Clinical Significance of Computer Aided Image Analysis in Treatment Response Evaluation of Lung Cancer
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Lung Cancer
- Sponsor
- Wuhan Union Hospital, China
- Enrollment
- 1000
- Locations
- 1
- Primary Endpoint
- Overall survival
- Last Updated
- 6 years ago
Overview
Brief Summary
The investigators will evaluate the utility of computer aided image analysis in lung cancer with the aim of predicting treatment response and prognosis.
Detailed Description
Tumor biological behavior is the fundamental cause of heterogeneous prognosis. The features found on medical images are also reflections of the tumor biological behavior. However, the limitations in spatial and intensity resolution of the naked eye are two inevitable shortcomings of image interpretation by naked eyes, resulting in subjective and limited analyses of images. Computer aided image analyses such as radiomic analysis and machine learning methods are emerging as promising image interpretation methods. The natural advantage of the unlimited spatial and intensity resolution of computers can overcome the shortcomings of visual inspection with the naked eye. Moreover, the massive computing power of computer is also far greater than that of humans. This study will focus on the application of computer aided analysis in predicting treatment response and prognosis in lung cancer.
Investigators
Yang Jin
Professor
Wuhan Union Hospital, China
Eligibility Criteria
Inclusion Criteria
- •Patients \>= 18 years old
- •Pathological diagnosis of NSCLC between 2012 and 2019;
- •PET/CT or CT examination before any cancer-specific treatment;
Exclusion Criteria
- •A time interval between treatment and image examination greater than 1 month;
- •A history of other malignancies
Outcomes
Primary Outcomes
Overall survival
Time Frame: 2012-2021
The interval between the date of diagnosis and death
Secondary Outcomes
- Progression free survival(2012-2021)