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Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case

PURPOSE: To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports. METHODS: The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department’s structured reporting (SR) platform. The...

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Autores principales: Jorg, Tobias, Halfmann, Moritz C., Rölz, Niklas, Mager, René, Pinto dos Santos, Daniel, Düber, Christoph, Mildenberger, Peter, Müller, Lukas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10556151/
https://www.ncbi.nlm.nih.gov/pubmed/37466646
http://dx.doi.org/10.1007/s00261-023-04006-9
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author Jorg, Tobias
Halfmann, Moritz C.
Rölz, Niklas
Mager, René
Pinto dos Santos, Daniel
Düber, Christoph
Mildenberger, Peter
Müller, Lukas
author_facet Jorg, Tobias
Halfmann, Moritz C.
Rölz, Niklas
Mager, René
Pinto dos Santos, Daniel
Düber, Christoph
Mildenberger, Peter
Müller, Lukas
author_sort Jorg, Tobias
collection PubMed
description PURPOSE: To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports. METHODS: The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department’s structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated. RESULTS: The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36–62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy. CONCLUSION: SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt. GRAPHICAL ABSTRACT: [Image: see text]
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spelling pubmed-105561512023-10-07 Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case Jorg, Tobias Halfmann, Moritz C. Rölz, Niklas Mager, René Pinto dos Santos, Daniel Düber, Christoph Mildenberger, Peter Müller, Lukas Abdom Radiol (NY) Kidneys, Ureters, Bladder, Retroperitoneum PURPOSE: To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports. METHODS: The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department’s structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated. RESULTS: The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36–62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy. CONCLUSION: SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt. GRAPHICAL ABSTRACT: [Image: see text] Springer US 2023-07-19 2023 /pmc/articles/PMC10556151/ /pubmed/37466646 http://dx.doi.org/10.1007/s00261-023-04006-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Kidneys, Ureters, Bladder, Retroperitoneum
Jorg, Tobias
Halfmann, Moritz C.
Rölz, Niklas
Mager, René
Pinto dos Santos, Daniel
Düber, Christoph
Mildenberger, Peter
Müller, Lukas
Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
title Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
title_full Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
title_fullStr Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
title_full_unstemmed Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
title_short Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
title_sort structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case
topic Kidneys, Ureters, Bladder, Retroperitoneum
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10556151/
https://www.ncbi.nlm.nih.gov/pubmed/37466646
http://dx.doi.org/10.1007/s00261-023-04006-9
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