View clinical trials related to Lung Cancer.
Filter by: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.
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.
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.
This research aims to develop an innovative photodynamic therapy (PDT) for peripheral lung tumors. Current treatments involve surgery, chemotherapy, and radiation. Photodynamic therapy, using light and photosensitizing drugs, is promising but has limitations. Our team proposes using Lipiodol, a contrast agent, instilled into the trachea via bronchoscopy, surrounding the tumor. Preliminary pig model trials showed safety. Clinical trials, building on a U.S. study (NCT02916745), commenced in October 2021, treating three cases. Initial results suggest safety, but efficacy requires further investigation. Based on ongoing trials, we propose a phase I trial with multiple light treatments from different directions and an additional dose after 48 hours to assess safety and efficacy. This study will guide future clinical trials for optimal PDT dosage.
For patients with lung cancer who have undergone tumor resection, early relapse significantly impacts survival. However, there are currently no reliable screening or imaging tools available to identify patients at risk of early relapse. To address this clinical challenge, many studies have focused on understanding the clinicopathologic characteristics associated with an increased risk of early relapse. Despite these efforts, we can identify patients at risk but cannot pinpoint which individuals will actually experience early relapse. Studies on adjuvant therapy have shown improved survival in cases of more advanced disease but have not demonstrated a reduction in early relapse rates. In our preliminary analysis of previous study data, we observed that patients with a smaller reduction in circulating tumor cells (CTCs) within the first three days after surgery, followed by an increase on the third-day post-operation, are more likely to experience early relapse during regular monitoring. This pattern may be indicative of minimal residual disease. By combining trends in circulating tumor cell variations with pathologic characteristics, we aim to select patients for adjuvant therapy who are at high risk of developing early relapse. The objective of our study is to employ screening based on circulating tumor cell dynamics and pathologic features to identify patients likely to experience early relapse and to assess the effectiveness of adjuvant therapy in these cases.
CONQUER cancer study: To collect blood samples from cancer and non-cancer participants for development and validation of the 'OncoSweep Cancer Spotlight and Spectrum Product Line'
This study is a multi-center, observational, real-world study for patients with resected lung cancers in China. With the help of a properly designed data processing algorithm and extensively performed data quality assurance, this study aims to harness the potential of real-world big data to (1) describe characteristics and treatment patterns and their evolving trends; (2) discover features associated with overall survival; and (3) address recently-emerging clinical questions.
The goal of this study is to show that trained detection dogs can identify breast, lung, prostate or colorectal cancer by sniffing masks containing breath samples. In this study, individuals who will undergo cancer screening at an integrated cancer prevention center or biopsy for a suspected malignancy, will be asked to provide a breath sample by breathing into a surgical mask. The mask will then be sent to the laboratory, where trained detection dogs will determine if the person who provided the mask has breast, lung, prostate or colorectal cancer or if the person does not have these types of cancer. The results provided by the dogs will be compared to the actual cancer screening results or biopsy results in order to determine the accuracy of cancer detection by the trained dogs.
The purpose of the study is to determine whether a preference-oriented quality of life monitoring with defined diagnostic and therapeutic options improves quality of life in patients with lung cancer during routine follow-up care.