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Principal Investigator
Name
Chin-Chi Kuo
Degrees
MD, PhD, MPH, FNKF
Institution
China Medical University Hospital Taiwan
Position Title
Vice Superintendent
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-1313
Initial CDAS Request Approval
Aug 28, 2024
Title
Development and Validation of an Intelligent Risk Prediction Model for Lung Diseases Based on the NLST Cohort
Summary
Lung diseases, particularly lung cancer, are among the leading causes of morbidity and mortality worldwide. Early detection and risk stratification are crucial for improving patient outcomes and optimizing screening strategies. The NLST dataset, with its comprehensive clinical, demographic, and imaging data, offers a unique opportunity to advance predictive modeling in this critical area of public health. The proposed study aims to develop and validate a predictive model for lung disease risk.
Our research team intends to leverage advanced machine learning and deep learning techniques to create a robust and accurate model capable of predicting the risk of lung diseases, including lung cancer, among high-risk populations. The model will incorporate various predictive factors such as demographic characteristics, clinical parameters, and imaging findings. Utilizing a training dataset from NLST, we will explore multiple algorithms, such as logistic regression, random forests, and neural networks, etc, to identify the most suitable model architecture. The model will be developed to predict the risk of various lung diseases, with a primary focus on lung cancer.
Aims

Aim 1: Development of a Predictive Model for Lung Disease Risk
Objective: To develop a machine learning-based predictive model for lung disease risk, with a focus on lung cancer.
Approach: We will explore and compare different machine learning algorithms, such as logistic regression, random forests, and neural networks, to identify the most effective model. The model will be trained on a subset of the NLST data, utilizing a wide range of predictors including demographic data, smoking history, and LDCT imaging results.

Aim 2: Validation of the Predictive Model
Objective: To validate the developed predictive model using an independent subset of the NLST dataset.
Approach: The model’s performance will be evaluated using key metrics such as AUC-ROC, sensitivity, specificity, and calibration. We will ensure the model's robustness through cross-validation and will conduct subgroup analyses to assess its generalizability across different patient populations.

Aim 3: Clinical Utility and Comparison with Existing Tools
Objective: To assess the clinical utility of the predictive model in comparison to existing lung disease risk prediction tools.
Approach: We will evaluate the model's potential impact on clinical decision-making by comparing its predictive accuracy and practical utility against currently used models.

Collaborators

None