Gliomas are the most common primary brain tumors that originate from neuroepithelial cells and can occur in any part of the central nervous system(CNS) (1). Previously, World Health Organization(WHO) classified gliomas into grades I-IV, with grades I and II considered low-grade gliomas (LGG) and grades III and IV considered high-grade gliomas (HGG) (2), with a median survival of 14 months for glioblastomas (grade IV) and more than 7 years for grades II and III gliomas (3, 4). However, many studies have reported that the prognosis of gliomas is not related to the pathological grade, but is mainly based on the molecular characteristics of the tumor, and if the same genotype exists, similar biological behavior and prognosis may exist even if the tumors have different pathological grades (5, 6). The 2016 WHO CNS tumor classification included molecular features, particularly isocitrate dehydrogenase (IDH), which is divided into IDH mutant(IDH-M)and IDH wild type(IDH-W),on a histological basis for the first time and used it as one of the important bases for molecular typing of gliomas (7, 8). Low-grade gliomas with IDH-W are similar to glioblastomas in terms of molecular features and prognosis, while IDH-M gliomas have a better prognosis than IDH-W (9, 10). The impact of total tumor resection on the prognosis of low-grade gliomas has been reported to depend on IDH mutation status (11). Therefore, preoperative prediction of IDH status is necessary for appropriate treatment planning.

Currently, IDH genotypes are identified mainly by sequencing or immunohistochemistry of tumor specimens, which can only be obtained after surgery, and even biopsies of unresectable gliomas carry the risk of neurological impairment, and the small samples obtained do not reflect the full heterogeneity of the entire tumor (6, 12, 13). To overcome these limitations, there is an urgent need to establish a non-invasive technique to identify the IDH genotype of the tumor (14, 15), thus MRI examination is of great value in the preoperative diagnosis of glioma. At this stage, studies have evaluated the performance of various machine learning algorithms in predicting glioma genotypes (16–20). High-throughput features from MRI have been shown to be highly advantageous and effective in predicting the classification of IDH (15). Conventional MRI examinations correlate with IDH genotype and its prognosis by tumor morphology, border and enhancement, but most of them rely on the subjective diagnosis of radiologists and cannot be analyzed comprehensively from the whole tumor area. Radiomics can extract a large number of intrinsic features that cannot be observed by the naked eye and analyze the shape and texture of images (14, 21–23), which shows great advantages and values in the diagnosis of glioma.

In recent years, studies on preoperative prediction of tumor genotype by radiomics have been widely carried out, and the methods and results of different studies are not the same. Zhang et al. (18) predicted IDH-M in LGGs preoperatively by multiparametric MRI radiomics model and obtained AUC value of 0.83, with T2-weighted imaging(T2WI) images being the most important.Another study titled “Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging, 2022 (24)” concluded that a multiparametric radiomics model of T2-weighted-fluid-attenuated inversion recovery (T2-FLAIR) is most effective in distinguishing IDH mutation status in low-grade gliomas. However, Niu et al. (20) found that contrast-enhanced T1-weighted imaging (CE-T1WI) radiomics model could effectively predict IDH genotype in high-grade glioma, which is inconsistent with the results of the the two aforementioned studies. Therefore, despite numerous studies on using radiomics models to predict the IDH genotype status of gliomas, the research results are not conclusive and still exhibit certain differences. Further research from more clinical centers is required to enhance the accuracy of the model’s results. Sun et al (25) concluded that a combined machine learning algorithm exhibits excellent predictive performance in non-invasively predicting the molecular subtypes of lower-grade glioma (LGG) preoperatively. Several studies have incorporated clinical data into radiomics to build a combined model and found superior results (24–27). Zhou et al (28) and Tan et al (27). concluded that incorporating age information can improve the predictive results of the models. Furthermore, compared to imaging features, age information has a higher predictive value. This finding is of great importance for clinical practice, as clinical information can be obtained preoperatively and can provide more valuable information for treatment and prognosis. Therefore, more research results are needed to corroborate this conclusion. Furthermore, several studies have integrated functional sequences like perfusion⁃weighted imaging(PWI) and diffusion tensor imaging(DTI) to develop radiomics models for the prediction of IDH gene status in gliomas (29, 30). These studies have achieved satisfactory outcomes. However, a meta-analysis (1) indicates that despite the growing adoption of advanced imaging sequences for constructing feature models, traditional MRI sequences exhibit superior specificity in predicting IDH gene status in gliomas.Currently, most research is limited to studying low-grade or high-grade gliomas, which are limited by pathological findings and have variable results.

In this study, all high-grade and low-grade gliomas were included and constructed radiomics models based on multiparametric MRI sequences, including T1WI, T2-FLAIR, CE-T1WI, and apparent diffusion coefficient (ADC).Additionally, a combined model of radiomics features and clinical data has been established, making the model results more stable and enabling a more comprehensive prediction of the applicative value of the IDH genotype in gliomas.

In this study, the IDH genotype of grade II-IV glioma was predicted and analyzed based on a multiparametric MRI radiomics model, and the clinical data were statistically significant in terms of patient age and whether the tumor was enhancing or not, and the clinical model and radiomics model were combined for prediction. The combined multiparametric model of T1WI + CE-T1WI + T2-FAIR + ADC had the best diagnostic performance. Thus, it can be seen that features obtained jointly from MR images of multiparametric sequences can better predict glioma IDH genotypes with higher diagnostic performancd than single sequence studies, and that combined radiomics features of multiparametric sequences can quantify comprehensive information on glioma heterogeneity. CE-T1WI contains information on local angiogenesis and blood-brain barrier disruption of the tumor. T2-FLAIR reflects the anatomical information of the tumor, and ADC provides information on the structure and density of the tumor cells. we also found that the diagnostic performance of the radiomics model of CE-T1WI was the highest in single-sequence studies, and its diagnostic performance was higher than that of ADC maps. some studies have shown that DWI sequences are not grading or stable indicator of molecular subtypes, which may be responsible for this result (29–34).

We finally extracted the most significant feature including five features on T1WI, six features on CE-T1WI, four features on T2-FLAIR, and four features on ADC. For T1WI+CE-T1WI + T2-FLAIR + ADC images, eight features were selected which included 2 first-order features and 6 texture features. The first-order features are obtained by calculating the tumor’s gray value and can respond to the tumor’s gray intensity distribution. They also capture the tumor’s heterogeneity, representing low-dimensional information easily perceived by vision. Texture features, including GLSZM, GLCM, and GLRLM, quantify the texture or tissue distribution within the tumor. These features are difficult to perceive visually but can provide information about the structure of tumor cells and the microenvironment (13, 22). In our study, we used filters to extract radiomics features from the original images. Most of the final independent imaging features comprise wavelet features, which analyze the spatial frequency changes in a comprehensive way. These features effectively capture high-frequency and low-frequency signals in the image, allowing for a detailed analysis of texture changes. The wavelet features can describe clinical problems related to the visual features of tumor images (30). Additionally, it is believed that wavelet features may contribute to our understanding of tumor morphology, pathophysiology, and proteomics (35).

In 2016, WHO classified gliomas into mutant and wild-type according to the IDH gene in the classification of central nervous system tumors (7, 8), and the IDH gene is an important genetic marker of glioma that plays an important role in glioma metabolism, pathogenesis and progression (2, 10). A growing number of studies have shown the clinical importance of genotype in developing treatment plans and assessing prognosis (20–23). Pathology is still the gold standard for diagnosis, but histological examination, as an invasive test, is invasive and has sample error, especially when stereotactic biopsy is performed. In this study, we attempted to predict IDH genotype of glioma non-invasively before surgery by the method of radiomics model based on MRI images, and provide some reference and guidance for clinical selection of surgical and postoperative radiotherapy regimens, and the results showed that this method can predict IDH genotype well.

Radiomics can dig deeper into the intrinsic features of medical images through machine learning methods and extract a large number of quantitative features that cannot be observed by the naked eye, which can support the implementation of precision medicine and individualized treatment. Some radiomics-based studies have also focused on the modeling of conventional sequences, and the obtained conventional structural sequences can reveal basic information about the tumor, such as the location, size, whether the glioma is combined with necrotic cystic lesions, the extent of edema, and the blood supply, which is helpful to provide more informed clinical information (1, 13, 23, 25). In this paper, the clinical data of the tumors were compiled in detail and statistically analyzed, in which age and whether the tumor intensified were statistically significant, indicating that age and tumor intensification were independent risk factors for predicting the IDH genotype of glioma.

Previous studies have suggested that gender is a risk factor for IDH mutation, potentially due to hormonal fluctuations in females (27, 36). However, our study found that gender did not exhibit significant predictive capability in univariate logistic regression analysis. This discrepancy underscores the need for additional research to ascertain the relevance of gender in predictive models.Although more and more scholars are using advanced techniques of MRI to analyze the relationship between IDH genotype and glioma, kim et al. (37) concluded that DWI and PWI have high diagnostic performance in predicting IDH mutations in low-grade glioma, with ADC features playing a significant role.In contrast, our study found that CE-T1WI played a more significant role, which is inconsistent with the results of this article.And in addition Park et al. (30) found that adding DTI imaging histology to conventional serial radiomics significantly improved the predictive accuracy of IDH status in low-grade glioma. However, a meta-analysis (1) revealed that despite an increasing number of scholars using more advanced examination sequences to establish feature models, the conventional MRI sequence imaging model showed better specificity in predicting IDH genotype.Niu et al. (20) found that a radiomics model based on preoperative enhanced MR was effective in predicting the IDH gene in high-grade gliomas, this is consistent with the research findings of this study, but we are not limited to a single MRI sequence. Instead, it is based on the analysis of multiple-parameter conventional MRI sequences and combined models, which are more stable and accurate. The present study was not limited to a single MRI sequence, but modeled and combined models based on multiparametric conventional MRI sequences for analysis. Tan et al. (27) studied the use of age as a clinical model to predict IDH mutations in astrocytoma based on radiomics, clinical and combined models and found that the combined model had higher diagnostic performance, this part of the research results is consistent with the findings of this study, but it only indicates the importance of age as an independent risk factor for preoperative prediction of IDH mutation in gliomas. However, this study also includes tumor enhancement as a variable in the model. Importantly, both age and tumor enhancement demonstrate satisfactory results as important variables in the model, and clinical data can be easily obtained before surgery. This is crucial for the stability of the model and its clinical application. Li et al. (17) extracted features from the enhanced, non-enhanced, necrotic, edematous, tumor core, and six regions of the whole tumor on multiparametric MRI, respectively, and showed that multiregional radiomics models can predict the mutational status of glioblastoma preoperatively. Most studies have focused on high-grade gliomas or low-grade gliomas for experimental studies (16, 18, 20, 23–25, 31, 38). The present study did not include pathological grading in the model characteristics, making the study unrestricted by pathological grading, extending the clinical application of radiomic models, and offering the possibility of preoperative prediction of IDH gene status in glioma.

This study still has some limitations. Firstly, the sample size of our study is relatively small, which may obscure the predictive value of clinical data and radiomics features of patients. This should be further investigated in larger cohorts. Secondly, we conducted a retrospective study and selected the IDH genotype for analysis. As we gather more cases, we will further investigate the relationship between abnormal expression of other important genotypes and imaging features. Lastly, our study is a single-center study, and in the future, it is necessary to collect multi-center data to validate the stability performance of the radiomics model.

The multiparametric radiomics model performs better than other single sequence models in predicting the IDH genotype of gliomas. After incorporating features such as patient age and whether the tumor was enhancing or not, the results of the clinical-radiomics model are more satisfactory, indicating that the combined model is an effective tool for predicting the IDH genotype. Furthermore, the variable parameters obtained in the model contribute differently to the prediction of the IDH genotype. These findings will be beneficial for future research on using brain tumor imaging to predict molecular status and tumor invasiveness.

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

The studies involving humans were approved by the ethics committee of the Aerospace Center Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

YL: Data curation, Formal Analysis, Investigation, Writing – original draft, Writing – review & editing, Conceptualization. WL: Writing – review & editing, Supervision. DB: Methodology, Data curation, Writing – review & editing. JH: Methodology, Data curation, Writing – review & editing. ZW: Writing – review & editing, Conceptualization, Supervision.