Formatting the Risk Prediction Models for Never-Smoking Lung Cancer

"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."

"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."