ClinicalTrials.gg
Status unknownNCT03487952Updated Apr 4, 2018

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

An observational study in Solitary Pulmonary Nodule and Multiple Pulmonary Nodules, sponsored by Peking University People's Hospital. Status unknown. Open to participants aged 40 Years and older. Per ClinicalTrials.gov, last updated 2018-04-04.

Sponsored by Peking University People's Hospital · Observational

The sponsor has not verified this record recently (last verified Mar 2018), so the status shown — last known as Not yet recruiting — may be out of date.
Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
5,000
Ages
40 Years and older
Sex
All
01

Study 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. 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.

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Conditions studied

  • Solitary Pulmonary Nodule
  • Multiple Pulmonary Nodules

Keywords

  • Lung nodule
  • Computed tomography
  • Artificial intelligence
03

In context

Solitary Pulmonary Nodule

93 studies on the registry are indexed under Solitary Pulmonary Nodule; 20 are open to participants now.

This study's planned enrollment of 5,000 is above the median of 401 across 45 observational studies indexed under Solitary Pulmonary Nodule.

Browse Solitary Pulmonary Nodule studies →

Lead sponsor

Peking University People's Hospital is the lead sponsor of 609 studies on the registry; 247 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
40 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
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.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

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 Form.

Exclusion criteria

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 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.
Terms in these criteria, in plain language(2)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

Life expectancy

The study doctor’s estimate of how long someone is likely to live. Criteria use it so that people can stay in the study long enough for it to answer its question.

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Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
5,000 participants (estimated)
Patient registry
No

Groups and cohorts

  • LDCT screening group

    People receive questionnaire administration at baseline, then subsequent yearly chest LDCT scan and follow up.

    Other: Questionnaire Administration

Interventions

  • OtherQuestionnaire 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.

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What researchers measure

Primary outcomes

  1. 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).

    Time frame: 3 months

  2. 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).

    Time frame: 3 months

Secondary outcomes

  1. 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).

    Time frame: 3 months

  2. 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).

    Time frame: 3 years

  3. 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).

    Time frame: 3 months

  4. 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).

    Time frame: 3 months

  5. 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)

    Time frame: 3 months

07

Study locations

No study locations are listed for this record.

08

References and documents

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 23972816 ↗
  • Silva 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 28168954 ↗
  • National 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 21714641 ↗
  • Detterbeck 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 23649455 ↗
  • Baldwin 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 26135833 ↗
  • Wiener 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 22814873 ↗
  • Harris 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. Erratum In: JAMA Intern Med. 2014 Mar;174(3):484. PubMed 24322781 ↗
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Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Start is estimated for Apr 2018; primary completion for Dec 2021, 44 months later.

How these figures are worked out, and which studies are left out of them.

Registered
Mar 21, 2018
First posted
Apr 4, 2018
StartEst.
Apr 2018
Primary completionEst.
Dec 2021
CompletionEst.
Dec 2021
Last update
Apr 4, 2018
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Apr 4, 2018, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT03487952
Collaborators
Lu'an Municipal Hospital, North China Petroleum Bureau General Hospital
Responsible party
Jun Wang (Principal Investigator, Clinical Professor, Peking University People's Hospital) — Principal investigator
First posted
Apr 4, 2018
Start date
Apr 2018 (estimated)
Primary completion
Dec 2021 (estimated)
Completion
Dec 2021 (estimated)
Last update
Apr 4, 2018

Study contacts

Jun J Wang, MM
Contact
xiongwai@263.net
+8601088326650
Feng F Yang, MD
Contact
yangfeng1007@sina.com
Jun J Wang, MM
principal investigator · Peking University People's Hospital

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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