How to know if lung cancer is progressing?
Answers from top 5 papers
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| Papers (5) | Insight |
|---|---|
| The findings opened the door for clinical lung cancer screening and publication of lung cancer screening guidelines. | |
100 Citations | Tumor marker determination in patients with suspicious signs of lung cancer suggests, in a few hours, the histological diagnosis in the majority of lung cancer patients. |
33 Citations | It is suggested that one of these respiratory illnesses is lung cancer. |
37 Citations | Early diagnosis is pivotal for prognosis of lung cancer patients. |
| Diagnosing Lung Cancer in the early stage is essential. |
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What digital tools exist for monitoring disease progression in oncology?5 answersDigital tools for monitoring disease progression in oncology include EvAM-Tools, Disease Progression Modeling workbench 360 (DPM360), and oncology digital symptom monitoring and patient self-management coaching tools. EvAM-Tools is an R package and web application that provides a unified interface to cancer progression models (CPMs) and evolutionary models of event accumulation. DPM360 is an opensource clinical informatics framework that manages the entire modeling life cycle, from data analysis to machine learning algorithm development and prototyping. Oncology digital symptom monitoring and patient self-management coaching tools enable frequent symptom assessments, personalized self-management reinforcement, and alert healthcare providers about key times for follow-up with patients. These tools aim to improve symptom management, extend care beyond clinic visits, and enhance clinical decision-making in oncology.
What technology exists for monitoring disease progression in oncology clinical trials?4 answersMachine learning models have been used to monitor disease progression in oncology clinical trials. These models are trained on data obtained after the initiation of therapy and can incorporate patient response to better predict progression outcomes. Multiple clinical outcomes are often used in cancer clinical trials to measure improvements in patients' signs and symptoms associated with treatments. These outcomes include overall survival, disease-free survival, objective response rate, time to progression, and progression-free survival. Utilizing multiple endpoints in clinical trials allows for a multidimensional assessment of the intervention's effects but also presents challenges in controlling Type I and Type II errors associated with multiple tests. Risk-based quality management, such as central statistical monitoring, can help monitor disease progression in oncology clinical trials more efficiently and reduce costs.
What data is used in lung cancer detection?4 answersLung cancer detection utilizes different types of data, including gene expression data from microarrays, CT scan images, and urine biomarkers. Microarray gene expression data is used to identify normal and abnormal patterns in lung cancer genes. CT scan images are processed using deep learning and convolutional neural networks to detect defects and abnormalities in the lungs. Urine biomarkers are analyzed to develop diagnostic tools for lung cancer, with a three-biomarker panel showing promise in discriminating lung cancer from healthy controls. These different types of data provide valuable information for the early identification and treatment of lung cancer.
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