Lung CancerFORMOSANCT05572944
Formatting the Risk Prediction Models for Never-Smoking Lung Cancer
An observational study of Lung Cancer, run by Chung Shan Medical University at 7 sites in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer
- What is being tested?
- LDCT lung cancer screen, immediately and LDCT lung cancer screen, later
- How many people
- 10,000 participants
- Where
- 7 sitesTaiwan
- Study dates
- 15 Dec 2022to 31 Dec 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Never-smoking population
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
7 sites in 1 country. Contact the study team or a site near you to ask about taking part.
7 sites in 1 country
Taiwan7 sites, 7 recruiting
- Recruiting
National Taiwan University Hospital Hsin-Chu Branch
Hsinchu
Chong-Jen Yu, MD PhD
- Recruiting
Hualien Tzu Chi Hospital
Hualien City
Chung-Ping Hsu, MD PhD
- Not yet recruiting
E-Da Hospital
Kaohsiung City
Yu-Feng Wei, MD PhD
- Recruiting
Kaohsiung Medical University Chung-Ho Memorial Hospital
Kaohsiung City
Inn-Wen Chong, MD PhD
- Not yet recruiting
Ministry of Health and Welfare Shuang-Ho Hospital
New Taipei City
Po-Hao Feng, MD PhD
- Recruiting
Chung Shan Medical University Hospital
Taichung, Taiwan, 402
GEECHEN CHANG, MD, PhD, +886-4-24739595 ext. 34414, geechen@gmail.com
- Recruiting
National Taiwan University Hospital
Taipei, 100229
Chao-Chi Ho, MD PhD
About this study
Official title: Validation and Optimization of Multidimensional Modelling for Never Smoking Lung Cancer Risk Prediction by Multicenter Prospective Study
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.
Read the detailed description
To achieve the goal there are four programs proposed.
Program 1: Validating non-smoker lung cancer prediction model among Taiwanese population: Integration with environmental and occupational factors. The investigators aim to enhance the accuracy of lung cancer prediction among Taiwanese non-smokers by incorporating environmental and occupational risk factors. The main aim of this program is to validate and optimize existing prediction models with more comprehensive epidemiologic, environmental and occupational factors with machine learning algorithms. Another aim is to validate existing air pollution-based lung cancer risk prediction models for nonsmokers and optimize them by incorporating higher-resolution environmental and occupational factors. The investigators hypothesize adding more GIS-based environmental exposure measurements, and occupational exposure using job-exposure matrix as proxy can increase the predictive power of lung cancer risk model. We also collect urine samples for metal analysis.
Program 2: Validation of autoantibody- and genetic prediction model for non-smoker lung cancer. The investigators detect the 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. The investigators will validate the prediction power of these autoantibodies and genetic biomarkers in the early diagnosis of patients with high risk of acquiring lung cancer in Taiwan.
Program 3: Detection, classification, prediction of lung cancer risk in CT using deep learning and radiomics. The investigators propose an integrated platform for detecting and following up lung nodules. A similarity measurement approach between two nodules is proposed. Base on Lung RADS assessment, the investigators plan to perform CT-radiomic analysis for nodules larger than or equal to 6-8 mm diameter aimed to find nodules in higher risk of developing lung cancer. The lung nodules will be detected and followed up by using a series of AIs. The detected nodules could be used for producing report and estimating Lung-RADS. Though Lung-RADS has considered the risk of malignancy based on their categories, the expectation of this project is to efficiently select CT screen high risk lung nodule(s) by using volume measurement, morphology, texture and CT radiomics of the detected nodules in addition to Lung-RADS criteria based on nodule size and characters.
Program 4: Optimization and validation of lung cancer risk and probability prediction model: prospective multicenter clinical study. The program 4 will first use retrospective cohort based the case control research design to optimize the lung cancer risk models from program 1 and the biomarker and imaging models from program 2 and 3, respectively. The prospective multi-center research design will further use to verify the optimized predictive model. The high-risk participants will be selected to measure for biomarkers and undergo LDCT. The optimized biomarker model and image feature models will be performed to predict the probability of lung cancer and compared it with conventional clinical diagnosis methods and low risk participants. Finally, the Taiwanese population suitable lung cancer screening strategy will be proposed.
- Conditions
- Lung Cancer
- Being tested
- LDCT lung cancer screen, immediately, LDCT lung cancer screen, later
- Keywords
- Lung cancer, Low-dose computed tomography, Machine learning
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
LDCT lung cancer screen, immediatelyOther
Also called to develop a risk model and assess the lung cancer risk
Participants will receive the following things in sequence
- Non-smoker lung cancer prediction model among Taiwanese population by questionnaire
- 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
- Check total bilirubin, urinary heavy metals, serum tumor marker, including CEA, alpha-fetal protein, etc.
- Check pulmonary function test and chest X ray
- Arrenge chest CT right away, and detection, classification, prediction of lung cancer risk in CT using deep learning and radiomics
- Optimization and validation of lung cancer risk and probability prediction model: prospective multicenter clinical study.
LDCT lung cancer screen, laterOther
Also called to develop a risk model and assess the lung cancer risk
Participants will receive the following things in sequence
- Non-smoker lung cancer prediction model among Taiwanese population by questionnaire
- 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
- Check total bilirubin, urinary heavy metals, serum tumor marker, including CEA, alpha-fetal protein, etc.
- Check pulmonary function test.
- Arrange AI-asisted chest X-ray right away.
- Arrange chest CT three years later, and detection, classification, prediction of lung cancer risk in CT using deep learning and radiomics
- Optimization and validation of lung cancer risk and probability prediction model: prospective multicenter clinical study.
| Study group | Role | What they receive |
|---|---|---|
| Never smoker with lung cancer high risk assessment | High risk: above the median of the initial risk model from retrospective study Receives: 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 Receives: Other: LDCT lung cancer screen, later |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
- 10,000 non-smoker participants will receive a prespecified questionnaire
- 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
- 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.
Measured: 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)
Measured: 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Oct 2022
- Study start15 Dec 2022
- Primary completion31 Dec 2025
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chung Shan Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Health and Welfare, Taiwan
- Responsible party
- Principal investigator, Gee-Chen Chang, Chung Shan Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05572944
- Sponsor's protocol ID
- MOHW111-TDU-B-221-114019
- Acronym
- FORMOSA
- Status last verified
- Feb 2026
- First submitted
- 29 Sep 2022
- Last updated
- 17 Feb 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation and Optimization of Multidimensional Modelling for Never Smoking Lung Cancer Risk Prediction by Multicenter Prospective Study
- ClinicalTrials.gov ID
- NCT05572944
- Acronym / sponsor ID
- FORMOSA / MOHW111-TDU-B-221-114019
- Conditions
- Lung Cancer
- Keywords
- Lung cancer, Low-dose computed tomography, Machine learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Chung Shan Medical UniversityUniversity, hospital or other
- Collaborators
- Ministry of Health and Welfare, Taiwan
- Study start
- 15 Dec 2022 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 10 Oct 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
To achieve the goal there are four programs proposed.
Program 1: Validating non-smoker lung cancer prediction model among Taiwanese population: Integration with environmental and occupational factors. The investigators aim to enhance the accuracy of lung cancer prediction among Taiwanese non-smokers by incorporating environmental and occupational risk factors. The main aim of this program is to validate and optimize existing prediction models with more comprehensive epidemiologic, environmental and occupational factors with machine learning algorithms. Another aim is to validate existing air pollution-based lung cancer risk prediction models for nonsmokers and optimize them by incorporating higher-resolution environmental and occupational factors. The investigators hypothesize adding more GIS-based environmental exposure measurements, and occupational exposure using job-exposure matrix as proxy can increase the predictive power of lung cancer risk model. We also collect urine samples for metal analysis.
Program 2: Validation of autoantibody- and genetic prediction model for non-smoker lung cancer. The investigators detect the 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. The investigators will validate the prediction power of these autoantibodies and genetic biomarkers in the early diagnosis of patients with high risk of acquiring lung cancer in Taiwan.
Program 3: Detection, classification, prediction of lung cancer risk in CT using deep learning and radiomics. The investigators propose an integrated platform for detecting and following up lung nodules. A similarity measurement approach between two nodules is proposed. Base on Lung RADS assessment, the investigators plan to perform CT-radiomic analysis for nodules larger than or equal to 6-8 mm diameter aimed to find nodules in higher risk of developing lung cancer. The lung nodules will be detected and followed up by using a series of AIs. The detected nodules could be used for producing report and estimating Lung-RADS. Though Lung-RADS has considered the risk of malignancy based on their categories, the expectation of this project is to efficiently select CT screen high risk lung nodule(s) by using volume measurement, morphology, texture and CT radiomics of the detected nodules in addition to Lung-RADS criteria based on nodule size and characters.
Program 4: Optimization and validation of lung cancer risk and probability prediction model: prospective multicenter clinical study. The program 4 will first use retrospective cohort based the case control research design to optimize the lung cancer risk models from program 1 and the biomarker and imaging models from program 2 and 3, respectively. The prospective multi-center research design will further use to verify the optimized predictive model. The high-risk participants will be selected to measure for biomarkers and undergo LDCT. The optimized biomarker model and image feature models will be performed to predict the probability of lung cancer and compared it with conventional clinical diagnosis methods and low risk participants. Finally, the Taiwanese population suitable lung cancer screening strategy will be proposed.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Never smoker with lung cancer high risk assessment | – | Other: LDCT lung cancer screen, immediatelyHigh risk: above the median of the initial risk model from retrospective study |
| Never smoker with lung cancer low risk assessment | – | Other: LDCT lung cancer screen, laterLow risk: below the median of the initial risk model from retrospective study |
Who may qualify
- Ages
- 20 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Never-smoking population
Key inclusion criteria (4 listed)
- Age 50-80 years old
- First-degree relatives of lung cancer patients
Key exclusion criteria (7 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| LDCT lung cancer screen, immediatelyAlso: to develop a risk model and assess the lung cancer riskParticipants 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. | Other | Never smoker with lung cancer high risk assessment |
| LDCT lung cancer screen, laterAlso: to develop a risk model and assess the lung cancer riskParticipants 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. | Other | Never smoker with lung cancer low risk assessment |
Primary outcome
Lung cancer detection rate differences between the high lung cancer risk group and the low lung cancer risk group.
Time frame: 4 years
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.
Predicted Area under curve (AUC) value > 0.8 of the lung cancer risk model
Time frame: 4 years
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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
7 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by Chung Shan Medical University at 7 sites in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 7 sites in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact GEECHEN CHANG, MD. PhD (+886-4-24739595 ext. 34414, geechen@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Lung cancer detection rate differences between the high lung cancer risk group and the low lung cancer risk group. (time frame: 4 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Feb 2026; AzTrials last refreshed its copy on 6 Nov 2026.