IDH mutation-specific radiomic signature in lower-grade gliomas

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

A Prediction Model for Deciphering Intratumoral Heterogeneity Derived from the Microglia/Macrophages of Glioma Using Non-Invasive Radiogenomics

Microglia and macrophages play a major role in glioma immune responses within the glioma microenvironment. We aimed to construct a prognostic prediction model for glioma based on microglia/macrophage-correlated genes. Additionally, we sought to develop a non-invasive radiogenomics approach for risk stratification evaluation. Microglia/macrophage-correlated genes were identified from four single-cell datasets. Hub genes were selected via lasso-Cox regression, and risk scores were calculated. The immunological characteristics of different risk stratifications were assessed, and radiomics models were constructed using corresponding MRI imaging to predict risk stratification. We identified eight hub genes and developed a relevant risk score formula. The risk score emerged as a significant prognostic predictor correlated with immune checkpoints, and a relevant nomogram was drawn. High-risk groups displayed an active microenvironment associated with microglia/macrophages. Furthermore, differences in somatic mutation rates, such as IDH1 missense variant and TP53 missense variant, were observed between high- and low-risk groups. Lastly, a radiogenomics model utilizing five features from magnetic resonance imaging (MRI) T2 fluid-attenuated inversion recovery (Flair) effectively predicted the risk groups under a random forest model. Our findings demonstrate that risk stratification based on microglia/macrophages can effectively predict prognosis and immune functions in glioma. Moreover, we have shown that risk stratification can be non-invasively predicted using an MRI-T2 Flair-based radiogenomics model.

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Objective

This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers.

Methods

To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space.

Results

The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively.

Conclusion

The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.

MAGOH is correlated with poor prognosis and is essential for cell proliferation in lower-grade glioma

OBJECTIVE

Mago-nashi homolog (MAGOH) has been shown to play a pivotal part in various tumors. However, its specific contribution in lower-grade glioma (LGG) is still unknown.

METHODS

Pan-cancer analysis was implemented to inspect the expression characteristics and prognostic significance of MAGOH in multiple tumors. The associations between MAGOH expression patterns and the pathological features of LGG were analyzed, as were the connections between MAGOH expression and the clinical traits, prognosis, biological activities, immune features, genomic variations, and responses to treatment in LGG. Additionally, in vitro studies were performed to detect the expression levels and biomedical functions of MAGOH in LGG.

RESULTS

Abnormally increased levels of MAGOH expression were connected with adverse prognosis in patients with several types of tumors, including LGG. Importantly, we found that levels of MAGOH expression were independent prognostic biomarker of patients with LGG. Increased MAGOH expression was also highly associated with several immune-related markers, immune cell infiltration, immune checkpoint genes (ICPGs), gene mutations, and responses to chemotherapy in patients with LGG. In vitro studies ascertained that abnormally increased MAGOH was essential for cell proliferation in LGG.

CONCLUSION

MAGOH is a valid predictive biomarker in LGG and may become a novel therapeutic target in these patients.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

GRN is a prognostic biomarker and correlated with immune infiltration in glioma: A study based on TCGA data

Background

Among primary brain tumors, gliomas are associated with a poor prognosis and a median survival that varies depending on the tumor grade and subtype. As the most malignant form of glioma, glioblastoma (GBM) constitutes a significant health concern. Alteration in granulin(GRN) has been proved to be accountable for several diseases. However, the relationship between GRN and GBM remains unclear. We evaluated the role of GRN in GBM through The Cancer Genome Atlas (TCGA) database

Methods

First, we assessed the relationship between GRN and GBM through the GEPIA database. Next, the relationship between GRN and GBM prognosis was analyzed by logistic regression and multivariate cox methods. Using CIBERSORT and the GEPIA correlation module, we also investigated the link between GRN and immune infiltrates in cancer. Using the TCGA data, a gene set enrichment analysis (GSEA) was performed. We also employed Tumor Immune Estimation Resource (TIMER) to examine the data set of GRN expression and immune infiltration level in GBM and investigate the cumulative survival in GBM. We also validated tissues from GBM patients by Western blotting, RT-qPCR, and immunohistochemistry.

Results

Increased GRN expression was shown to have a significant relationship to tumor grade in a univariate study utilizing logistic regression. Furthermore, multivariate analysis disclosed that GRN expression down-regulation is an independent predictive factor for a favorable outcome. GRN expression level positively correlates with the number of CD4+ T cells, neutrophils, macrophages, and dendritic cells (DCs) that infiltrate a GBM. The GSEA also found that the high GRN expression phenotype pathway was enriched for genes involved in immune response molecular mediator production, lymphocyte-mediated immunity, cytokine-mediated signaling pathway, leukocyte proliferation, cell chemotaxis, and CD4+ alpha beta T cell activation. Differentially enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) include lysosome, apoptosis, primary immunodeficiency, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and B cell receptor signaling pathway. Validated result showed that GRN was upregulated in GBM tissues. These results suggested that GRN was a potential indicator for the status of GBM.

Conclusion

GRN is a prognostic biomarker and correlated with immune infiltrates in GBM.

Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging

The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a "6-Step" general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features (n = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.

Clinical management and survival outcomes of patients with different molecular subtypes of diffuse gliomas in China (2011-2017): a multicenter retrospective study from CGGA

OBJECTIVE

We aimed to summarize the clinicopathological characteristics and prognostic features of various molecular subtypes of diffuse gliomas (DGs) in the Chinese population.

METHODS

In total, 1,418 patients diagnosed with DG between 2011 and 2017 were classified into 5 molecular subtypes according to the 2016 WHO classification of central nervous system tumors. The IDH mutation status was determined by immunohistochemistry and/or DNA sequencing, and 1p/19q codeletion was detected with fluorescence in situ hybridization. The median clinical follow-up time was 1,076 days. T-tests and chi-square tests were used to compare clinicopathological characteristics. Kaplan-Meier and Cox regression methods were used to evaluate prognostic factors.

RESULTS

Our cohort included 15.5% lower-grade gliomas, IDH-mutant and 1p/19q-codeleted (LGG-IDHm-1p/19q); 18.1% lower-grade gliomas, IDH-mutant (LGG-IDHm); 13.1% lower-grade gliomas, IDH-wildtype (LGG-IDHwt); 36.1% glioblastoma, IDH-wildtype (GBM-IDHwt); and 17.2% glioblastoma, IDH-mutant (GBM-IDHm). Approximately 63.3% of the enrolled primary gliomas, and the median overall survival times for LGG-IDHm, LGG-IDHwt, GBM-IDHwt, and GBM-IDHm subtypes were 75.97, 34.47, 11.57, and 15.17 months, respectively. The 5-year survival rate of LGG-IDHm-1p/19q was 76.54%. We observed a significant association between high resection rate and favorable survival outcomes across all subtypes of primary tumors. We also observed a significant role of chemotherapy in prolonging overall survival for GBM-IDHwt and GBM-IDHm, and in prolonging post-relapse survival for the 2 recurrent GBM subtypes.

CONCLUSIONS

By controlling for molecular subtypes, we found that resection rate and chemotherapy were 2 prognostic factors associated with survival outcomes in a Chinese cohort with DG.

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

Purpose

This study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.

Methods

This retrospective study included 105 patients with meningiomas, including 60 NF2-mutant samples and 45 wild-type samples. Radiomic features were extracted from magnetic resonance imaging scans, including T1-weighted, T2-weighted, and contrast T1-weighted images. Student’s t-test and LASSO regression were performed to select the radiomic features. All patients were randomly divided into training and validation cohorts in a 7:3 ratio. Five linear models (RF, SVM, LR, KNN, and xgboost) were trained to predict the NF2 mutational status. Receiver operating characteristic curve and precision-recall analyses were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of NF2 mut/loss prediction for patients with different NF2 statuses.

Results

Nine features had nonzero coefficients in the LASSO regression model. No significant differences was observed in the clinical features. Nine features showed significant differences in patients with different NF2 statuses. Among all machine learning algorithms, SVM showed the best performance. The area under curve and accuracy of the predictive model were 0.85; the F1-score of the precision-recall curve was 0.80. The model risk was assessed by plotting calibration curves. The p-value for the H-L goodness of fit test was 0.411 (p> 0.05), which indicated that the difference between the obtained model and the perfect model was statistically insignificant. The AUC of our model in external validation was 0.83.

Conclusion

A combination of radiomic analysis and machine learning showed potential clinical utility in the prediction of preoperative NF2 status. These findings could aid in developing customized neurosurgery plans and meningioma management strategies before postoperative pathology.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.

Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion

Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T₂-weighted MRI-based radiomics model for predicting the survival of gliomas.

A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T₂-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T₂-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining.

The 14 radiomic features-based prediction model was constructed from preoperative T₂-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration.

The preoperative T₂-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.

Genome-wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.

Molecular subtype impacts surgical resection in low-grade gliomas: A Chinese Glioma Genome Atlas database analysis

Surgeons have considered extending the resection margins for better outcomes in gliomas, but have not considered molecular pathology. We investigated the impact of molecular pathology on the surgical benefit in gliomas. Herein, we collected the clinical and pathological information of 449 patients with glioma from the Chinese Glioma Genome Atlas database, and enrolled those who underwent surgical resection. We measured the impact of the extent of resection on survival time in subgroups classified by clinical characteristics. We found that gross total resection (GTR) was associated with longer survival times in the entire cohort, and each of the three molecular subtypes. Even after age stratification, there was no survival benefit from GTR in those with a Karnofsky performance score (KPS) ≤ 80. In patients aged >45 years with a KPS >80, extensive resection resulted in longer survival times in isocitrate dehydrogenase-mutated astrocytomas. Additionally, GTR was associated with longer overall survival times in patients aged ≤45 years with a KPS >80. In conclusion, extensive resection does not always prolong survival in patients with glioma. Along with clinical characteristics, molecular pathology positively impacts survival in gliomas. Neurosurgeons may consider our findings when planning surgery in the future.

Characterization of METTL7B to Evaluate TME and Predict Prognosis by Integrative Analysis of Multi-Omics Data in Glioma

Glioma is the most common and aggressive type of primary brain malignant tumor with limited treatment approaches. Methyltransferase-like 7B (METTL7B) is associated with the pathogenesis of several diseases but is rarely studied in glioma. In this study, 1,493 glioma samples (data from our cohort, TCGA, and CGGA) expressing METTL7B were used to explore its prognostic value and mechanism in the immune microenvironment. Results showed that high expression of METTL7B is associated with poor prognosis and abundant immunosuppressive cells. Further, functional enrichment showed that METTL7B is involved in the negative regulation of immunity and carcinogenic signaling pathways. Moreover, a METTL7B-related prognostic signature constructed based on multi-omics showed a good prediction of the overall survival (OS) time of glioma patients. In conclusion, METTL7B is a potential prognostic biomarker. In addition, the prognostic prediction model constructed in this study can be used in clinical setups for the development of novel effective therapeutic strategies for glioma patients and improving overall survival.

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics

Background

Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.

Purpose

To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.

Methods and Materials

A total of 350 patients were collected from two institutions, including 182 patients with GBM and 168 patients with MET, which were all proven by pathology. The ROI of the tumor was obtained on axial postcontrast MRI which was performed before operation. Seven radiomic feature selection methods and four classification algorithms constituted 28 classifiers in two classification strategies, with the best classifier serving as the final radiomics model. The clinical and combination models were constructed using the nomograms developed. The performance of the nomograms was evaluated in terms of calibration, discrimination, and clinical usefulness. Student’s t-test or the chi-square test was used to assess the differences in the clinical and radiological characteristics between the training and internal validation cohorts. Receiver operating characteristic curve analysis was performed to assess the performance of developed models with the area under the curve (AUC).

Results

The classifier fisher_decision tree (fisher_DT) showed the best performance (AUC: 0.696, 95% CI:0.608-0.783) for distinguishing between GBM and MET in internal validation cohorts; the classifier reliefF_random forest (reliefF_RF) showed the best performance (AUC: 0.759, 95% CI: 0.613-0.904) for distinguishing between MET-lung and MET-other in internal validation cohorts. The combination models incorporating the radiomics signature and clinical-radiological characteristics were superior to the clinical-radiological models in the two classification strategies (AUC: 0.764 for differentiation between GBM in internal validation cohorts and MET and 0.759 or differentiation between MET-lung and MET-other in internal validation cohorts). The nomograms showed satisfactory performance and calibration and were considered clinically useful, as revealed in the decision curve analysis.

Data Conclusion

The combination of radiomic and non-radiomic features is helpful for the differentiation among GBM, MET-lung, and MET-other.

CKS2 (CDC28 protein kinase regulatory subunit 2) is a prognostic biomarker in lower grade glioma: a study based on bioinformatic analysis and immunohistochemistry

Gliomas account for the highest cases of primary brain malignancies. Whereas previous studies have demonstrated the roles of CDC28 Protein Kinase Regulatory Subunit 2 (CKS2) in various cancer types, its functions in lower grade gliomas (LGGs) remain elusive. This study aimed to profile the expression and functions of CKS2 in LGG. Multiple online databases such as The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), Gene Expression Profiling Interactive Analysis 2nd edition (GEPIA2), Tumor Immune Estimation Resource 2nd edition (TIMER2.0) as well as Gene Expression Omnibus (GEO) were used in this study. Immunohistochemistry (IHC) was performed to evaluate CKS2 protein expression. Our data demonstrated upregulation of CKS2 in LGG tissues at both mRNA and protein level, especially in grade III gliomas. Similarly, there was increased expression of CKS2 in isocitrate dehydrogenase 1 (IDH1) wildtype gliomas. In addition, increased DNA copy number and DNA hypomethylation might be associated with the upregulation of the CKS2 in LGG. Using the Kaplan–Meier survival analysis and the Cox regression analysis, CKS2 was shown to be independently associated with poor prognosis of LGG patients. Receiver operating characteristic (ROC) analysis revealed that CKS2 could effectively predict the 1-, 3- and 5-year survival rates of LGG patients. Enrichment analyses revealed that CKS2 was mainly involved in the regulation of the cell cycle in LGG. Taken together, our study demonstrated that CKS2 might be a candidate prognostic biomarker for LGG and could predict the survival rates of LGG patients.

Abbreviations: LGG: lower grade glioma; CKS2: CDC28 protein kinase regulatory subunit 2; TCGA: The Cancer Genome Atlas; CGGA: the Chinese Glioma Genome Atlas; GEO: Gene Expression Omnibus; GEPIA: Gene Expression Profiling Interactive Analysis; TIMER: Tumor Immune Estimation Resource; IHC: immunohistochemistry; qRT-PCR: quantitative real-time polymerase chain reaction; PBS: phosphate buffered saline; DAB: diaminobenzidine tetrachloride; OS: overall survival; CAN: copy number alteration; IDH: Isocitrate dehydrogenase; GSEA: Gene Set Enrichment Analysis; DEG: differentially expressed gene; KEGG: Kyoto encyclopedia of genes and genomes; GO: Gene ontology; BP: biological process; CC: cellular component; MF: molecular function; NES: normalized enrichment score; NOM: nominal; FDR: false discovery rate

Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas

Simple Summary

Significant efforts have been put toward developing MRI-based radiogenomics for IDH status subtyping predictions; however, in the vast majority of these approaches, the external validation sets are absent. Another limitation in current studies is the lack of explainability in radiomics models, which hampers clinical trust and translation. Motivated by these challenges, we proposed a multicenter DSC–MRI-based radiomics study based on an independent exploratory set, which was externally validated on two independent cohorts, for IDH mutation status prediction. Our results demonstrated that DSC–MRI radiogenomics in gliomas, coupled with dynamic-based image standardization techniques, hold the potential to provide (a) increased predictive performance by offering models that generalize well, (b) reasoning behind the IDH mutation status predictions, and (c) interpretability of the radiomics features’ impacts in model performance.

Abstract

To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC–MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent cohorts. The maximum performance of the IDH mutation status prediction on the validation set had an accuracy of 0.544 (Cohen’s kappa: 0.145, F1-score: 0.415, area under the curve-AUC: 0.639, sensitivity: 0.733, specificity: 0.491), which significantly improved to an accuracy of 0.706 (Cohen’s kappa: 0.282, F1-score: 0.474, AUC: 0.667, sensitivity: 0.6, specificity: 0.736) when dynamic-based standardization of the images was performed prior to the radiomics. Model explainability using local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) revealed potential intuitive correlations between the IDH–wildtype increased heterogeneity and the texture complexity. These results strengthened our hypothesis that DSC–MRI radiogenomics in gliomas hold the potential to provide increased predictive performance from models that generalize well and provide understandable patterns between IDH mutation status and the extracted features toward enabling the clinical translation of radiogenomics in neuro-oncology.

Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas

Purpose

The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas.

Methods

This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status.

Results

Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group.

Conclusion

Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

Detection of Isocitrate Dehydrogenase Mutated Glioblastomas Through Anomaly Detection Analytics

BACKGROUND

The rarity of Isocitrate Dehydrogenase mutated (mIDH) glioblastomas relative to wild-type IDH glioblastomas, as well as their distinct tumor physiology, effectively render them "outliers". Specialized tools are needed to identify these outliers.

OBJECTIVE

To carefully craft and apply anomaly detection methods to identify mIDH glioblastoma based on radiomic features derived from magnetic resonance imaging.

METHODS

T1-post gadolinium images for 188 patients and 138 patients were downloaded from The Cancer Imaging Archive's (TCIA) The Cancer Genome Atlas (TCGA) glioblastoma collection, and from the University of Minnesota Medical Center (UMMC), respectively. Anomaly detection methods were tested on glioblastoma image features for the precision of mIDH detection and compared to standard classification methods.

RESULTS

Using anomaly detection training methods, we were able to detect IDH mutations from features in noncontrast-enhancing regions in glioblastoma with an average precision of 75.0%, 69.9%, and 69.8% using three different models. Anomaly detection methods consistently outperformed traditional two-class classification methods from 2 unique learning models (67.9%, 67.6%). The disparity in performances could not be overcome through newer, popular models such as neural networks (67.4%).

CONCLUSION

We employed an anomaly detection strategy in the detection of IDH mutation in glioblastoma using preoperative T1 postcontrast imaging. We show these methods outperform traditional two-class classification in the setting of dataset imbalances inherent to IDH mutation prevalence in glioblastoma. We validate our results using an external dataset and highlight new possible avenues for radiogenomic rare event prediction in glioblastoma and beyond.

Colen R, Zinn PO. MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift

Low-grade gliomas (LGGs) are tumors that affect mostly adults. These neoplasms are comprised mainly of oligodendrogliomas and diffuse astrocytomas. LGGs remain vexing to current management and therapeutic modalities although they exhibit more favorable survival rates compared with high-grade gliomas (HGGs). The specific genetic subtypes that these tumors exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of an LGG pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). The introduction of radiomics as a high throughput quantitative imaging technique that allows for improved diagnostic, prognostic and predictive indices has created more interest for such techniques in cancer research and especially in neurooncology (MRI-based classification of LGGs, predicting Isocitrate dehydrogenase (IDH) and Telomerase reverse transcriptase (TERT) promoter mutations and predicting LGG associated seizures). Radiogenomics refers to the linkage of imaging findings with the tumor/tissue genomics. Numerous applications of radiomics and radiogenomics have been described in the clinical context and management of LGGs. In this review, we describe the recently published studies discussing the potential application of radiomics and radiogenomics in LGGs. We also highlight the potential pitfalls of the above-mentioned high throughput computerized techniques and, most excitingly, explore the use of machine learning artificial intelligence technologies as standalone and adjunct imaging tools en route to enhance a personalized MRI-based tumor diagnosis and management plan design.

Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI

BACKGROUND

In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients.

METHODS

This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant.

RESULTS

After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset.

CONCLUSION

The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.

Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

DZIP3 is a key factor to stratify IDH1 wild-type lower-grade gliomas

BACKGROUND

Malignant glioma is the most common form of primary malignant brain cancer. Heterogeneity is the hallmark of glioma. DAZ-interacting zinc finger 3 (DZIP3), acts as an RNA-binding RING-type ubiquitin ligase; however, its function in glioma is yet unclear.

RESULTS

The DZIP3 expression was related to the World Health Organization (WHO) grade and isocitrate dehydrogenase 1(IDH1) status, as well as the clinical outcome. Malignant cases exhibit lower DZIP3 expression. DZIP3 was an independent predictive factor of good prognosis in all grade and lower grade gliomas (p < 0.0001). Gene enrichment analysis and immunohistochemistry indicated that DZIP3 affected the biological behavior of glioma through the angiogenesis pathway. Moreover, based on DZIP3 expression, IDH1 wild-type lower-grade gliomas could be divided into two groups with different survival time.

CONCLUSION

In conclusion, the loss of DZIP3 may be involved in the mechanism of angiogenesis in the invasive biological process of glioma. These findings laid an understanding of DZIP3-specific clinical features in glioma.

METHODS

A total of 325 glioma patients from the Chinese Glioma Genome Atlas (CGGA) RNA-seq cohort comprised the training cohort, while 265 patients from the GSE 16011 array cohort formed the validation cohort. The mRNA expression of DZIP3 and clinical characteristics was assessed. DZIP3 protein expression and microvessel density (MVD) were evaluated by immunohistochemistry (IHC).

Predicting Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features

BACKGROUND

Accurate and noninvasive detection of isocitrate dehydrogenase (IDH, including IDH1 and IDH2) status is clinically meaningful for molecular stratification of glioma, but remains challenging.

PURPOSE

To establish a model for classifying IDH status in gliomas based on multiparametric MRI.

STUDY TYPE

Retrospective, radiomics.

POPULATION

In all, 105 consecutive cases of grade II-IV glioma with 50 IDH1 or IDH2 mutant (IDHm) and 55 IDH wildtype (IDHw) were separated into a training cohort (n = 73) and a test cohort (n = 32).

FIELD STRENGTH/SEQUENCE

Contrast-enhanced T₁ -weighted (CE-T₁ W), T₂ -weighted (T₂ W), and arterial spin labeling (ASL) images were acquired at 3.0T.

ASSESSMENT

Two doctors manually labeled the volume of interest (VOI) on CE-T₁ W, then T₂ W and ASL were coregistered to CE-T₁ W. A total of 851 radiomics features were extracted on each VOI of three sequences. From the training cohort, all radiomics features with age and gender were processed by the Mann-Whitney U-test, Pearson test, and least absolute shrinkage and selection operator to obtain optimal feature groups to train support vector machine models. The accuracy and area under curve (AUC) of all models for classifying the IDH status were calculated on the test cohort. Two subtasks were performed to verify the efficiency of texture features and the Pearson test in IDH status classification, respectively.

STATISTICAL TESTS

The permutation test with Bonferroni correction; chi-square test.

RESULTS

The accuracy and AUC of the classifier, which combines the features of all three sequences, achieved 0.823 and 0.770 (P < 0.05), respectively. The best model established by texture features only had an AUC of 0.819 and an accuracy of 0.761. The best model established without the Pearson test got an AUC of 0.747 and an accuracy of 0.719.

DATA CONCLUSION

IDH genotypes of glioma can be identified by radiomics features from multiparameter MRI. The Pearson test improved the performance of the IDH classification models.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 1.

Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors

PURPOSE

This is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort.

METHODS

Magnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests.

RESULTS

Low-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05).

CONCLUSION

Texture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.

A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas

•

We treat gray-level co-occurrence matrices (GLCM) as two-dimensional functional data objects.

•

We develop a Bayesian functional regression method that relates GLCMs to IDH mutation status.

•

The method accounts for multiple imaging sequences.

•

The method outperforms other competing methods that use only GLCM derived summary statistics.

In cancer radiomics, textural features evaluated from image intensity-derived gray-level co-occurrence matrices (GLCMs) have been studied to evaluate gray-level spatial dependence within the regions of interest in the brain. Most of these analysis work with summary statistics (or texture-based features) constructed using the GLCM entries, and potentially overlook other structural properties in the GLCM. In our proposed Bayesian framework, we treat each GLCM as a realization of a two-dimensional stochastic functional process observed with error at discrete time points. The latent process is then combined with the outcome model to evaluate the prediction performance. We use simulation studies to assess the performance of our method and apply it to data collected from individuals with lower grade gliomas. We found our approach to outperform competing methods that use only summary statistics to predict isocitrate dehydrogenase (IDH) mutation status.

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

OBJECTIVES

Isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas of histologic grades II and III follow heterogeneous clinical outcomes, which necessitates risk stratification. We aimed to evaluate whether radiomics from MRI would allow prediction of overall survival in patients with IDHwt lower-grade gliomas and to investigate the added prognostic value of radiomics over clinical features.

METHODS

Preoperative MRIs of 117 patients with IDHwt lower-grade gliomas from January 2007 to February 2018 were retrospectively analyzed. The external validation cohort consisted of 33 patients from The Cancer Genome Atlas. A total of 182 radiomic features were extracted. Radiomics risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator (LASSO) and elastic net. Multivariable Cox regression analyses, including clinical features and RRSs, were performed. The integrated areas under the receiver operating characteristic curves (iAUCs) from models with and without RRSs were calculated for comparisons. The prognostic value of RRS was assessed in the validation cohort.

RESULTS

The RRS derived from LASSO and elastic net independently predicted survival with hazard ratios of 9.479 (95% confidence interval [CI], 3.220-27.847) and 6.148 (95% CI, 3.009-12.563), respectively. Those RRSs enhanced model performance for predicting overall survival (iAUC increased to 0.780-0.797 from 0.726), which was externally validated. The RRSs stratified IDHwt lower-grade gliomas in the validation cohort with significantly different survival.

CONCLUSION

Radiomics has the potential for noninvasive risk stratification and can improve prediction of overall survival in patients with IDHwt lower-grade gliomas when integrated with clinical features.

KEY POINTS

• Isocitrate dehydrogenase wild-type lower-grade gliomas with histologic grades II and III follow heterogeneous clinical outcomes, which necessitates further risk stratification. • Radiomics risk scores derived from MRI independently predict survival even after incorporating strong clinical prognostic features (hazard ratios 6.148-9.479). • Radiomics risk scores derived from MRI have the potential to improve survival prediction when added to clinical features (integrated areas under the receiver operating characteristic curves increased from 0.726 to 0.780-0.797).

Role of molecular biomarkers in glioma resection: a systematic review

New discoveries based on genetic and epigenetic evidence have significantly expanded the understanding of diffuse gliomas. Molecular biomarkers detected in diffuse gliomas are not only potential targets for radiotherapy, chemotherapy, and immunotherapy, but are also able to guide surgical treatment. Previous studies have suggested that the optimal extent of resection of diffuse gliomas varies according to the expression of specific molecular biomarkers. However, the specific guiding role of these biomarkers in the resection of diffuse gliomas has not been systemically analyzed. This review summarizes several critical molecular biomarkers of tumorigenesis and progression in diffuse gliomas and discusses different strategies of tumor resection in the context of varying genetic expression. With ongoing study and advances in technology, molecular biomarkers will play a more important role in glioma resection and maximize the survival benefit from surgery for diffuse gliomas.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Background

Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.

Methods

This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.

Results

Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.

Conclusion

Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

Development and Validation of an IDH1-Associated Immune Prognostic Signature for Diffuse Lower-Grade Glioma

A mutation in the isocitrate dehydrogenase 1 (IDH1) gene is the most common mutation in diffuse lower-grade gliomas (LGGs), and it is significantly related to the prognosis of LGGs. We aimed to explore the influence of the IDH1 mutation on the immune microenvironment and develop an IDH1-associated immune prognostic signature (IPS) for predicting prognosis in LGGs. IDH1 mutation status and RNA expression were investigated in two different public cohorts. To develop an IPS, LASSO Cox analysis was conducted for immune-related genes that were differentially expressed between IDH1^wt and IDH1^mut LGG patients. Then, we systematically analyzed the influence of the IPS on the immune microenvironment. A total of 41 immune prognostic genes were identified based on the IDH1 mutation status. A four-gene IPS was established and LGG patients were effectively stratified into low- and high-risk groups in both the training and validation sets. Stratification analysis and multivariate Cox analysis revealed that the IPS was an independent prognostic factor. We also found that high-risk LGG patients had higher levels of infiltrating B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells, and expressed higher levels of CTLA-4, PD-1 and TIM-3. Moreover, a novel nomogram model was established to estimate the overall survival in LGG patients. The current study provides novel insights into the LGG immune microenvironment and potential immunotherapies. The proposed IPS is a clinically promising biomarker that can be used to classify LGG patients into subgroups with distinct outcomes and immunophenotypes, with the potential to facilitate individualized management and improve prognosis.

Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Lower-grade gliomas (LrGG), characterized by invasiveness and heterogeneity, remain incurable with current therapies. Clinicopathological features provide insufficient information to guide optimal individual treatment and cannot predict prognosis completely. Recently, an increasing amount of studies indicate that the tumor microenvironment plays a pivotal role in tumor malignancy and treatment responses. However, studies focusing on the tumor microenvironment (TME) of LrGG are still limited. In this study, taking advantage of the currently popular computational methods for estimating the infiltration of tumor-associated normal cells in tumor samples and using weighted gene co-expression network analysis, we screened the co-expressed gene modules associated with the TME and further identified the prognostic hub genes in these modules. Furthermore, eight prognostic hub genes (ARHGDIB, CLIC1, OAS3, PDIA4, PARP9, STAT1, TAP2, and TAGLN2) were selected to construct a prognostic risk score model using the least absolute shrinkage and selection operator method. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor for LrGG. Moreover, time-dependent ROC curves indicated that our model had favorable efficiency in predicting both short- and long-term prognosis in LrGG patients, and the stratified survival analysis demonstrated that our model had prognostic value for different subgroups of LrGG patients. Additionally, our model had potential value for predicting the sensitivity of LrGG patients to radio- and chemotherapy. Besides, differential expression analysis showed that the eight genes were aberrantly expressed in LrGG compared to normal brain tissue. Correlation analysis revealed that the expression of the eight genes was significantly associated with the infiltration levels of six types of immune cells in LrGG. In summary, the TME-related eight-gene signature was significantly associated with the prognosis of LrGG patients. They might act as potential prognostic biomarkers for LrGG patients, offer better stratification for future clinical trials, and be candidate targets for immunotherapy, thus deserving further investigation.

Discovery of 5-Signature Predicting Survival of Patients with Lower-Grade Glioma

OBJECTIVE

In the study, we aimed to identify key microRNAs (miRNAs) and clinical factors associated with survival time of lower-grade glioma (LGG) and develop an expression-based miRNA signature to provide survival risk prediction for patients with LGG.

METHODS

We obtained miRNA expression profiles and clinical information of patients with LGG from The Cancer Genome Atlas dataset. All 591 miRNAs were modeled using random Forest Survival, Regression, and Classification to construct a random forest model for survival analysis, and feature selection was performed. We used univariate and multivariate Cox regression analysis to screen differentially expressed miRNAs and clinical factors related to overall survival of patients with LGG.

RESULTS

A total of 591 differentially expressed miRNAs were obtained between LGG and normal tissues. After univariate and multivariate Cox regression analysis, 2 predictive miRNAs (hsa-miR-10b-5p and hsa-miR-15b-5p) and 3 clinical factors (grade, age, and cancer status) were finally screened out to construct a 5-signature, based on which patients in the training dataset were divided into high-risk and low-risk groups. The competitive performance of the 5-signature was revealed by receiver operating characteristic curve analysis. The prognostic value of the 5-signature was successfully validated in the testing and validation dataset.

CONCLUSIONS

Our study demonstrated the promising potential of the novel 5-signature as an independent biomarker for survival prediction of patients with LGG.