Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression

Abstract

Purpose To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas. Patients and Methods One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBRmean, TBRmax, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis. Results Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBRmean and TBRmax resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%). Conclusion The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

Diagnostic accuracy of glioma pseudoprogression identification with positron emission tomography imaging: a systematic review and meta-analysis

“…Figure 1 shows the PRISMA flowchart of this meta-analysis. A total of 1,293 records were identified, 4 of which ( 9 , 26 - 28 ) were manually retrieved from other sources. First, 401 records were removed due to being duplicates, and another 540 records were excluded based on the type of screening.…”

Research on application of radiomics in glioma: a bibliometric and visual analysis

“…Müller M et al. ( 54 ) developed a PET-based radiomic classifier that showed high accuracy in differentiating treatment-related changes (TRC) from tumor progression (TP) in gliomas. Distinguishing between glioblastoma and isolated brain metastases may be challenging due to the similar appearance of both on MRI.…”

Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation

“…A recent systematic review of the current status and quality of radiomics for glioma differential diagnosis in 2022 showed that the radiomic quality score (RQS) of 42 studies was only 24.21%, which meant that current radiomic studies for glioma differential diagnosis still lack the quality required to allow its introduction into clinical practice [ 26 , 27 ]. We identified several research trends based on radiomics and gliomas (not only DMG), including construction using multiparametric magnetic resonance radiomics (several MRI sequences combined with genotype status and clinical features), [ 25 , 28 , 29 ]; PET-extracted radiomics [ 30 , 31 , 32 , 33 ]; radiomics-based machine learning [ 34 , 35 , 36 ]; predictive models of recurrence [ 37 , 38 ]; survival and classification in gliomas [ 39 , 40 , 41 ]; and differential diagnosis [ 42 , 43 ].…”