Clinical trial · Observational
Evaluation of Lung Nodule Detection With Artificial Intelligence Assisted Computed Tomography in North China
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Multiple Pulmonary Nodules | — | UNRESOLVED | — |
| Solitary Pulmonary Nodule | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Questionnaire Administration | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- LDCT screening group
- description
- People receive questionnaire administration at baseline, then subsequent yearly chest LDCT scan and follow up.
- interventionNames
- Other: Questionnaire Administration
Primary outcomes (2)
- measure
- Detection rate of lung nodule
- timeFrame
- 3 months
- description
- 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).
- measure
- Profile of detected lung nodule
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 40 Years
Show eligibility criteria text
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.
References
Publications (7)
- BACKGROUNDField 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. PMID 23972816
- BACKGROUNDSilva 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. PMID 28168954
- BACKGROUNDNational 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. PMID 21714641
- BACKGROUNDDetterbeck 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. PMID 23649455
- BACKGROUNDBaldwin 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. PMID 26135833
- BACKGROUNDWiener 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. PMID 22814873
- BACKGROUNDHarris 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.