View clinical trials related to Lung Cancer.
Filter by:Lung cancer screening is currently not recommended in non-smokers due to paucity of evidence. Emerging evidence suggests that first-degree family history is a strong risk factor for lung cancer in Asian non-smokers. In Asia, lack of resource is a major challenge in successful implementation of lung cancer screening. Artificial intelligence (AI) is a promising tool to overcome this resource. In this study, we aim to study the clinical utility and demonstrate the feasibility of using an AI assisted programme for lung cancer screening in Asian non-smokers with a positive family history. This is a single-arm non-randomized lung cancer screening study. 1000 non-smokers, age 50 to 75 year old, with a first-degree family history of lung cancer, will be enrolled. Participants will undergo low does computed tomography (LDCT) of thorax and blood taking at enrolment. LDCT films will be interpreted by AI softwares for presence of lung nodules. Participants with lung nodules will be further investigated and followed up according to the risk of malignancy. The primary endpoint is the prevalence of early-staged lung cancer detected by first-round LDCT thorax in this population.
The goal of this clinical trial is to implement lung cancer screening in a targeted high-risk population of heavy (ex-)smokers in Flanders (Belgium). This implementation study will investigate the participation rate of eligible high risk (ex-)smokers in the First Line Zone South East Region of Antwerp (ZORA) in a LDCT screening program, combined with smoking cessation.
The goal of this retrospective cohort analysis is to discover the impact of the involvement extent of tumor-draining lymph nodes (TDLNs) in patients who received neoadjuvant immunochemotherapy. The main question it aims to answer is the role of TDLN in the prediction of treatment effectiveness. Researches will compare tumor-draining lymph nodes metastasis (mTDLNs) group and non-draining lymph nodes metastasis (mNDLNs) group to see whether different metastatic patterns of mediastinal lymph nodes can indicate the treatment effectiveness.
The goal of this clinical trial is to compare breath samples from patients with lung cancer, those with stable lung nodules, and individuals without lung cancer. The main questions it aims to answer are whether breath patterns are different in these groups and to see if breath patterns change after treatment in patients with lung cancer. Participants will be asked to provide one breath sample, and two if they get treated. They will also be asked to fill out a questionnaire about their background and medical history.
Lung cancer survival rates are low for intersectional underserved groups. Lung cancer stigma and intersectional stigma related to minoritized group status leads to increased morbidity and mortality and health disparities. Mindfulness interventions have been shown to decrease stigma and the negative impacts of stigma, however, these interventions have never been tested to decrease lung cancer stigma specifically. In this study, the investigators will use Community Based Participatory Research framework and MOST methodology to build and optimize a brief virtual mindfulness intervention to decrease lung cancer stigma, through first building a diverse coalition of lung cancer patients on a participatory action council.
Robotic surgical systems have become a promising surgical assistance system, with unique advantages such as 3D high-definition visual system, highly flexible wrist-jointed instruments, and automatic fitering of hand tremors. Robotic surgical systems is applied in various fields including urology, general surgery, cardiothoracic surgery, head and neck surgery, and gynecology. Currently, the Da Vinci surgical robot system is the most widely used robot system globally. However, the Da Vinci robot system for single-port laparoscopic surgery has not yet been launched in China. Therefore, the investigators conduct a prespective, single-center study to evaluate the efficacy and safety of single-port surgical robot system in thoracic surgery.
This study proposes to increase Lung-cancer screening (LCS) in the Bronx, New York. Despite strong evidence that Lung-cancer screening (LCS) can reduce Lung cancer (LCa) deaths, low-dose computed tomography (LDCT) referral rates by clinicians are very low and there is poor adherence with LCS by patients. Both provider and patient barriers may be amenable to systemic improvements in support, coordination and infrastructure for screening. The investigator team hypothesizes that the implementation of a Central Screening Unit (CSU) that shifts routine workflow attributed to LCS (e.g., collection of smoking history, determination of eligibility, shared decision making and arranging follow-up) away from busy practices (usual care) and that offers patients an array of navigation and support services will increase the uptake of LCS guidelines and subsequent low-dose computed tomography (LDCT) screening scans in a low-income, predominately Hispanic and Black catchment area. The proposed study represents a unique opportunity to test this hypothesis in the context of the roll out of a CSU as a significant new component of the Montefiore-Einstein health system. The investigator team will examine whether and how the CSU facilitates LCS uptake and retention of patients. This study is powered to test whether CSU reduces proportion of late-stage lung cancer diagnoses in the Bronx, New York.
To explore and evaluate the safety and efficacy of camrelizumab combined with chemotherapy ± thalidomide in first-line treatment of advanced non-small cell lung cancer patients
The study aims to develop a deep learning-based diagnostic method for lung cancer using the oral microbiome. This innovative approach involves establishing an observational cohort of 576 individuals, including lung cancer patients, non-cancerous benign lung disease patients, and healthy controls, to collect tongue swab samples for 16S rRNA sequencing. Additionally, an international cohort of approximately 1700 individuals will be formed using in silico data. The project will utilize deep learning methods to analyze all data integratively and develop an AI diagnostic algorithm capable of distinguishing lung cancer patients from others. The diagnostic method's performance will be tested in a pilot clinical trial with 96 individuals using a PRoBE design. Led by experts in chest surgery, molecular microbiology, and bioinformatics, the project spans over 30 months and aims to create a non-invasive, easily accessible lung cancer screening method that could lead to significant diagnostic advancements and potential spin-off companies in the field of liquid biopsy/molecular diagnosis.
Predicting relapse and overall survival in potentially resectable Stage IIIA-IIIB Non-small Cell Lung Cancer (NSCLC) patients remains challenging. It is now widely recognized that patients with detectable MRD have a worse prognosis than those with undetectable MRD. Therefore, investigators performed this prospective clinical trial to evaluate the predictive value of MRD with increased risk of relapse and improves prediction of outcome in potentially resectable Stage IIIA-IIIB NSCLC with neoadjuvant chemoimmunotherapy. In this study, investigators will pay more attention to the long-term follow-up time and dynamic monitoring of MRD. The predictive value of MRD with Disease-free survival (DFS) rate was observed as the primary endpoint. Besides that, the correlation of MRD with major pathologic response (MPR) rate, pathologic complete response (pCR) rateļ¼event-free survival(EFS) rate and overall survival (OS) were observed as the second endpoints. Investigators hope it will provide a new insight for these potentially resectable Stage IIA-IIIB NSCLC with neoadjuvant chemoimmunotherapy.