Formatting the Risk Prediction Models for Never-Smoking Lung Cancer

ConditionLung Cancer

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age20-80

SponsorChung Shan Medical University

About this trial

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 criteria

This trial accepts healthy volunteers

Qualifiers

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

Disqualifiers

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

Trial population

Never-smoking population

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

LDCT lung cancer screen, immediately
Other intervention
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
Other intervention
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.

Treatment groups

10,000 Participants

are divided into 2 treatment groups

Group A: Never smoker with lung cancer high risk assessment1 intervention

Group B: Never smoker with lung cancer low risk assessment1 intervention

Trial outcomes

Primary outcomes

Lung cancer detection rate differences between the high lung cancer risk group and the low lung cancer risk group.

Participants will receive the following things in sequence 1. 10,000 non-smoker participants will receive a prespecified questionnaire 2. Autoantibodies will be checked including p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGE A4, and SOX2 in the blood of recruited participants. All 133 SNPs and 11 mitochondrial mutations will be detected which are highly correlated with never-smoking lung cancer in our preliminary data 3. In the high-risk group, the investigators will arrange LDCT scans for four rounds to determine the lung cancer detection rate. Also, the pulmonary nodule lesions detected will be classified by Lung-RADS and prediction of lung cancer risk in CT scans using deep learning and radiomics. In the low-risk group, the matched participants will receive LDCT scans for two rounds to determine the lung cancer detection rate.

Time frame

4 years

Predicted Area under curve (AUC) value > 0.8 of the lung cancer risk model

Through steps 1,2, and 3 of the above column in primary outcome 1, the lung cancer risk model will be developed with optimization and validation of lung cancer risk and probability prediction model by this prospective multicenter study. ( predicted Area under curve (AUC) \> 0.8)

Time frame

4 years

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Chung Shan Medical University

Lead sponsor

Ministry of Health and Welfare, Taiwan

Collaborator

Source ClinicalTrials.gov

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