Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study.

Tao Hua,Yan Liu,Yihui Guan,Zhirui Zhou,Yakang Dai,Qi Huang,Jianbo Wen,Yuhua Zhu,Ming Li,Weiyan Zhou

doi:10.3389/fonc.2021.772703

Abstract

PurposeWe aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.MethodsFifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.ResultsThe AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).ConclusionThe predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

Highlights

Glioma is the most frequently-occurred primary malignant tumor in the brain
The purpose of this research was to investigate the efficacy of predictive radiomics models originating from positron emission tomography (PET) modality, Computed tomography (CT) modality and combined PET/CT modalities generated with machine learning algorithms based on static 18F-FET PET/CT imaging in noninvasive Isocitrate dehydrogenase (IDH) prediction, and to compare with the conventional semi-quantitative parameter analysis
DeLong test results indicated that PET/CT-Rad Model outperformed the PET-Rad Model and the simple model in IDH genotype prediction (p = 0.048 and p = 0.034, respectively) (Figure 5)

Summary

Introduction

Glioma is the most frequently-occurred primary malignant tumor in the brain. The 2016 WHO classification of the central nervous system tumor introduced a new integrated classification mode of glioma [1]. Isocitrate dehydrogenase (IDH) mutation is considered to be an early event in the occurrence and development of glioma [2]. IDH mutations were identified with a high percentage in low-grade gliomas and secondary glioblastoma multiforme (GBM) but with a much lower percentage in primary GBM [3]. IDH mutation altered the metabolism and microstructure of gliomas, affecting the biological characteristics and prognosis [4]. The identification of IDH mutation status was mainly based on surgical resection or biopsy specimens. Additional surgical risks related to patient’s comorbidities, advanced age, deep-seated tumors are the barriers to accurately detect the IDH mutation status. Reliable methods which could noninvasively detect IDH mutation status are needed

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