Universität zu Köln

Laukamp, Kai Roman, Shakirin, Georgy, Baessler, Bettina ORCID: 0000-0002-3244-3864, Thiele, Frank, Zopfs, David ORCID: 0000-0001-9978-7453, Hokamp, Nils Grosse, Timmer, Marco, Kabbasch, Christoph ORCID: 0000-0003-3712-2258, Perkuhn, Michael and Borggrefe, Jan ORCID: 0000-0003-2908-7560 (2019). Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading. World Neurosurg., 132. S. E366 - 25. NEW YORK: ELSEVIER SCIENCE INC. ISSN 1878-8769

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Abstract

OBJECTIVE: Meningioma grading is relevant to therapy decisions in complete or partial resection, observation, and radiotherapy because higher grades are associated with tumor growth and recurrence. The differentiation of low and intermediate grades is particularly challenging. This study attempts to apply radiomics-based shape and texture analysis on routine multiparametric magnetic resonance imaging (MRI) from different scanners and institutions for grading. METHODS: We used MRI data (T1-weighted/T2-weighted, T1-weighted-contrast-enhanced [T1CE], fluid-attenuated inversion recovery [FLAIR], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC]) of grade I (n = 46) and grade II (n = 25) nontreated meningiomas with histologic workup. Two experienced radiologists performed manual tumor segmentations on FLAIR, T1CE, and ADC images in consensus. The MRI data were preprocessed through T1CE and T1-subtraction, coregistration, resampling, and normalization. A PyRadiomics package was used to generate 990 shape/texture features. Stepwise dimension reduction and robust radiomics feature selection were performed. Biopsy results were used as standard of reference. RESULTS: Four statistically independent radiomics features were identified as showing the strongest predictive values for higher tumor grades: roundness-of-FLAIR-shape (area under curve [AUC], 0.80), cluster-shades-of-FLAIR/T1CE-gray-level (AUC, 0.80), DWI/ADC-gray-level-variability (AUC, 0.72), and FLAIR/T1CE-gray-level-energy (AUC, 0.76). In a multivariate logistic regression model, the combination of the features led to an AUC of 0.91 for the differentiation of grade I and grade II meningiomas. CONCLUSIONS: Our results indicate that radiomics-based feature analysis applied on routine MRI is viable for meningioma grading, and a multivariate logistic regression model yielded strong classification performances. More advanced tumor stages are identifiable through certain shape parameters of the lesion, textural patterns in morphologic MRI sequences, and DWI/ADC variability.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Laukamp, Kai Roman UNSPECIFIED UNSPECIFIED UNSPECIFIED Shakirin, Georgy UNSPECIFIED UNSPECIFIED UNSPECIFIED Baessler, Bettina UNSPECIFIED orcid.org/0000-0002-3244-3864 UNSPECIFIED Thiele, Frank UNSPECIFIED UNSPECIFIED UNSPECIFIED Zopfs, David UNSPECIFIED orcid.org/0000-0001-9978-7453 UNSPECIFIED Hokamp, Nils Grosse UNSPECIFIED UNSPECIFIED UNSPECIFIED Timmer, Marco UNSPECIFIED UNSPECIFIED UNSPECIFIED Kabbasch, Christoph UNSPECIFIED orcid.org/0000-0003-3712-2258 UNSPECIFIED Perkuhn, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Borggrefe, Jan UNSPECIFIED orcid.org/0000-0003-2908-7560 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-126446
DOI:	10.1016/j.wneu.2019.08.148
Journal or Publication Title:	World Neurosurg.
Volume:	132
Page Range:	S. E366 - 25
Date:	2019
Publisher:	ELSEVIER SCIENCE INC
Place of Publication:	NEW YORK
ISSN:	1878-8769
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HISTOGRAM ANALYSIS; PREOPERATIVE MRI; TEXTURE ANALYSIS; SEGMENTATION; BENIGN; SYSTEM Multiple languages Clinical Neurology; Surgery Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/12644