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

Inclusion Criteria4

Age 50-80 years old
First-degree relatives of lung cancer patients
aged more than 50 - 80 years old
or older than the age at diagnosis of the youngest lung cancer the proband in the family if they are less than 50 years old

Exclusion Criteria7

Previous history of lung cancer
Another malignancy except for cervical carcinoma in situ or non-melanomatous carcinoma of the skin within 5 years
An inability to tolerate transthoracic procedures or thoracotomy
Chest CT examination was performed within 18 months
Hemoptysis of unknown etiology within one month
Body weight loss of more than 6 kg within one year without an evident cause
A known pregnancy

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.