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

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

Study Overview

• Status: Unknown
• Conditions: Lung Cancer

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.

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430000
      • Recruiting
      • Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall survival	The interval between the date of diagnosis and death	2012-2021

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Progression free survival	The interval between the date of treatment initiation and disease progression	2012-2021

Collaborators and Investigators

• Sponsor: Wuhan Union Hospital, China

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2012
• Primary Completion (Anticipated): December 31, 2020
• Study Completion (Anticipated): December 31, 2020

Study Registration Dates

• First Submitted: May 16, 2019
• First Submitted That Met QC Criteria: May 16, 2019
• First Posted (Actual): May 17, 2019

Study Record Updates

• Last Update Posted (Actual): May 17, 2019
• Last Update Submitted That Met QC Criteria: May 16, 2019
• Last Verified: May 1, 2019

More Information

Terms related to this study

• Keywords: Prognosis; Lung cancer; Image; Treatment response; Deep learning; Radiomic
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: WuhanUH-V1.2

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No