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

Systematic mediastinal staging in non-small cell lung cancer: Filling in the guideline evidence gap

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EBUS-TBNA for mediastinal staging of centrally located T1N0M0 non-small cell lung cancer clinically staged with PET/CT

BACKGROUND AND OBJECTIVE

To evaluate the diagnostic accuracy and clinical usefulness of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for mediastinal staging of centrally located T1N0M0 non-small cell lung cancer (NSCLC) clinically staged with positron emission tomography/computed tomography (PET/CT).

METHODS

We conducted a study that included patients with centrally located T1N0M0 NSCLC, clinically staged with PET/CT who underwent EBUS-TBNA for mediastinal staging. Patients with negative EBUS-TBNA underwent mediastinoscopy, video-assisted mediastinoscopic lymphadenectomy (VAMLA) and/or lung resection with systematic nodal dissection, that were considered the gold standard. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), overall accuracy of EBUS-TBNA for diagnosing mediastinal metastases (N2 disease) and the number needed to treat (NNT: number of patients needed to undergo EBUS-TBNA to avoid a case of pathologic N2 disease after resection) were calculated.

RESULTS

One-hundred eighteen patients were included. EBUS-TBNA proved N2 disease in four patients. In the remaining 114 patients who underwent mediastinoscopy, VAMLA and/or resection there were two cases of N2 (N2 prevalence 5.1%). The sensitivity, specificity, NPV, PPV and overall accuracy for diagnosing mediastinal metastases (N2 disease) were of 66%, 100%, 98%, 100% and 98%, respectively. The NNT was 31 (95% CI: 15-119).

CONCLUSION

EBUS-TBNA in patients with central clinically staged T1N0M0 NSCLC presents a good diagnostic accuracy for mediastinal staging, even in a population with low prevalence of N2 disease. Therefore, its indication should be considered in the management of even these early lung cancers.

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.

Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

Background

Based on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).

Methods

Radiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.

Results

Thirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.

Conclusion

CT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.