Universität zu Köln

Kebir, Sied, Schmidt, Teresa, Weber, Matthias, Lazaridis, Lazaros, Galldiks, Norbert, Langen, Karl-Josef ORCID: 0000-0003-1101-5075, Kleinschnitz, Christoph, Hattingen, Elke, Herrlinger, Ulrich, Lohmann, Philipp ORCID: 0000-0002-5360-046X and Glas, Martin (2020). A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma. Cancers, 12 (11). BASEL: MDPI. ISSN 2072-6694

Full text not available from this repository.

Abstract

Simple Summary Pseudoprogression detection in glioblastoma patients remains a challenging task. Although pseudoprogression has only a moderate prevalence of 10-30% following first-line treatment of glioblastoma patients, it bears critical implications for affected patients. Non-invasive techniques, such as amino acid PET imaging using the tracer O-(2-[F-18]-fluoroethyl)-L-tyrosine (FET), expose features that have been shown to provide useful information to distinguish tumor progression from pseudoprogression. The usefulness of FET-PET in IDH-wildtype glioblastoma exclusively, however, has not been investigated so far. Recently, machine learning (ML) algorithms have been shown to offer great potential particularly when multiparametric data is available. In this preliminary study, a Linear Discriminant Analysis-based ML algorithm was deployed in a cohort of newly diagnosed IDH-wildtype glioblastoma patients (n = 44) and demonstrated a significantly better diagnostic performance than conventional ROC analysis. This preliminary study is the first to assess the performance of ML in FET-PET for diagnosing pseudoprogression exclusively in IDH-wildtype glioblastoma and demonstrates its potential. Pseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[F-18]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Kebir, Sied UNSPECIFIED UNSPECIFIED UNSPECIFIED Schmidt, Teresa UNSPECIFIED UNSPECIFIED UNSPECIFIED Weber, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED Lazaridis, Lazaros UNSPECIFIED UNSPECIFIED UNSPECIFIED Galldiks, Norbert UNSPECIFIED UNSPECIFIED UNSPECIFIED Langen, Karl-Josef UNSPECIFIED orcid.org/0000-0003-1101-5075 UNSPECIFIED Kleinschnitz, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Hattingen, Elke UNSPECIFIED UNSPECIFIED UNSPECIFIED Herrlinger, Ulrich UNSPECIFIED UNSPECIFIED UNSPECIFIED Lohmann, Philipp UNSPECIFIED orcid.org/0000-0002-5360-046X UNSPECIFIED Glas, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-313006
DOI:	10.3390/cancers12113080
Journal or Publication Title:	Cancers
Volume:	12
Number:	11
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; ARTIFICIAL-INTELLIGENCE; TEMOZOLOMIDE; CRITERIA; GLIOMAS; RADIOTHERAPY; PROGRESSION; CONCOMITANT; RADIOLOGY; DIAGNOSIS Multiple languages Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/31300