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Introduction: Endobronchial metastases (EBMs) are rare, with primary tumours predominantly of breast, renal, and colorectal origin. Bronchoscopy is the diagnostic gold standard, with histological confirmation through immunohistochemical study.

Cases: We presented three cases of EBMs, one secondary to colorectal cancer and two associated with renal tumours.

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Background Early staging of lung carcinoma (CA) is pivotal in planning the treatment. Lymph node metastasis can be detected by imaging and invasive procedures. The 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is an emerging noninvasive imaging modality in detecting nodal metastasis.

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A man in his 60s, who had undergone surgery for rectal cancer, liver metastases, and lung metastasis, had a past history of myocardial infarction and ventricular fibrillation with reduced cardiac functions. He was referred to our department because of a pulmonary nodule shadow in the S2 right upper lobe and a bronchial cast shadow along the B2 bronchus. Robot-assisted thoracoscopic right S2 segmentectomy was performed and intraoperative bronchoscopy revealed a polyp-like tumor within B2a.

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Objective: Endobronchial ultrasound-guided transbronchial needle aspiration is a vital tool for mediastinal and hilar lymph node staging in patients with lung cancer. Despite its high diagnostic performance and safety, it has a limited negative predictive value. Our objective was to evaluate the diagnostic performance of deep learning-based prediction of lung cancer lymph node metastases using convolutional neural networks developed from automatically extracted images of endobronchial ultrasound videos without supervision of the lymph node location.

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