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

H

Huazhong University of Science and Technology

Status

Unknown

Conditions

Lung Cancer

Study type

Observational

Funder types

Other

Identifiers

NCT03954847
WuhanUH-V1.2

Details and patient eligibility

About

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

Full 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.

Enrollment

1,000 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

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;

Exclusion criteria

  1. A time interval between treatment and image examination greater than 1 month;
  2. A history of other malignancies

Trial design

1,000 participants in 1 patient group

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

Trial contacts and locations

1

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Central trial contact

Yang Jin, MD

Data sourced from clinicaltrials.gov

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