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Formatting the Risk Prediction Models for Never-Smoking Lung Cancer
Sponsor: Chung Shan Medical University
Summary
Lung Cancer is the leading cause of cancer-related deaths in Taiwan and worldwide and the incidence is also increasing. The payment for lung cancer which occupies the largest part of National Health Insurance expense is over 15 billion in 2018. Because about 80% lung cancer patients are smokers in western countries the low-dose computed tomography screening focuses on the smoking population It is quite different in South-East Asia particularly in Taiwan that 53% of Taiwan lung cancer are never-smokers and the etiology and the underlying mechanisms are still unknown. The preliminary results of prospective TALENT study indicated that family history plays a key role in tumorigenesis of Taiwan lung cancers but several important variables such as air pollution, biomarkers, radiomics analysis are not available limits the accuracy of lung cancer identification. Hence, it is critical to integrate most of factors involved in lung cancer formation into a multidimensional lung cancer prediction model which could benefit never-smoker lung cancers in Taiwan and East Asia even in the western countries. The investigators initiate a clinical study to validate the multidimensional lung cancer prediction model for never-smoking population by multicenter prospective study.
Official title: Validation and Optimization of Multidimensional Modelling for Never Smoking Lung Cancer Risk Prediction by Multicenter Prospective Study
Key Details
Gender
All
Age Range
20 Years - 80 Years
Study Type
OBSERVATIONAL
Enrollment
10000
Start Date
2022-12-15
Completion Date
2029-12-31
Last Updated
2026-02-17
Healthy Volunteers
Yes
Conditions
Interventions
LDCT lung cancer screen, immediately
Participants will receive the following things in sequence 1. Non-smoker lung cancer prediction model among Taiwanese population by questionnaire 2. Check autoantibodies against p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2 in the blood of recruited patients and detect 133 SNPs and 11 mitochondrial mutations which are highly correlated with never-smoking lung cancer in our preliminary data 3. Check total bilirubin, urinary heavy metals, serum tumor marker, including CEA, alpha-fetal protein, etc. 4. Check pulmonary function test and chest X ray 5. Arrenge chest CT right away, and detection, classification, prediction of lung cancer risk in CT using deep learning and radiomics 6. Optimization and validation of lung cancer risk and probability prediction model: prospective multicenter clinical study.
LDCT lung cancer screen, later
Participants will receive the following things in sequence 1. Non-smoker lung cancer prediction model among Taiwanese population by questionnaire 2. Check autoantibodies against p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4 and SOX2 in the blood of recruited patients and detect 133 SNPs and 11 mitochondrial mutations which are highly correlated with never-smoking lung cancer in our preliminary data 3. Check total bilirubin, urinary heavy metals, serum tumor marker, including CEA, alpha-fetal protein, etc. 4. Check pulmonary function test. 5. Arrange AI-asisted chest X-ray right away. 6. Arrange chest CT three years later, and detection, classification, prediction of lung cancer risk in CT using deep learning and radiomics 7. Optimization and validation of lung cancer risk and probability prediction model: prospective multicenter clinical study.
Locations (7)
Chung Shan Medical University Hospital
Taichung, Taiwan, Taiwan
National Taiwan University Hospital Hsin-Chu Branch
Hsinchu, Taiwan
Hualien Tzu Chi Hospital
Hualien City, Taiwan
E-Da Hospital
Kaohsiung City, Taiwan
Kaohsiung Medical University Chung-Ho Memorial Hospital
Kaohsiung City, Taiwan
Ministry of Health and Welfare Shuang-Ho Hospital
New Taipei City, Taiwan
National Taiwan University Hospital
Taipei, Taiwan