Clinical Significance of Computer Aided Image Analysis in Treatment Response Evaluation of Lung Cancer

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2012-01-01

Study Completion Date

2020-12-31

Brief Summary

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The investigators will evaluate the utility of computer aided image analysis in lung cancer with the aim of predicting treatment response and prognosis.

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Detailed Description

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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.

Conditions

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Lung Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Lung cancer patients

Lung cancer patients with PET/CT or CT examination before any cancer-specific treatment

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

1. Patients \>= 18 years old
2. Pathological diagnosis of NSCLC between 2012 and 2019;
3. PET/CT or CT examination before any cancer-specific treatment;