Formatting the Risk Prediction Models for Never-Smoking Lung Cancer

Overview

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.

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.