Formatting the Risk Prediction Models for Never-Smoking Lung Cancer

NCT05572944 | Recruiting

• 1 other identifier: MOHW111-TDU-B-221-114019
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 7

Brief 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.

Conditions

Lung Cancer

Keywords

Lung cancer; Low-dose computed tomography; Machine learning

Outcome Measures

Primary Outcomes (2)

• 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.

  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)

  4 years

Study Arms (2)

Never smoker with lung cancer high risk assessment

High risk: above the median of the initial risk model from retrospective study

Other: LDCT lung cancer screen, immediately

Never smoker with lung cancer low risk assessment

Low risk: below the median of the initial risk model from retrospective study

Other: LDCT lung cancer screen, later

Interventions

LDCT lung cancer screen, later OTHER

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.

Also known as: to develop a risk model and assess the lung cancer risk

Never smoker with lung cancer low risk assessment

LDCT lung cancer screen, immediately OTHER

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.

Also known as: to develop a risk model and assess the lung cancer risk

Never smoker with lung cancer high risk assessment

Eligibility Criteria

• Age: 20 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Never-smoking population

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Chung Shan Medical University: lead
• Ministry of Health and Welfare, Taiwan: collaborator

Study Sites (7)

Chung Shan Medical University Hospital

Taichung, Taiwan, 402, Taiwan

RECRUITING

National Taiwan University Hospital Hsin-Chu Branch

Hsinchu, Taiwan

RECRUITING

Hualien Tzu Chi Hospital

Hualien City, Taiwan

RECRUITING

E-Da Hospital

Kaohsiung City, Taiwan

NOT YET RECRUITING

Kaohsiung Medical University Chung-Ho Memorial Hospital

Kaohsiung City, Taiwan

RECRUITING

Ministry of Health and Welfare Shuang-Ho Hospital

New Taipei City, Taiwan

NOT YET RECRUITING

National Taiwan University Hospital

Taipei, 100229, Taiwan

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

blood samples, urine samples, lung tissue samples.

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

GEECHEN CHANG, MD. PhD

CONTACT

+886-4-24739595; geechen@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice president of Chung Shan Medical University

Study Record Dates

• First Submitted: September 29, 2022
• First Posted: October 10, 2022
• Study Start: December 15, 2022
• Primary Completion: December 31, 2025
• Study Completion (Estimated): December 31, 2029
• Last Updated: February 17, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

TW (7)