RecruitingNCT05572944

Formatting the Risk Prediction Models for Never-Smoking Lung Cancer

Validation and Optimization of Multidimensional Modelling for Never Smoking Lung Cancer Risk Prediction by Multicenter Prospective Study

Sponsor

Chung Shan Medical University

Enrollment

10,000 participants

Start Date

Dec 15, 2022

Study Type

OBSERVATIONAL

Conditions

Lung Cancer

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.

Eligibility

Min Age: 20 YearsMax Age: 80 Years

Plain Language Summary

Simplified for easier understanding

This study is developing and refining a risk prediction tool specifically for people who have never smoked but may be at risk for lung cancer. Researchers want to better identify who among nonsmokers should be screened with a CT scan. **You may be eligible if...** - You are between 50 and 80 years old - You are a first-degree relative (parent, sibling, or child) of someone diagnosed with lung cancer **You may NOT be eligible if...** - You have a personal history of lung cancer - You have had another cancer (other than certain skin cancers or cervical carcinoma in situ) in the past 5 years - You had a chest CT scan within the past 18 months - You have had unexplained coughing up blood in the past month - You have lost more than 6 kg (about 13 lbs) without explanation in the past year - You are pregnant Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

OTHERLDCT 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.

OTHERLDCT 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.