Natural history models for lung Cancer: A scoping review

Area of Science:

• Oncology
• Biostatistics
• Health Informatics

Background:

• Natural history models (NHMs) simulate lung cancer (LC) progression, providing a crucial baseline for intervention impact assessment.
• NHMs are increasingly vital for informing public health policies related to lung cancer.
• This scoping review aims to consolidate knowledge on existing LC NHMs, pinpoint their limitations, and suggest a framework for future development.

Purpose of the Study:

• To systematically review and synthesize the landscape of existing lung cancer natural history models.
• To identify the limitations and gaps in current LC NHMs.
• To propose a comprehensive framework for the development of next-generation LC NHMs.

Main Methods:

• A comprehensive literature search was conducted across major databases (MEDLINE, Embase, Web of Science, IEEE Xplore) up to October 5, 2023.
• Peer-reviewed, full-length articles detailing LC NHMs were included.
• Data on model characteristics, applications, data sources, and limitations were extracted and synthesized.

Main Results:

• The review identified 22 original LC NHMs from 69 publications, with microsimulation being the predominant approach (68%).
• Most models (91%) incorporated basic risk factors (age, sex, smoking history), but few addressed never-smokers (14%) or recurrence (5%).
• Significant gaps exist, with no models considering non-tobacco smoking, nodule type, or biomarker expression; a framework for future models was proposed.

Conclusions:

• Existing lung cancer natural history models have notable limitations that require addressing.
• Regular updates and further research are essential to enhance the accuracy and relevance of LC NHMs.
• The proposed framework aims to guide future NHM development, incorporating critical factors for a more comprehensive understanding of lung cancer.