Evaluation of Lung Nodule Detection With Artificial Intelligence Assisted Computed Tomography in North China

March 27, 2018 updated by: Jun Wang, Peking University People's Hospital

Evaluation of Lung Nodule and Lung Cancer Detection With Artificial Intelligence Assisted Computed Tomography Among People Living in North China: a Prospective Single-arm Multicentre Study of Screening

Lung cancer is one of the leading cause of cancer related death in China. Lung cancer screening with low-dose computed tomography was considered as a better approach than radiography. However, the role of Lung cancer screening with Low-dose CT (LDCT) among Chinese people remains unclear. With rapid development of artificial intelligence (AI),the application of AI in detection and diagnosis of diseases has become research focus. Moreover, patients' psychological status also plays an important role in diagnosis and treatment.

This study focuses on detection and natural history management of lung nodule and lung cancer with AI assisted chest CT among people living in North China, and aims to investigate epidemiological results, patients' medical records and social psychological status.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Detailed Description

Lu'an Municipal Hospital and North China Petroleum Bureau General Hospital initialed the lung cancer screening by LDCT a few years ago. People living in North China who are administrated by these hospitals routinely took a chest CT every year. This study is to the best of our knowledge the first one designed to combine lung nodule and lung cancer screening with the application of artificial intelligence in China.

Methods: Firstly, the study acquires epidemiological, medical information and psychological status of people recruited, and investigates the data acquired from past several years of CT scans using AI to develop a model for lung nodule detection. Secondly, evaluating the performance of models and apply it to analyse the CT scans from the North China population recruited. Thirdly, improving the model and adding function for lung nodule prediction of natural history and probability of malignancy.

Aims: To depict the epidemiological results about the incidence of lung nodules and lung cancer in North China population; To evaluate association between people 's epidemiological, medical and psychological profiles and incidence, diagnosis and treatment of lung nodule; To develop an artificial intelligence assisted lung nodule diagnosis and management software to assist strategies of CT screening.

Study Type

Observational

Enrollment (Anticipated)

5000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

40 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

People aged over 40, routinely conducting chest LDCT scan yearly in designated hospital of North China in at least the past 4 years up to December 2017, with acceptable physical conditions are eligible.

Description

Inclusion Criteria:

  • Aged 40 years or older
  • Routinely conducting chest CT scan at a low-dose setting (120kVp, 40-80mA, slice thickness of 1.25 mm or less) yearly in Lu'an Municipal Hospital and North China Petroleum Bureau General Hospital in at least the past 4 years up to December 2017, willing to continue routine yearly LDCT scan.
  • Chest CT data are available for DICOM format.
  • Signed Informed Consent Form.

Exclusion Criteria:

  • Pregnant woman and the disabled
  • Past thoracic surgery history, except for diagnostic thoracoscopy
  • Poor physical status without sufficient respiratory reserve to undergo lobectomy if necessary
  • Shortened life expectancy less than 10 years
  • Malignant tumor history within the past 5 years, except for the following conditions: cured skin basal cell carcinoma, superficial bladder carcinoma. and uterine cervix cancer in situ.
  • Past history of interstitial lung disease, pulmonary bulla and lung tuberculosis.
  • Other circumstances which is deemed inappropriate for enrollment by the researchers.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
LDCT screening group
People receive questionnaire administration at baseline, then subsequent yearly chest LDCT scan and follow up.
Other: Questionnaire Administration
Subjects will be asked to complete an additional detailed questionnaire regarding personal information, smoking history, medical history, their diet and lifestyle habits, family history of malignant neoplasm, any past or current environmental exposures and psychological status.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detection rate of lung nodule
Time Frame: 3 months
Study participants undergo baseline LDCT. Images are reviewed via AI software independently to identify lung nodules with diameters greater than 4mm. The software is developed by our computer technology collaborator. A radiologist then reviews the images, reports lung nodules with diameters greater than 4mm and any other abnormalities. The radiologist's findings will be conveyed to the study participants or their primary care physicians within 3 weeks. The process was conducted via double-blind method and detection rates of AI and radiologist will be recorded respectively. Unit of measurement: Percentage (number of participants with detected lung nodules over the total number of participants).
3 months
Profile of detected lung nodule
Time Frame: 3 months
All lung nodules detected will be classified as 4 classes by the density and composition of nodule: 1. pure ground-glass nodule (pGGN); 2. part-solid nodule; 3. solid nodule; 4. uncertain nodule. The number and proportion of each class and the diameter and location of each nodule will be recorded. Unit of measurement: Percentage (number of nodules in each class over the total number of nodules); Numerical value (average value±standard deviation of nodules in each class); Percentage (number of nodules in each lobe over the total number of nodules).
3 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity in the detection of clinically actionable lung nodules
Time Frame: 3 months
AI assisted CT compared with conventional CT read via radiologist. Unit of measurement: Percentage (detected actionable lung nodules over the total number of actionable nodules).
3 months
Growth of lung nodule
Time Frame: 3 years
Study participants undergo baseline LDCT and subsequent yearly LDCT. Making use of these consecutive CT images, volume doubling time (VDT) for each lung nodule can be calculated via software and can be used to evaluate growth of nodule. cUnit of measurement: Numerical value (volume doubling time, VDT).
3 years
Anxiety and depression level
Time Frame: 3 months
Hospital Anxiety and Depression Scale (HADS) is included in our questionnaire and score of this scale will be recorded. Unit of measurement: Numerical value (score of scale).
3 months
Life quality and health status
Time Frame: 3 months
The MOS item short from health survey (SF-36) is included in our questionnaire and score of this scale will be recorded. Unit of measurement: Numerical value (score of scale).
3 months
Lung cancer detection rate
Time Frame: 3 months
Percentage (the number of detected lung nodules which was finally diagnosed as primary lung cancer over the total number of detected lung nodules)
3 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking University People's Hospital

Collaborators

Lu'an Municipal Hospital
North China Petroleum Bureau General Hospital

Investigators

  • Principal Investigator: Jun J Wang, MM, Peking University People's Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

April 1, 2018

Primary Completion (Anticipated)

December 1, 2021

Study Completion (Anticipated)

December 1, 2021

Study Registration Dates

First Submitted

March 21, 2018

First Submitted That Met QC Criteria

March 27, 2018

First Posted (Actual)

April 4, 2018

Study Record Updates

Last Update Posted (Actual)

April 4, 2018

Last Update Submitted That Met QC Criteria

March 27, 2018

Last Verified

March 1, 2018

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • NCLUNG

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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