Mediastinal lymph node staging for lung cancer

Safety and Diagnostic Accuracy of the Transnasal Approach for Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA)

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a safe and accurate diagnostic procedure used for investigating mediastinal pathologies. It is usually performed using an oral approach. The nasal route has been proposed but not extensively investigated. With the aim to report the use of linear EBUS through the nasal route and compare its accuracy and safety with the oral one, we conducted a retrospective analysis of the subjects who underwent an EBUS-TBNA procedure at our center. From January 2020 to December 2021, 464 subjects underwent an EBUS-TBNA, and in 417 patients, EBUS was performed through the nose or mouth. Nasal insertion of the EBUS bronchoscope was performed in 58.5% of the patients. No difference between the two insertion routes was observed in terms of location or number of stations sampled per subject. Procedure complications were mild and similar between the two groups (10.2% for the nasal group vs. 9.8% for the oral group). Minor epistaxis occurred in five subjects in the nasal group. Comparing the two groups, the rates of adequate specimens were similar (95.1% vs. 94.8%), as were the proportions of diagnostic specimens (84% vs. 82%). In conclusion, the nasal route for EBUS-TBNA is a valid alternative to the oral one.

Contribution of F-18 fluorodeoxyglucose PET/CT and contrast-enhanced thoracic CT texture analyses to the differentiation of benign and malignant mediastinal lymph nodes

BACKGROUND

Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues.

PURPOSE

To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and contrast-enhanced computed tomography (CT) texture analyses with machine learning and determine lung cancer subtypes based on the analysis of lymph nodes.

MATERIAL AND METHODS

Suitable texture features were entered into the algorithms. Features that statistically significantly differed between the lymph nodes with small cell lung cancer (SCLC), adenocarcinoma (ADC), and squamous cell carcinoma (SCC) were determined.

RESULTS

The most successful algorithms were decision tree with the sensitivity, specificity, and area under the curve (AUC) values of 89%, 50%, and 0.692, respectively, and naive Bayes (NB) with the sensitivity, specificity, and AUC values of 50%, 81%, and 0.756, respectively, for PET/CT, and NB with the sensitivity, specificity, and AUC values of 10%, 96%, and 0.515, respectively, and logistic regression with the sensitivity, specificity, and AUC values of 21%, 83%, and 0.631, respectively, for CT. In total, 13 features were able to differentiate SCLC and ADC, two features SCLC and SCC, and 33 features ADC and SCC lymph node metastases in PET/CT. One feature differed between SCLC and ADC metastases in CT.

CONCLUSION

Texture analysis is beneficial to discriminate between benign and malignant lymph nodes and differentiate lung cancer subtypes based on the analysis of lymph nodes.