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

Solitary Pulmonary Nodule Clinical Trial

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT03487952
• Other study ID #: NCLUNG
• Status: Not yet recruiting
• First received: March 21, 2018
• Last updated: March 27, 2018
• Start date: April 2018
• Est. completion date: December 2021

Study information

• Verified date: March 2018
• Source: Peking University People's Hospital
• Contact: Jun J Wang, MM
• Phone: +8601088326650
• Email: xiongwai[@]263.net
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 5000
• Est. completion date: December 2021
• Est. primary completion date: December 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 40 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Multiple Pulmonary Nodules
• Solitary Pulmonary Nodule

Intervention

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.

Locations

Country	Name	City	State
n/a

Sponsors (3)

Lead Sponsor	Collaborator
Peking University People's Hospital	Lu'an Municipal Hospital, North China Petroleum Bureau General Hospital

References & Publications (7)

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. Review. — View Citation

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. Review. — View Citation

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. Review. — View Citation

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. Review. Erratum in: JAMA Intern Med. 2014 Mar;174(3):484. — View Citation

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. — View Citation

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. Review. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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).	3 months
Primary	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).	3 months
Secondary	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).	3 months
Secondary	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).	3 years
Secondary	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).	3 months
Secondary	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).	3 months
Secondary	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)	3 months