Formatting the Risk Prediction Models for Never-Smoking Lung Cancer (FORMOSA)

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.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer
• Intervention / Treatment: Other: LDCT lung cancer screen, immediately; Other: LDCT lung cancer screen, later

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. The other aim is to validate current PM2.5-based lung cancer risk prediction models among nonsmokers, and optimize existing model with environmental and occupational factors in higher resolution. 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.

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 Type: Observational
• Enrollment (Estimated): 10000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: GEECHEN CHANG, MD. PhD; Phone Number: 34414 +886-4-24739595; Email: geechen@gmail.com

Study Locations

• Taiwan
  • Hsinchu, Taiwan
    • Recruiting
    • National Taiwan University Hospital Hsin-Chu Branch
    • Contact: Chong-Jen Yu, MD PhD
  • Hualien City, Taiwan
    • Recruiting
    • Hualien Tzu Chi Hospital
    • Contact: Chung-Ping Hsu, MD PhD
  • Kaohsiung, Taiwan
    • Recruiting
    • Kaohsiung Medical University Chung-Ho Memorial Hospital
    • Contact: Inn-Wen Chong, MD PhD
  • Kaohsiung, Taiwan
    • Not yet recruiting
    • E-DA Hospital
    • Contact: Yu-Feng Wei, MD PhD
  • New Taipei City, Taiwan
    • Not yet recruiting
    • Ministry of Health and Welfare Shuang-Ho Hospital
    • Contact: Po-Hao Feng, MD PhD
  • Taichung, Taiwan, 402
    • Recruiting
    • Chung Shan Medical University Hospital
    • Contact: GEECHEN CHANG, MD, PhD; Phone Number: 34414 +886-4-24739595; Email: geechen@gmail.com
  • Taipei City, Taiwan, 100229
    • Recruiting
    • National Taiwan University Hospital
    • Contact: Chao-Chi Ho, MD PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 78 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Never-smoking population

Description

Inclusion Criteria:

1. Age 50-80 years old

2. 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 Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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.; 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.; Other Names: to develop a risk model and assess the lung cancer risk
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; 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.; Other Names: to develop a risk model and assess the lung cancer risk

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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

Collaborators and Investigators

• Sponsor: Chung Shan Medical University
• Collaborators: Ministry of Health and Welfare, Taiwan

Study record dates

Study Major Dates

• Study Start (Actual): December 15, 2022
• Primary Completion (Estimated): December 31, 2025
• Study Completion (Estimated): December 31, 2029

Study Registration Dates

• First Submitted: September 29, 2022
• First Submitted That Met QC Criteria: October 7, 2022
• First Posted (Actual): October 10, 2022

Study Record Updates

• Last Update Posted (Actual): February 12, 2024
• Last Update Submitted That Met QC Criteria: February 8, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Keywords: Lung cancer; Machine learning; Low-dose computed tomography
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: MOHW111-TDU-B-221-114019

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No