View clinical trials related to Lung Cancer.
Filter by:Developing and validating an AI model that supports physicians in their decision process for treating lung cancer patients. This AI model needs to predict the probability of (the evolution of) the outcomes, based on clinical data and a simulated lung cancer treatment plan. The outcome probabilities can be evaluated with different treatment plans to identify the optimal plan. Initially, the input data will be a limited set of selected features such as general patient information, tumour characteristics, laboratory measurement results, comorbidities and treatments. Finally, the goal is to use a deep patient as input to the models.This deep patient is an AI model on its own, trained on hospital data, as described in secondary objectives.
There are ~85,000 lung cancer patients (LCPs) in the UK; yet only around 8% were recruited into clinical trials in 2021/22. LCPs need opportunities to take part in clinical trials to access new treatments, increasing their quality of life, treatment choices and life expectancy. Discussions with nurses can help patients make better treatment decisions, improving experiences of care. However, research has shown that lung cancer nurses (LCNs) often feel unable to discuss participation in trials with patients due to lack of knowledge, confidence, time and training. This study aims to develop and test a research recruitment tool for LCNs, to support LCPs to enter clinical trials. Objectives include to: - Explore reasons for low uptake of LCPs into clinical trials - Develop a tool for LCNs to talk to patients about clinical trials - Test whether the tool improves the number and quality of discussions nurses have with LCPs about clinical trials The study has four phases: Phase 1: A literature review will identify problems that make clinical trial uptake difficult for LCPs, carers and clinicians Phase 2: Six group discussions with LCNs, patients and carers will explore issues that create potential barriers for patients taking part in clinical trials. The groups will take place online, last approximately one hour and be recorded. Phase 3: Part 1&2 findings will help us develop a LCN research recruitment tool. The tool will contain information on how nurses obtain information about LC clinical trials, research teams, communication issues, practical issues and how to reach LCPs. Phase 4: The research recruitment tool will be tested in four UK NHS hospitals. A survey will identify any changes in nurses' clinical trials awareness and confidence before and after using the tool. Interviews with LCNs, patients and carers will explore their views on the tool, clinical trials participation and experiences of care.
This study evaluates the effectiveness of a supervised one-leg resistance training program in patients with inoperable lung cancer with the primary outcome being rate of force development. Study design: Single center, two-armed, parallel-group, randomized controlled trial. The primary outcome being rate of force development after 12 weeks of progressive resistance training.
Identifying the outline of a Gross Tumour Volume (GTV) in lung cancer is an essential step in radiation treatment. Clinical research, such as radiomics and image-based prognostication, requires the GTV to be pre-defined on massive imaging datasets. The ARGOS community creates an open-source and vendor-agnostic federated learning infrastructure that makes it possible to train a deep learning neural network to automatically segment Lung Cancer GTV on computed tomography images. To reduce risks associated with sharing of patient data, we have used a data-secure Federated Learning paradigm known as the "Personal Health Train" that has been jointly developed by MAASTRO Clinic and the Dutch Comprehensive Cancer Organization (IKNL). The successful completion of this project will deliver a highly scalable and readily-reusable framework where multiple clinics anywhere in the world - large or small - can equitably collaborate and solve complex clinical problems with the help of artificial intelligence and massive amounts of data, while reducing the barriers associated with moving sensitive patient data across borders.
Sarcopenia is common in lung cancer patients. It is one of the significant factors affecting the overall survival, radiotherapy and chemotherapy efficacy, and quality of life of patients with lung cancer. Previous studies have shown that increased protein intake can stimulate postprandial muscle synthesis, and improve muscle mass, strength, function, and overall survival. However, most previous studies have focused on the intervention of whey protein, while the protective effect of soybean protein on lung cancer-related sarcopenia (LCRS) has not yet been fully explored and reached an agreement. So, this study aims to explore the effects of soy and whey protein on muscle, gut microbiota, and clinical outcomes among patients with lung cancer-related sarcopenia, to provide a reference for further nutrition treatment.
To investigate whether compound amino acid supplementation can further promote protein synthesis and improve nutritional status for lung cancer patients.
This prospective randomized multicentered clinical study aims at implementing early diagnosis of lung cancer in high-risk heavy smokers in the Italian population. The main goal of the study is to develop a nationwide lung cancer prevention screening with high quality standard, similar to that of other screening programs i.e. breast, colon and cervix ongoing in Italy. The Italian Pulmonary Screening Network (RISP) includes 18 centers, which will promote primary prevention by offering a smoking cessation program (i.e. counselling and anti-smoking cytisine-based therapy) and secondary prevention by screening volunteers with chest Low Dose Computed Tomography (LDCT). The primary objective of the study is to demonstrate the non-inferiority of a risk-based screening strategy (less intensive, every 2 years) compared to the standard annual screening, in terms of stage I/II lung cancer incidence. Furthermore, the study aims to provide evidence whether blood biomarkers screening intervals can improve the efficiency of lung cancer screening by requiring less CT examinations while retaining the ability to diagnose lung cancer at curative state.
Lung cancer can be detected via screening of high-risk individuals, i.e current or ex-heavy smokers, with low-dose computer tomography (LDCT) of thorax. The National Lung Screening Trial in US and the NELSON trial in Europe demonstrated reduction in lung cancer mortality with LDCT screening for lung cancer. In Hong Kong, however, there is a prominence of female never-smokers with lung cancer. There is no identifiable risk factors for non-smokers with lung cancer except family history of lung cancer. The hypothesis is that lung cancer screening for subjects with family history of lung cancer, can detect early lung cancer.
Participation in medical trials usually favors a particular demographic group. But there is limited research available to explain what trial attributes affect the completion of these specific demographic groups. This trial will admit a wide range of data on the clinical trial experience of lung cancer patients to determine which factors prevail in limiting a patient's ability to join or finish a trial. It will also try to analyze data from the perspective of different demographic groups to check for recurring trends which might yield insights for the sake of future lung cancer patients.
Investigators intend to combine low-dose hypersensitivity with high-dose immunopotentiation effect, and use super-hyperfractionation pulse radiotherapy, which is expected to achieve the effect of in situ vaccine that can enhance tumor killing, protect normal tissues, reduce immune cell damage and enhance tumor immunogenicity at the same time, and play a stronger immunopotentiation effect in combined immunotherapy. Thereby inducing a stronger abscopal effect of radiotherapy.