Active clinical trials for "Lung Neoplasms"

The purpose of this study is to compare the features that the pathologist sees, when examining a lung tumor under the microscope, to the way that the tumor appears on the computed tomography (CT) scan. Features of the tumor may include abnormal blood vessels and areas in which tumor cells are dying. The samples that are taken during the needle biopsy contain information from one small part of the tumor. The investigators believe that they can show where in the tumor the samples came from, based on the CT scans during the biopsy procedure. If the investigators can accurately determine where in the tumor their samples came from, they can compare the features of that part of the tumor, as seen on the CT images, to the features of that part of the tumor as seen under the microscope. This research study also will give the investigators an idea of how much the biopsy samples are distorted in the process of preparing them for examination under the microscope.

This is a real-world study with the largest sample size investigating the pathological tumor and lymph node responses to neoadjuvant immunochemotherapy in non-small cell lung cancer to date. Patients with initially unresectable NSCLC underwent immunochemotherapy and response to treatment was assessed after every two treatment cycles. Clinicopathologic features of patients including epidemiological data, clinical manifestations, operation strategies, pathological findings, and prognostic information were recorded and evaluated.

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.

This is a multi-center retrospective observational research study collecting data for participants initiated on docetaxel for the treatment of relapsed non-small cell lung cancer (NSCLC) up to 6 years before start of data collection and who have completed/stopped docetaxel treatment at least 30 days prior to data collection. The primary goal of this study is to describe the percentage of participants with at least one neutropenic sepsis (NS) episode following initiation of treatment with single-agent docetaxel. Data will be collected until end of docetaxel treatment plus an additional 30 days to allow for presentation with toxicity.

National, prospective, multicentric, non-interventional registry-based study, conducted among oncologists, pulmonologists in community and university hospitals, from the public sector in Algeria. The study will collect information on the characteristics, and lung carcinomas patterns of patients whose lung cancer diagnosis was confirmed by a pathologist during the study period.

Not significantly increased survival in T/T

The purpose of this study is to explore the detection of circulating tumor DNA, soluble immune markers, and the evaluation of peripheral blood mononuclear cells (PBMC).

Lung cancer is the leading cause of cancer death in North America and in the world. The vast majority of lung cancers are associated with cigarette smoking. Unfortunately, the majority of lung cancers are detected at an advanced stage when they have a very poor prognosis. A substantial amount of data has been reported on low-dose computed tomography (LDCT) screening. Yet endorsement of lung cancer screening has not been universal because of outstanding concerns which need to be addressed. Our study will aim to screen at risk Albertans for lung cancer with LDCT scan at baseline, year 1 and year 2 while attempting to fill some knowledge gaps on this topic.

This is a research study for participants who have been diagnosed with lung cancer and are receiving VATS (Video Assisted Thoracoscopic Surgery) lobectomy or segmentectomy. The overall objective of this study is to develop a database to collect data on participants who are undergoing VATS lobectomy or segmentectomy for lung cancer.

The assessment of the type and frequency of EGFR and KRAS mutations in lung cancer patients, as well as clinical-prognostic correlation, are crucial in the era of targeted therapies. EGFR-activating mutations predict responsiveness to EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC) patients and KRAS analysis will be useful in a near future for newest target drugs. In Brazil, few data about the prevalence of EGFR and KRAS mutations is available and their knowledge would allow optimize personalized medicine.