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Breast MRI: does a clinical decision algorithm outweigh reader experience?
OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experienc...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9474540/ https://www.ncbi.nlm.nih.gov/pubmed/35852572 http://dx.doi.org/10.1007/s00330-022-09015-8 |
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author | Pötsch, Nina Korajac, Aida Stelzer, Philipp Kapetas, Panagiotis Milos, Ruxandra-Iulia Dietzel, Matthias Helbich, Thomas H. Clauser, Paola Baltzer, Pascal A. T. |
author_facet | Pötsch, Nina Korajac, Aida Stelzer, Philipp Kapetas, Panagiotis Milos, Ruxandra-Iulia Dietzel, Matthias Helbich, Thomas H. Clauser, Paola Baltzer, Pascal A. T. |
author_sort | Pötsch, Nina |
collection | PubMed |
description | OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. METHODS: Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. RESULTS: A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05). CONCLUSION: The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. KEY POINTS: • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09015-8. |
format | Online Article Text |
id | pubmed-9474540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-94745402022-09-16 Breast MRI: does a clinical decision algorithm outweigh reader experience? Pötsch, Nina Korajac, Aida Stelzer, Philipp Kapetas, Panagiotis Milos, Ruxandra-Iulia Dietzel, Matthias Helbich, Thomas H. Clauser, Paola Baltzer, Pascal A. T. Eur Radiol Breast OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. METHODS: Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. RESULTS: A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723–0.742) as well as the three residents was equal (AUC 0.842–0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts’ ratings using the MR BI-RADS scale (p ≤ 0.05). CONCLUSION: The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical “problem solving MRI” setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. KEY POINTS: • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical “problem solving MRI” setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09015-8. Springer Berlin Heidelberg 2022-07-19 2022 /pmc/articles/PMC9474540/ /pubmed/35852572 http://dx.doi.org/10.1007/s00330-022-09015-8 Text en © The Author(s) 2022 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 | Breast Pötsch, Nina Korajac, Aida Stelzer, Philipp Kapetas, Panagiotis Milos, Ruxandra-Iulia Dietzel, Matthias Helbich, Thomas H. Clauser, Paola Baltzer, Pascal A. T. Breast MRI: does a clinical decision algorithm outweigh reader experience? |
title | Breast MRI: does a clinical decision algorithm outweigh reader experience? |
title_full | Breast MRI: does a clinical decision algorithm outweigh reader experience? |
title_fullStr | Breast MRI: does a clinical decision algorithm outweigh reader experience? |
title_full_unstemmed | Breast MRI: does a clinical decision algorithm outweigh reader experience? |
title_short | Breast MRI: does a clinical decision algorithm outweigh reader experience? |
title_sort | breast mri: does a clinical decision algorithm outweigh reader experience? |
topic | Breast |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9474540/ https://www.ncbi.nlm.nih.gov/pubmed/35852572 http://dx.doi.org/10.1007/s00330-022-09015-8 |
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