[Research progress on the lung cancer risk prediction models]

Area of Science:

• Oncology
• Medical Informatics

Background:

• Lung cancer has high global incidence and mortality rates.
• Early detection and accurate diagnosis are vital for patient prognosis.
• Lung cancer risk prediction models are increasingly valuable for optimizing screening.

Purpose of the Study:

• To review current research on lung cancer risk prediction models.
• To focus on recent advances in variable selection, model construction, and validation.
• To discuss trends, clinical applications, prospects, and challenges.

Main Methods:

• Review of traditional statistical models for lung cancer risk prediction.
• Review of machine learning approaches for lung cancer risk prediction.
• Analysis of variable selection, model construction, and performance validation.

Main Results:

• Summarizes progress in lung cancer risk prediction models.
• Highlights advances in both statistical and machine learning methodologies.
• Discusses key trends and challenges in clinical application.

Conclusions:

• Lung cancer risk models are essential for efficient screening tools.
• Further development is needed to enhance clinical applicability.
• This review provides a reference for future model development and application.