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Browse 2,032 clinical trials for lung cancer. Find studies that match your criteria and connect with research centers.
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Showing 641-660 of 2,032 trials
NCT06785584
According to the Global Cancer Statistics 2022 report, lung cancer is the most common type of cancer (12.4% of the total) and the leading cause of cancer deaths (18.7% of total cancer deaths). According to the pathological classification of patients, lung cancer is divided into small cell lung cancer and non-small cell lung cancer, of which non-small cell lung cancer (NSCLC) accounts for 80-85% of all lung cancer. Surgery is the preferred treatment for patients with early-stage lung cancer, according to the 2024 CSCO Guidelines. However, most patients have the possibility of recurrence and metastasis after surgery. The 5-year survival rate of patients with stage IA NSCLC is 80%-90%, but the 5-year survival rate of patients with stage ⅢB NSCLC drops to 40%. Neoadjuvant therapy has become an important part of the treatment of non-small cell lung cancer (NSCLC) in order to prolong the survival of patients. In the past few years, many driver genes of NSCLC have been identified, and anaplastic lymphoma kinase (ALK) is one of them. ALK was first identified in anaplastic large cell lymphoma (ALCL). Studies at home and abroad have shown that ALK-rearranged (positive)NSCLC accounts for about 3%-7% of all NSCLC patients. Many studies have suggested that ALK-TKI is clinically feasible as a neoadjuvant therapy for ALK positve patients with locally advanced NSCLC. The investigators designed this study to explore the efficacy of enshatinib neoadjuvant therapy in patients with stage IIA to III ALK-positive lung adenocarcinoma
NCT06684418
This nationwide, multicenter observational study aims to develop and validate a multimodal artificial intelligence (AI) model for detecting occult lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) patients. Despite advances in lymph node staging, 12.9%-39.3% of occult nodal metastasis cases remain undetected preoperatively, affecting treatment decisions. This study will use deep learning to extract imaging features of occult metastasis and combine them with clinical data to build an AI model for risk prediction. This study will provide insights into the feasibility of AI-driven detection of occult metastasis, supporting clinical decision-making and potentially revealing underlying biological mechanisms of lymph node metastasis in NSCLC.