistic regression model.

Results

The average size of the lymph nodes was 8.55 (range, 3–22) mm and the average LN-SUVmax was 5.36 (range, 1.79–31.19). The prevalence of nodal metastasis was 26/147 (17.4%), including 22 in mediastinal lymph nodes and 4 in hilar lymph nodes. Multivariate analysis demonstrated four independent predictive factors for LNM; size, round or oval shape, absence of a central hilar structure, and LN-SUVmax. The optimal cutoff value for lymph node size and LN-SUVmax were 10 mm and 6.00, respectively. By combinating of the two modalities, we obtained the results with sensitivity of 76.9%, specificity of 95.1% and accuracy of 93.2%.

Conclusions

A combination of sonographic findings and LN-SUVmax showed a higher diagnostic rate of LNM than either modality alone in lung cancer 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.

Both the presence of a micropapillary component and the micropapillary predominant subtype predict poor prognosis after lung adenocarcinoma resection: a meta-analysis

Objective

It has been confirmed that the micropapillary (MP) pattern is a poor prognostic factor after resection of lung adenocarcinoma (ADC), but the proportion of the MP component as a prognostic criterion is still controversial. Hence, a meta-analysis was performed to evaluate whether the presence of an MP component has equal prognostic power as the MP predominant subtype.

Methods

Literature retrieval was performed in the MEDLINE, EMBASE, and Cochrane databases until December 23, 2019. Eligible studies were selected based on the inclusion and exclusion criteria. The included studies were divided into two subgroups, the MP component subgroup and the MP predominant subgroup, according to the proportion of the MP pattern to analyse the effect of this pattern on disease-free survival (DFS) and overall survival (OS). The hazard ratio (HR) and 95% confidence interval (CI) were extracted from each study. Review Manager 5.3 was used for statistical analyses.

Results

Finally, 10 studies, including a total of 4934 lung ADC patients, were included in this meta-analysis. Our results indicated a significantly worse pooled DFS (HR 1.62, 95% CI 1.20–2.21) and OS (HR 1.53, 95% CI 1.19–1.96) in the subgroup of MP predominant subtype patients. The pooled DFS (HR 1.80, 95% CI 1.45–2.85) and OS (HR 2.26, 95% CI 1.46–3.52) were also poor in the subgroup of patients with the presence of an MP component.

Conclusions

Both the presence of an MP component and the MP predominant subtype are related to poor DFS and OS after lung ADC resection and represent adverse prognostic factor for lung ADC patients. However, there are some limitations in this meta-analysis, and quantitative stratification based on the proportion of the MP component is needed to explore its effect on prognosis of lung ADC patients in the future.

Preoperative Prediction of Lymph Node Metastasis in Patients With Early-T-Stage Non-small Cell Lung Cancer by Machine Learning Algorithms

Background: Lymph node metastasis (LNM) is difficult to precisely predict before surgery in patients with early-T-stage non-small cell lung cancer (NSCLC). This study aimed to develop machine learning (ML)-based predictive models for LNM.

Methods: Clinical characteristics and imaging features were retrospectively collected from 1,102 NSCLC ≤ 2 cm patients. A total of 23 variables were included to develop predictive models for LNM by multiple ML algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors.

Results: The areas under the ROC curve (AUCs) of the 8 models ranged from 0.784 to 0.899. Some ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 9 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors were tumor size, imaging density, carcinoembryonic antigen (CEA), maximal standardized uptake value (SUV_max), and age.

Conclusions: By incorporating clinical characteristics and radiographical features, it is feasible to develop ML-based models for the preoperative prediction of LNM in early-T-stage NSCLC, and the RFC model performed best.

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.

The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma

Background

Primary pulmonary papillary adenocarcinoma (PA) is a specific and rare subtype of invasive pulmonary adenocarcinoma (ADC). The knowledge concerning the clinicopathologic features and prognosis of patients with primary pulmonary PA has not been clarified because of its rarity.

Methods

The clinical data of a total of 3391 patients with primary pulmonary PA were retrospectively analyzed to confirm their clinical characteristics and factors influencing prognosis and were in comparison with 3236 patients with non- PA pulmonary adenocarcinoma. All patients were histologically diagnosed between 1988 and 2015 in The Surveillance Epidemiology and End Results (SEER) database. A nomogram with satisfactory predictive performance was established to visually predict long-term survival of these patients.

Results and conclusion

Collectively, primary pulmonary PA is a rare pathological cancer and its prognosis is analogous to that of non-PA pulmonary adenocarcinoma. Older age, larger lesions, distant metastases, lymph node invasion, and poor pathological differentiation are correlative with unacceptable prognosis. Surgical intervention is conducive to reaping favorable prognosis. Unfortunately, radiotherapy or chemotherapy results of no significant effects on patient survival. In our study, a nomogram with prognostic function is formulated to confer individual prediction of overall survival (OS).

Minimizing residual occult nodal metastasis in NSCLC: recent advances, current status and controversies

Introduction: Nodal involvement in lung cancer is a significant determinant of prognosis and treatment management. New evidence exists regarding the management of occult lymph node metastasis and residual disease in the fields of imaging, mediastinal staging, and operative management.Areas covered: This review summarizes the latest body of knowledge on the identification and management of occult lymph node metastasis in NSCLC. We focus on tumor-specific characteristics; imaging modalities; invasive mediastinal staging; and operative management including, technique, degree of resection, and lymph node examination.Expert opinion: Newly identified risk-factors associated with nodal metastasis including tumor histology, location, radiologic features, and metabolic activity are not included in professional societal guidelines due to the heterogeneity of their reporting and uncertainty on how to adopt them into practice. Imaging as a sole diagnostic method is limited. We recommend confirmation with invasive mediastinal staging. EBUS-FNA is the best initial method, but adoption has not been uniform. The diagnostic algorithm is less certain for re-staging of mediastinal nodes after neoadjuvant therapy. Mediastinal node sampling during lobectomy remains the gold-standard, but evidence supports the use of minimally invasive techniques. More study is warranted regarding sublobar resection. No consensus exists regarding lymph node examination, but new evidence supports reexamination of current quality metrics.