Universität zu Köln

Lohmann, Philipp ORCID: 0000-0002-5360-046X, Elahmadawy, Mai A., Gutsche, Robin, Werner, Jan-Michael, Bauer, Elena K., Ceccon, Garry, Kocher, Martin, Lerche, Christoph W., Rapp, Marion, Fink, Gereon R., Shah, Nadim J., Langen, Karl-Josef ORCID: 0000-0003-1101-5075 and Galldiks, Norbert (2020). FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation. Cancers, 12 (12). BASEL: MDPI. ISSN 2072-6694

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Abstract

Simple Summary Following chemoradiation with alkylating agents in glioma patients, structural magnetic resonance imaging (MRI) may suggest tumor progression which subsequently improves during the course of the disease without any treatment change. This phenomenon has been termed pseudoprogression. Despite advances in medical imaging, a reliable diagnosis of pseudoprogression remains a challenging task. Radiomics is a subdiscipline of artificial intelligence and allows the identification and extraction of imaging features from various routine imaging modalities. These features can be used for the generation of mathematical models to improve diagnostics in patients with brain tumors. The present study highlights the potential of radiomics obtained from amino acid positron emission tomography (PET) for the diagnosis of pseudoprogression. In 34 patients with suspicious MRI early after chemoradiation completion, our radiomics model correctly identified all patients with pseudoprogression. Currently, a reliable diagnostic test for differentiating pseudoprogression from early tumor progression is lacking. We explored the potential of O-(2-[F-18]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) radiomics for this clinically important task. Thirty-four patients (isocitrate dehydrogenase (IDH)-wildtype glioblastoma, 94%) with progressive magnetic resonance imaging (MRI) changes according to the Response Assessment in Neuro-Oncology (RANO) criteria within the first 12 weeks after completing temozolomide chemoradiation underwent a dynamic FET PET scan. Static and dynamic FET PET parameters were calculated. For radiomics analysis, the number of datasets was increased to 102 using data augmentation. After randomly assigning patients to a training and test dataset, 944 features were calculated on unfiltered and filtered images. The number of features for model generation was limited to four to avoid data overfitting. Eighteen patients were diagnosed with early tumor progression, and 16 patients had pseudoprogression. The FET PET radiomics model correctly diagnosed pseudoprogression in all test cohort patients (sensitivity, 100%; negative predictive value, 100%). In contrast, the diagnostic performance of the best FET PET parameter (TBRmax) was lower (sensitivity, 81%; negative predictive value, 80%). The results suggest that FET PET radiomics helps diagnose patients with pseudoprogression with a high diagnostic performance. Given the clinical significance, further studies are warranted.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Lohmann, Philipp UNSPECIFIED orcid.org/0000-0002-5360-046X UNSPECIFIED Elahmadawy, Mai A. UNSPECIFIED UNSPECIFIED UNSPECIFIED Gutsche, Robin UNSPECIFIED UNSPECIFIED UNSPECIFIED Werner, Jan-Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Bauer, Elena K. UNSPECIFIED UNSPECIFIED UNSPECIFIED Ceccon, Garry UNSPECIFIED UNSPECIFIED UNSPECIFIED Kocher, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Lerche, Christoph W. UNSPECIFIED UNSPECIFIED UNSPECIFIED Rapp, Marion UNSPECIFIED UNSPECIFIED UNSPECIFIED Fink, Gereon R. UNSPECIFIED UNSPECIFIED UNSPECIFIED Shah, Nadim J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Langen, Karl-Josef UNSPECIFIED orcid.org/0000-0003-1101-5075 UNSPECIFIED Galldiks, Norbert UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-309331
DOI:	10.3390/cancers12123835
Journal or Publication Title:	Cancers
Volume:	12
Number:	12
Date:	2020
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2072-6694
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language RESPONSE ASSESSMENT; RADIATION-THERAPY; IMAGING PREDICTOR; BRAIN; F-18-FDG; FEATURES; SYSTEM; MECHANISMS; DIAGNOSIS; SURVIVAL Multiple languages Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/30933