View clinical trials related to Lung Neoplasms.
Filter by:The purpose of this study to test measures of physical and psychological resilience while using Self-System therapy (SST), to treat depression and lung-cancer-related distress in older adults (65 years and older).
The primary objective of this study, DELFI-L101, is to train and test classifiers for lung cancer detection using the DELFI assay and other biomarker and clinical features.
The purpose of this study is to evaluate the safety and efficacy of Keynatinib capsules in patients with advanced non-small cell lung cancer (NSCLC) with brain metastasis or progression of brain metastasis after treatment with EGFR inhibitors. As well as, to evaluate the penetration rate of Keynatinib in the Blood-Brain Barrier (BBB) and its PK characteristics, and the relationship between exposure levels with efficacy and safety.
This study aims to evaluate the safety and tolerability of AMG 160 and to evaluate the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D).
This clinical trial focuses on a nurse-led program that is designed to help patients cope with worries, fears, and uncertainty about the future. The purpose of this study is to understand if the program is helpful and practical to carry out at medical centers and community clinics. This study may help patients learn more effective ways to cope and respond to your concerns and any unhelpful thoughts.
Lung cancer is the most frequently dianosed cancer worldwide. To date, no screening method has been able to establish itself as routinely recommended by the guidelines. In this prospective study with 1:1 randomized questioning using an Internet tool, physicians will be asked in 2 phases (before and after intervention with a fact box) about their assessment of the benefits and risks of lung cancer screening by thoracic computed tomography and about a potential intention to change referral behavior. Randomly assigned, half of the participants will receive the same information in addition to the fact box graphically presented as a Cates plot.
This study is implemented in association with the study "J-TAIL-2" ; prospective multicenter observational study of atezolizumab in patients with unresectable, locally advanced or metastatic non-small cell lung cancer, UMIN study ID: UMIN000041263, to evaluate biomarkers for selection of appropriate patients in treatment with atezolizumab combination therapy.
This is an open-label, two-part study to evaluate the safety, tolerability, pharmacokinetics, and efficacy of SHR-A1811 for injection in subjects with advanced non-small cell lung cancer who have HER2 expression , amplification, or mutation
The purpose of this study is to develop a lung cancer diagnosis tool using a multi-omics approach based on liquid biopsy.
Before any treatment decisions are made for patients with lung cancer, it is crucial to determine whether the cancer has spread to the lymph nodes in the chest. Traditionally, this is determined by taking biopsy samples from these lymph nodes, using the Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA) procedure. Unfortunately, in 40% of the time, the results of EBUS-TBNA are not informative and wrong treatment decisions are made. There is, therefore, a recognized need for a better way to determine whether the cancer has spread to the lymph nodes in the chest. The investigators believe that elastography, a recently discovered imaging technology, can fulfill this need. In this study, the investigators are proposing to determine whether elastography can diagnose cancer in the lymph nodes. Elastography determines the tissue stiffness in the different parts of the lymph node and generates a colour map, where the stiffest part of the lymph node appears blue, and the softest part appears red. It has been proposed that if a lymph node is predominantly blue, then it contains cancer, and if it is predominantly red, then it is benign. To study this, the investigators have designed an experiment where the lymph nodes are imaged by EBUS-Elastography, and the images are subsequently analyzed by a computer algorithm using Artificial Intelligence. The algorithm will be trained to read the images first, and then predict whether these images show cancer in the lymph node. To evaluate the success of the algorithm, the investigators will compare its predictions to the pathology results from the lymph node biopsies or surgical specimens.