Automated data extraction tool (DET) for external applications in radiotherapy

被引:1
|
作者
Gurjar, Mruga [1 ]
Lindberg, Jesper [1 ,2 ,3 ]
Bjork-Eriksson, Thomas [4 ]
Olsson, Caroline [1 ,3 ]
机构
[1] Univ Gothenburg, Inst Clin Sci, Sahlgrenska Acad, Med Radiat Sci, Gothenburg, Sweden
[2] Sahlgrens Univ Hosp, Dept Med Phys & Biomed Engn, Gothenburg, Sweden
[3] Reg Canc Ctr West, Western Sweden Healthcare Reg, Gothenburg, Sweden
[4] Univ Gothenburg, Inst Clin Sci, Sahlgrenska Acad, Dept Oncol, Gothenburg, Sweden
基金
瑞典研究理事会;
关键词
Radiotherapy; Data extraction; Data cleaning; Automation;
D O I
10.1016/j.tipsro.2022.12.001
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose: Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. Methods and materials: We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015-16). Extracted data included patients' referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. Results: From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and +/- 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. Conclusion: We successfully implemented a software tool to prepare ready-touse OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.
引用
收藏
页数:7
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