Universität zu Köln

Baltzer, Pascal Andreas Thomas, Krug, Kathrin Barbara and Dietzel, Matthias (2022). Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Rofo-Fortschr. Gebiet Rontgenstrahlen Bildgeb. Verfahr., 194 (11). S. 1216 - 1229. STUTTGART: GEORG THIEME VERLAG KG. ISSN 1438-9010

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Abstract

Background Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis. Method Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed. Results The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios. Citation Format Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Rontgenstr 2022; DOI: 10.1055/a-1829-5985 Background Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis. Method Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed. Results The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios. Citation Format Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Rontgenstr 2022; DOI: 10.1055/a-1829-5985

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Baltzer, Pascal Andreas Thomas UNSPECIFIED UNSPECIFIED UNSPECIFIED Krug, Kathrin Barbara UNSPECIFIED UNSPECIFIED UNSPECIFIED Dietzel, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-676955
DOI:	10.1055/a-1829-5985
Journal or Publication Title:	Rofo-Fortschr. Gebiet Rontgenstrahlen Bildgeb. Verfahr.
Volume:	194
Number:	11
Page Range:	S. 1216 - 1229
Date:	2022
Publisher:	GEORG THIEME VERLAG KG
Place of Publication:	STUTTGART
ISSN:	1438-9010
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CARCINOMA IN-SITU; PROGNOSTIC VALUE; MAMMOGRAPHY; CLASSIFICATION; LESIONS; SYSTEM; EDEMA; RECOMMENDATIONS; TOMOSYNTHESIS; ENHANCEMENT Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67695