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<p>This study presents a reproducible methodological framework for constructing a longitudinal low-dose computed tomography (LDCT) screening image database and demonstrates its implementation using 11&#xa0;years of real-world data from a single region in Japan. The framework integrates hierarchical identifier design, deterministic metadata linkage, structured anonymization, and a minimal metadata specification to enable consistent organization of longitudinal imaging data. The implemented database comprises 45,337 LDCT examinations from 23,065 examinees, with 47.0% undergoing repeated screening. All examinations were acquired using a standardized CT scanner and protocol and were linked to screening assessment categories and smoking exposure information. The resulting structure supports reproducible subject-level aggregation, temporal tracking, and quantitative image analysis. This framework provides a transferable model for institutions seeking to construct longitudinal screening imaging repositories.</p>

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A reproducible framework for constructing a longitudinal low-dose CT screening image database: implementation using 11 years of real-world data

  • 期刊论文

  • 发表日期：

    2026年6月19日

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  • Junji Shiraishi,
  • Rie Tanaka,
  • Tetsuo Matsunaga,
  • Tetsuya Minami,
  • Satoshi Kobayashi

摘要

This study presents a reproducible methodological framework for constructing a longitudinal low-dose computed tomography (LDCT) screening image database and demonstrates its implementation using 11 years of real-world data from a single region in Japan. The framework integrates hierarchical identifier design, deterministic metadata linkage, structured anonymization, and a minimal metadata specification to enable consistent organization of longitudinal imaging data. The implemented database comprises 45,337 LDCT examinations from 23,065 examinees, with 47.0% undergoing repeated screening. All examinations were acquired using a standardized CT scanner and protocol and were linked to screening assessment categories and smoking exposure information. The resulting structure supports reproducible subject-level aggregation, temporal tracking, and quantitative image analysis. This framework provides a transferable model for institutions seeking to construct longitudinal screening imaging repositories.