Universität zu Köln

Zopfs, D., Laukamp, K., Reimer, R., Grosse Hokamp, N., Kabbasch, C., Borggrefe, J., Pennig, L., Bunck, A. C., Schlamann, M. and Lennartz, S. (2022). Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases. Am. J. Neuroradiol., 43 (2). S. 188 - 195. DENVILLE: AMER SOC NEURORADIOLOGY. ISSN 1936-959X

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Abstract

BACKGROUND AND PURPOSE: MR imaging is the technique of choice for follow-up of patients with brain metastases, yet the radiologic assessment is often tedious and error-prone, especially in examinations with multiple metastases or subtle changes. This study aimed to determine whether using automated color-coding improves the radiologic assessment of brain metastases compared with conventional reading. MATERIALS AND METHODS: One hundred twenty-one pairs of follow-up examinations of patients with brain metastases were assessed. Two radiologists determined the presence of progression, regression, mixed changes, or stable disease between the follow-up examinations and indicated subjective diagnostic certainty regarding their decisions in a conventional reading and a second reading using automated color-coding after an interval of 8 weeks. RESULTS: The rate of correctly classified diagnoses was higher (91.3%, 221/242, versus 74.0%, 179/242, P?<?.01) when using automated color-coding, and the median Likert score for diagnostic certainty improved from 2 (interquartile range, 2?3) to 4 (interquartile range, 3?5) (P?<?.05) compared with the conventional reading. Interrater agreement was excellent (? = 0.80; 95% CI, 0.71?0.89) with automated color-coding compared with a moderate agreement (? = 0.46; 95% CI, 0.34?0.58) with the conventional reading approach. When considering the time required for image preprocessing, the overall average time for reading an examination was longer in the automated color-coding approach (91.5 [SD, 23.1]?seconds versus 79.4 [SD, 34.7?] seconds, P?<?.001). CONCLUSIONS: Compared with the conventional reading, automated color-coding of lesion changes in follow-up examinations of patients with brain metastases significantly increased the rate of correct diagnoses and resulted in higher diagnostic certainty.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Zopfs, D. UNSPECIFIED UNSPECIFIED UNSPECIFIED Laukamp, K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Reimer, R. UNSPECIFIED UNSPECIFIED UNSPECIFIED Grosse Hokamp, N. UNSPECIFIED UNSPECIFIED UNSPECIFIED Kabbasch, C. UNSPECIFIED UNSPECIFIED UNSPECIFIED Borggrefe, J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Pennig, L. UNSPECIFIED UNSPECIFIED UNSPECIFIED Bunck, A. C. UNSPECIFIED UNSPECIFIED UNSPECIFIED Schlamann, M. UNSPECIFIED UNSPECIFIED UNSPECIFIED Lennartz, S. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-667205
DOI:	10.3174/ajnr.A7380
Journal or Publication Title:	Am. J. Neuroradiol.
Volume:	43
Number:	2
Page Range:	S. 188 - 195
Date:	2022
Publisher:	AMER SOC NEURORADIOLOGY
Place of Publication:	DENVILLE
ISSN:	1936-959X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MANAGEMENT; RADIOLOGY; SEARCH Multiple languages Clinical Neurology; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/66720