View clinical trials related to Lung Neoplasms.
Filter by:Most of the lung cancer patients treated in hospitals in China are in advanced stage, accounting for more than 2/3 of all lung cancer patients, and some of the patients have a poor performence status.At present, most of the patients included in clinical trials are patients with good PS score, and the NCCN guidelines for advanced lung cancer patients with poor performence status recommend the best supportive care.Therefore, the investigator wanted to explore the efficacy and safety of Almonertinib in lung cancer patients with poor performance status.
LungTalk and leveraging Facebook-targeted Advertisement (FBTA) addresses the call to develop and test multi-level, cancer communication interventions using innovative methods and designs. The study's long term goal is to increase lung cancer screening uptake among appropriate, high-risk individuals nationwide.
Therapeutic progress for subgroups of Non Small Cell Lung Cancer can largely be attributed to the accumulation of molecular knowledge and the development of new drugs that specifically target molecular abnormalities. An understanding of the immune landscape of tumors, including immune-evasion strategies, has also led to breakthrough therapeutic advances.These new options require prior treatment tumoral sampling to identify patients who have neoplasms with specific genomic aberrations or favorable immune environment. Medical imaging and radiomic approach may provides surrogate markers non invasively.The objective of the present retrospective study is to build and validate a predictive model of common molecular alterations and PD-L1 expression in NSCLC using pre treatment PET/CT derived radiomics.
The aim of this study is to assess the efficacy of a digital lifestyle intervention in non-small cell lung cancer (NSCLC) survivors following inpatient rehabilitation on health-related quality of life (HRQoL) over three months.
The aim of the DigiNet project is to improve the treatment of patients with advanced non-small cell lung cancer (NSCLC) in Germany. The project promotes the transfer of the latest scientific knowledge into standard care. The DigiNet project is based on the established precision medicine program, the National Network Genomic Medicine Lung Cancer (nNGM) in Germany, whereby every patient receives molecular diagnostics and personalized therapy information after the initial diagnosis. Within the framework of the DigiNet project, specialized academic centers will be digitally connected with practitioners via a shared project database. Furthermore, a committee of experts will monitor the course of treatment and will advise the practitioners in case of critical conditions. Additionally, patient-reported outcomes will be incorporated into the treatment.
Background: To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients. Method: The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. The optimization model with best performance was compared to the clinical predictor. In addition, stratified LM patients by risk score were utilized for survival analysis.
The purpose of this study is to evaluate the safety, tolerability and objective response rate of SKB264 as combination with therapy in subjects with advanced or metastatic non-small cell lung cancer.
The use of advanced technological tools able to exploit patient-centered "Real World Data", represents an innovative and fascinating challenge for the most modern personalized medicine paradigms. Monitoring oncological patients during multimodal cancer therapies may represent a significant step towards a comprehensive and reliable quality of life assessment, prevention of toxicity before its clinical onset and treatment outcomes prediction. The big data approach, being able to collect, manage and interpret large volumes of health data, eventually supported by artificial intelligence (A.I.) is therefore fundamental in this setting and may be translated in the next future in tangible advantages for the patients. Primary aim of the project is to assess patients experience of using portable monitoring systems during multimodal oncological therapies and follow up period, through the use of a dedicated app and wearable technology (i.e. monitoring bracelet), as Electronic Health Record data harvesting devices. More specifically, the patients report experience measure of man/women affected by locally advanced non-small-cell lung cancer undergoing chemo(radio)therapy followed either by surgery or immunotehrapy (e.g. describing toxicity, instrumental activities of daily living and stress/coping levels) will be analyzed. The machine learning assisted analysis of these data will allow to identify patients profile that may be used as risk categories to optimize assistance and follow up practices. This is an observational study with device, co-financed, monocentric study with a foreseen study duration of 36 months.
Its to explore the expression pattern, diagnostic and prognostic potentials of miRs (106b-5p, 601 and 760) in serum of NSCLC patients
Study record has been combined with NCT05815173. See NCT05815173 for summary.