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A reproducible framework for constructing a longitudinal low-dose CT screening image database: implementation using

Junji Shiraishi¹, Rie Tanaka², Tetsuo Matsunaga³

¹Department of Radiological Sciences, Faculty of Medical Science, Fukuoka International University of Health and Welfare, 2-4-16 Momochihama, Sawara-ku, Fukuoka, 814-0001, Japan. j2s@ihwg.jp.

Radiological Physics and Technology

|June 19, 2026

Summary

Researchers developed a framework to build a longitudinal low-dose computed tomography (LDCT) screening database. This reproducible method organizes 11 years of Japanese LDCT data for consistent analysis and future research.

Keywords:

Database framework Longitudinal imaging database Low-dose computed tomography Metadata integration Reproducibility

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Area of Science:

Medical Imaging
Radiology
Data Science

Background:

Longitudinal imaging data is crucial for screening studies.
Standardized frameworks are needed for managing large-scale screening databases.
Previous efforts lacked a unified, reproducible methodology for longitudinal LDCT data.

Purpose of the Study:

To present a reproducible methodological framework for constructing a longitudinal low-dose computed tomography (LDCT) screening image database.
To demonstrate the framework's implementation using real-world data.
To enable consistent organization and analysis of longitudinal imaging data.

Main Methods:

Developed a framework integrating hierarchical identifier design, deterministic metadata linkage, and structured anonymization.
Implemented the framework using 11 years of LDCT data from Japan.
Linked imaging data with screening assessment categories and smoking exposure information.

Main Results:

Created a database of 45,337 LDCT examinations from 23,065 examinees, with 47.0% undergoing repeated screening.
The implemented structure supports reproducible subject-level aggregation and temporal tracking.
Demonstrated the framework's capability for quantitative image analysis.

Conclusions:

The proposed framework provides a reproducible and transferable model for building longitudinal screening imaging repositories.
This methodology facilitates consistent organization and analysis of large-scale LDCT screening data.
The database supports advanced research in lung cancer screening and related fields.