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.
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
5,000
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.
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
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
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
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
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
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
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
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