Solitary Pulmonary NoduleNCT03487952
Evaluation of Lung Nodule Detection With Artificial Intelligence Assisted Computed Tomography in North China
An observational study of Solitary Pulmonary Nodule and Multiple Pulmonary Nodules, run by Peking University People's Hospital.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 40 years and older
- Conditions the study is for
- Solitary Pulmonary Nodule, Multiple Pulmonary Nodules
- What is being tested?
- Questionnaire Administration
- How many people
- 5,000 participants
- Where
- No sites listed yet
- Study dates
- Apr 2018to Dec 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet.
Feng F Yang, MD contact
About this study
Official title: 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.
Read the 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.
- Being tested
- Questionnaire Administration
- Keywords
- Lung nodule, Computed tomography, Artificial intelligence
- Medical subject headings
- Solitary Pulmonary Nodule, Multiple Pulmonary Nodules
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Questionnaire AdministrationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| LDCT screening group | People receive questionnaire administration at baseline, then subsequent yearly chest LDCT scan and follow up. Receives: Other: Questionnaire Administration |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection rate of lung nodule
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).
Measured: 3 months
Profile of detected lung nodule
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).
Measured: 3 months
Secondary outcomes (5)
Sensitivity in the detection of clinically actionable lung nodules
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).
Measured: 3 months
Growth of lung nodule
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).
Measured: 3 years
Anxiety and depression level
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).
Measured: 3 months
Life quality and health status
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).
Measured: 3 months
Lung cancer detection rate
Percentage (the number of detected lung nodules which was finally diagnosed as primary lung cancer over the total number of detected lung nodules)
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Apr 2018
- Study startApr 2018 (estimated)
- Primary completionDec 2021 (estimated)
- Study completionDec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Solitary Pulmonary NoduleStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Lu'an Municipal Hospital, North China Petroleum Bureau General Hospital
- Responsible party
- Principal investigator, Jun Wang, Peking University People's Hospital
- Investigators
- Jun J Wang, MM, Principal investigator, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03487952
- Sponsor's protocol ID
- NCLUNG
- Status last verified
- Mar 2018
- First submitted
- 21 Mar 2018
- Last updated
- 4 Apr 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Field JK, Oudkerk M, Pedersen JH, Duffy SW. Prospects for population screening and diagnosis of lung cancer. Lancet. 2013 Aug 24;382(9893):732-41. doi: 10.1016/S0140-6736(13)61614-1.
PubMed 23972816Silva M, Pastorino U, Sverzellati N. Lung cancer screening with low-dose CT in Europe: strength and weakness of diverse independent screening trials. Clin Radiol. 2017 May;72(5):389-400. doi: 10.1016/j.crad.2016.12.021. Epub 2017 Feb 4.
PubMed 28168954National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
PubMed 21714641Detterbeck FC, Mazzone PJ, Naidich DP, Bach PB. Screening for lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013 May;143(5 Suppl):e78S-e92S. doi: 10.1378/chest.12-2350.
PubMed 23649455Baldwin DR, Callister ME; Guideline Development Group. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules. Thorax. 2015 Aug;70(8):794-8. doi: 10.1136/thoraxjnl-2015-207221. Epub 2015 Jul 1.
PubMed 26135833Wiener RS, Gould MK, Woloshin S, Schwartz LM, Clark JA. What do you mean, a spot?: A qualitative analysis of patients' reactions to discussions with their physicians about pulmonary nodules. Chest. 2013 Mar;143(3):672-677. doi: 10.1378/chest.12-1095.
PubMed 22814873Harris RP, Sheridan SL, Lewis CL, Barclay C, Vu MB, Kistler CE, Golin CE, DeFrank JT, Brewer NT. The harms of screening: a proposed taxonomy and application to lung cancer screening. JAMA Intern Med. 2014 Feb 1;174(2):281-5. doi: 10.1001/jamainternmed.2013.12745.
PubMed 24322781
Study at a glance
From the registry record, last updated by the sponsor on 4 Apr 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT03487952
- Acronym / sponsor ID
- NCLUNG
- Keywords
- Lung nodule, Computed tomography, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Collaborators
- Lu'an Municipal Hospital, North China Petroleum Bureau General Hospital
- Study start
- Apr 2018 (estimated)
- Primary completion
- Dec 2021 (estimated)
- Study completion
- Dec 2021 (estimated)
- First posted
- 4 Apr 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| LDCT screening group | – | Other: Questionnaire AdministrationPeople receive questionnaire administration at baseline, then subsequent yearly chest LDCT scan and follow up. |
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Questionnaire AdministrationSubjects 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. | Other | LDCT screening group |
Primary outcome
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).
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).
5 secondary outcomes
Sensitivity in the detection of clinically actionable lung nodules
Time frame: 3 months
Growth of lung nodule
Time frame: 3 years
Anxiety and depression level
Time frame: 3 months
Life quality and health status
Time frame: 3 months
Lung cancer detection rate
Time frame: 3 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Apr 2018.
- Background: PubMed 23972816Field JK, Oudkerk M, Pedersen JH, Duffy SW. Prospects for population screening and diagnosis of lung cancer. Lancet. 2013 Aug 24;382(9893):732-41. doi: 10.1016/S0140-6736(13)61614-1.
- Background: PubMed 28168954Silva M, Pastorino U, Sverzellati N. Lung cancer screening with low-dose CT in Europe: strength and weakness of diverse independent screening trials. Clin Radiol. 2017 May;72(5):389-400. doi: 10.1016/j.crad.2016.12.021. Epub 2017 Feb 4.
- Background: PubMed 21714641National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
- Background: PubMed 23649455Detterbeck FC, Mazzone PJ, Naidich DP, Bach PB. Screening for lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013 May;143(5 Suppl):e78S-e92S. doi: 10.1378/chest.12-2350.
- Background: PubMed 26135833Baldwin DR, Callister ME; Guideline Development Group. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules. Thorax. 2015 Aug;70(8):794-8. doi: 10.1136/thoraxjnl-2015-207221. Epub 2015 Jul 1.
- Background: PubMed 22814873Wiener RS, Gould MK, Woloshin S, Schwartz LM, Clark JA. What do you mean, a spot?: A qualitative analysis of patients' reactions to discussions with their physicians about pulmonary nodules. Chest. 2013 Mar;143(3):672-677. doi: 10.1378/chest.12-1095.
- Background: PubMed 24322781Harris RP, Sheridan SL, Lewis CL, Barclay C, Vu MB, Kistler CE, Golin CE, DeFrank JT, Brewer NT. The harms of screening: a proposed taxonomy and application to lung cancer screening. JAMA Intern Med. 2014 Feb 1;174(2):281-5. doi: 10.1001/jamainternmed.2013.12745.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Feng F Yang, MD contact
Jun J Wang, MM principal investigator
Peking University People's Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Solitary Pulmonary Nodule and Multiple Pulmonary Nodules, run by Peking University People's Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 4 Apr 2018. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jun J Wang, MM (+8601088326650, xiongwai@263.net), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection rate of lung nodule (time frame: 3 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03487952, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Apr 2018.