Abstract

PurposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.Materials and MethodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman’s correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan–Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.ResultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman’s correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p < 0.001 and p < 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74–0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.ConclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

Highlights

  • Among all primary brain and other central nervous system tumors, gliomas account for about 26% (Ostrom et al, 2018)

  • This study aimed to develop machine learning models that can be used to predict the prognosis of isocitrate dehydrogenase (IDH)-wildtype GBM based on the radiomics features extracted from multicenter, multi-parameter MRI images

  • The second part was from the public databases The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA; Clark et al, 2013)

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Summary

Introduction

Among all primary brain and other central nervous system tumors, gliomas account for about 26% (Ostrom et al, 2018). Among all malignant brain and other central nervous system tumors, glioblastoma (GBM) is the most common (Soomro et al, 2017). Several previous studies have confirmed that the status of isocitrate dehydrogenase (IDH) mutation has a great impact on the prognosis of GBM patients (Parsons et al, 2008; Hartmann et al, 2013; Reifenberger et al, 2014). According to the latest classification criteria of the World Health Organization (WHO) in 2021, the common diffuse gliomas of adults have been divided into only three types: astrocytoma, IDH-mutant; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and glioblastoma, IDH-wild type. Based on the new classification criteria, a more reasonable and personalized prognosis analysis method can be constructed, which will directly affect the condition assessment, targeted treatment, and follow-up management of IDH-wild-type GBM patients (Louis et al, 2021)

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