Lung Nodule

The purpose of this study is to test whether combining a unique analytical approach with changes in platelet RNA expression accurately diagnoses lung cancer. Using retrospective platelet transcriptomic data from 522 patients with non-small cell lung cancer (NSCLC, the most common type of lung cancer), an approach that appears to accurately classify lung cancer has been developed. The study will build upon these retrospective analyses to prospectively recruit patients with newly diagnosed lung cancer, obtain platelet RNA samples from whole blood, and perform validation analyses. This research will also test whether this approach accurately distinguishes benign from malignant lung nodules.

The BIRD biobank aims at collecting clinical and biological data from patients suffering from a chronic respiratory disease. The lung cancer subpopulation will be divided into two cohorts to identify biomarkers of cancer. One cohort will include patients with supra-centimetric lung nodule(s) whether surveillance, bronchoscopic or radio-guided biopsy or surgery is indicated, patients suspected of lung cancers requiring diagnostic and/or therapeutic bronchial endoscopy and patients with a known early stage lung cancer (early-stage cohort). The second cohort will include known advanced stage lung cancers (III-IV).

chest x-rays will be analyzed by AI software for a secondary read of lung nodules. Chest x-rays will either be sent to the AI tool to be read or to radiologists to read. If the image is sent to the AI tool, the AI software will generate a report on if it detects a lung nodule or not. The image will then be sent to a radiologist to determine if there is agreement or disagreement with the AI tool.

Investigators aim to evaluate the diagnostic accuracy of the Nu.Q blood test for lung cancer in the Taiwanese population and compare its diagnostic performance with low-dose computed tomography (LDCT) or computed tomography (CT). Additionally, investigators will investigate the potential role of Nu.Q in lung cancer prevention and its impact on survival outcomes. Study Method: Investigators plan to collect 20 mL of blood samples from individuals undergoing chest LDCT/CT, isolate plasma for Nu.Q™ analysis, and compare the results with corresponding lung cancer pathology findings. The estimated sample size is 500 participants.

The current study aims to develop and validate a deep learning signature for diagnosing pathology and predicting prognosis of lung nodule using smartphone photos of resected tumor specimens.

The goal of this clinical trial is to learn if AI-based low dose CBCT reconstructed images can guide lung puncture effectively. The main questions it aims to answer are: 1. Does the AI-based low dose CBCT reconstruction model reconstruct high quality images? 2. Is it possible that low-dose CBCT reconstructed images can guide lung puncture procedures without compromising the efficiency of the procedure? Researchers will compare AI-based low dose CBCT reconstructed images to a placebo (conventional CBCT images) to see if AI-based low dose CBCT reconstructed image can guide lung puncture procedures without compromising the efficiency of the procedure. Participants will: 1. Undergo lung puncture under AI-based low dose CBCT reconstructed images guidance or under conventional CBCT images 2. Be followed up for 1 week postoperative to obtain patient complications