Texture-Based Analysis of 100 MR Examinations of Head and Neck Tumors – Is It Possible to Discriminate Between Benign and Malignant Masses in a Multicenter Trial?

Abstract

Aim: To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors.

Materials and Methods: The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2 D and 3 D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values.

Results: There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found.

Conclusion: Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data.

Key Points:

1. 2 D/3 D texture-based analysis can be performed in head and neck tumors

2. Texture-based analysis can differentiate between benign and malignant masses

3. Analyzed MR images should originate from one scanner with an identical protocol

Citation Format:

• Fruehwald-Pallamar J., Hesselink J. R., Mafee M. F. et al. Texture-Based Analysis of 100 MR Examinations of Head and Neck Tumors – Is It Possible to Discriminate Between Benign and Malignant Masses in a Multicenter Trial?. Fortschr Röntgenstr 2016; 188: 195 – 202

Zusammenfassung

Ziel: Ziel der Studie war die Auswertung der Texturanalyse in Bezug auf eine mögliche Unterscheidung zwischen benignen und malignen Kopf-Hals-Raumforderungen mittels konventioneller MRT-Sequenzen.

Material und Methoden: Die MRT-Daten von 100 Patienten mit histologisch verifizierten Kopf-Hals-Raumforderungen aus zwei Institutionen wurden mit einer Texturanalyse-Software untersucht. Dafür wurden 2D- und 3D-Messfelder auf allen axialen Sequenzen eingezeichnet. Folgende Texturparameter wurden für alle Messfelder berechnet: COC, RUN, GRA, ARM und WAV. Benigne und maligne Raumforderungen wurden anhand von zehn Untergruppen einer linearen Diskriminanzanalyse mit einer k-nearest-neighbor-Klassifikation zugeführt.

Ergebnisse: Die Bilder unterschieden sich aufgrund des Fabrikats und der Feldstärke der MRT-Geräte voneinander. Es war bei folgenden Sequenzen möglich zwischen benignen und malignen RF mittels TA zu differenzieren: auf den axialen STIR und T2-gewichteten-Bildern mit 2D-Messfeldern, und auf den kontrastmittelverstärkten T1-gewichteten Bilder mit Fettunterdrückung für 3D-Messfelder. In einer Subgruppenanalyse für 1,5 T- und 3T-Feldstärke konnten weitere diskriminierende Parameter erarbeitet werden.

Schlussfolgerung: Es ist möglich benigne und maligne Kopf-Hals-Raumforderungen anhand von Texturparametern zu unterscheiden, falls diese mit einem einheitlichen Protokoll auf einem Gerät untersucht werden. Wir können diese Methode allerdings nicht für eine Multicenterstudie empfehlen.

Kernaussagen:

1. Kopf-Hals-Raumforderungen können mittels 2D/3D-Texturanalyse untersucht werden

2. Es ist möglich benigne und maligne Raumforderungen anhand von Texturparametern zu unterschieden.

3. Die MRT Untersuchung sollte mit gleichem Protokoll auf einem Gerät stattfinden.

Key words

head/neck - tissue characterization - MR imaging - technology assessment

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