Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Can Radiomics Provide Additional Information in [18F]FET-Negative Glioma?

Katharina Von Rohr
,
Marcus Unterrainer
ORCID logo ,
Adrien Holzgreve
ORCID logo ,
Maximilian A. Kirchner
,
Zhicong Li
,
Lena M. Unterrainer
,
Bogdana Suchorska
,
Matthias Brendel
,
Jörg-Christian Tonn
,
Peter Bartenstein
,
Sibylle Ziegler
,
Nathalie L. Albert
,
Lena Kaiser
* ORCID logo
Version 1 : Received: 8 September 2022 / Approved: 9 September 2022 / Online: 9 September 2022 (09:31:45 CEST)

A peer-reviewed article of this Preprint also exists.

von Rohr, K.; Unterrainer, M.; Holzgreve, A.; Kirchner, M.A.; Zhicong, L.; Unterrainer, L.M.; Suchorska, B.; Brendel, M.; Tonn, J.-C.; Bartenstein, P.; Ziegler, S.; Albert, N.L.; Kaiser, L. Can Radiomics Provide Additional Information in [18F]FET-Negative Gliomas? Cancers 2022, 14, 4860. von Rohr, K.; Unterrainer, M.; Holzgreve, A.; Kirchner, M.A.; Zhicong, L.; Unterrainer, L.M.; Suchorska, B.; Brendel, M.; Tonn, J.-C.; Bartenstein, P.; Ziegler, S.; Albert, N.L.; Kaiser, L. Can Radiomics Provide Additional Information in [18F]FET-Negative Gliomas? Cancers 2022, 14, 4860.

Abstract

46 patients with a newly diagnosed, histologically verified glioma that was visually classified as [18F]FET-negative were included. Tumor volumes were defined using routine T2/FLAIR MRI data and applied to extract information from dynamic [18F]FET PET data, i.e. early and late tu-mor-to-background (TBR5-15, TBR20-40) images and time-to-peak (TTP) images. Radiomic features of healthy background were calculated from the tumor volume-of-interest mirrored to the con-tralateral hemisphere. Differences between tumor and healthy tissue features were compared us-ing Wilcoxon test. Additionally, the ability to distinguish tumor from healthy tissue was assessed using logistic regression. 5 % of features derived from TBR20-40 images were significantly differ-ent; 16 % of features derived from TBR5-15 images and 69 % of features derived from TTP images. The high number of significantly different features derived from TTP images was even found in isometabolic gliomas (after exclusion of photopenic gliomas) with visually normal [18F]FET up-take in static images. However, the differences did not reach satisfactory predictability for ma-chine learning based identification of tumor tissue. In conclusion, radiomic features derived from dynamic [18F]FET PET data may extract additional information even in [18F]FET-negative glio-mas, which should be investigated in larger cohorts and correlated with histological and out-come features in future studies.

Keywords

amino acid PET; FET PET; glioma; FET negative; photopenic; radiomics

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF Download Supplementary

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Supplementary Files Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.