A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas

[Research Progress in Imaging Investigation of TERT Promoter Mutations in Gliomas]

Somatic mutations in the promoter region of telomerase reverse transcriptase (TERT), a critical mechanism for telomerase reactivation, play a key role in tumorigenesis. The status of TERT promoter mutation serves as a crucial molecular biomarker for glioma assessment and classification, and is essential for early diagnosis of glioma subtypes, guiding treatment decision-making, and improving patient prognosis. With the recognition of the importance of molecular subtyping of gliomas, there has been a surge in research on non-invasive prediction of key molecular biomarkers based on preoperative imaging of gliomas, with a particular focus on TERT promoter studies using radiomics approaches. This article presents a comprehensive review of research on TERT promoter mutations in gliomas and imaging-related studies, with the goal of providing insights for future studies on non-invasive prediction of TERT promoters status and offering important references for the precision diagnosis and treatment of glioma patients.

-New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates

Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/-10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.

Predicting telomerase reverse transcriptase promoter mutation in glioma: A systematic review and diagnostic meta-analysis on machine learning algorithms

BACKGROUND

Glioma is one of the most common primary brain tumors. The presence of the telomerase reverse transcriptase promoter (pTERT) mutation is associated with a better prognosis. This study aims to investigate the TERT mutation in patients with glioma using machine learning (ML) algorithms on radiographic imaging.

METHOD

This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The electronic databases of PubMed, Embase, Scopus, and Web of Science were searched from inception to August 1, 2023. The statistical analysis was performed using the MIDAS package of STATA v.17.

RESULTS

A total of 22 studies involving 5371 patients were included for data extraction, with data synthesis based on 11 reports. The analysis revealed a pooled sensitivity of 0.86 (95% CI: 0.78-0.92) and a specificity of 0.80 (95% CI 0.72-0.86). The positive and negative likelihood ratios were 4.23 (95% CI: 2.99-5.99) and 0.18 (95% CI: 0.11-0.29), respectively. The pooled diagnostic score was 3.18 (95% CI: 2.45-3.91), with a diagnostic odds ratio 24.08 (95% CI: 11.63-49.87). The Summary Receiver Operating Characteristic (SROC) curve had an area under the curve (AUC) of 0.89 (95% CI: 0.86-0.91).

CONCLUSION

The study suggests that ML can predict TERT mutation status in glioma patients. ML models showed high sensitivity (0.86) and moderate specificity (0.80), aiding disease prognosis and treatment planning. However, further development and improvement of ML models are necessary for better performance metrics and increased reliability in clinical practice.

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

A radiomics-based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

BACKGROUND AND PURPOSE

As a crucial diagnostic and prognostic biomarker, telomerase reverse transcriptase (TERT) promoter mutation holds immense significance for personalized treatment of patients with glioblastoma (GBM). In this study, we developed a radiomics nomogram to determine the TERT promoter mutation status and assessed its prognostic efficacy in GBM patients.

METHODS

The study retrospectively included 145 GBM patients. A comprehensive set of 3736 radiomics features was extracted from preoperative magnetic resonance imaging, including T2-weighted imaging, T1-weighted imaging (T1WI), contrast-enhanced T1WI, and fluid-attenuated inversion recovery. The construction of the radiomics model was based on integrating the radiomics signature (rad-score)with clinical features. Receiver-operating characteristic curve analysis was employed to evaluate the discriminative ability of the prediction model, and the risk score was used to stratify patient outcomes.

RESULTS

The least absolute shrinkage and selection operator classifier identified 10 robust features for constructing the prediction model, and the radiomics nomogram exhibited excellent performance in predicting TERT promoter mutation status, with area under the curve values of.906 (95% confidence interval [CI]:.850-.963) and.899 (95% CI:.708-.966) in the training and validation sets, respectively. The clinical utility of the radiomics nomogram is further supported by calibration curve and decision curve analyses. Additionally, the radiomics nomogram effectively stratified GBM patients with significantly different prognoses (HR = 1.767, p = .019).

CONCLUSION

The radiomics nomogram holds promise as a modality for evaluating TERT promoter mutations and prognostic outcomes in patients with GBM.

Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas

AIM

To enhance the prediction of mutation status of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter, which are crucial for glioma prognostication and therapeutic decision-making, via sub-regional radiomics analysis based on multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A retrospective study was conducted on 401 participants with adult-type diffuse gliomas. Employing the K-means algorithm, tumours were clustered into two to four subregions. Sub-regional radiomics features were extracted and selected using the Mann-Whitney U-test, Pearson correlation analysis, and least absolute shrinkage and selection operator, forming the basis for predictive models. The performance of model combinations of different sub-regional features and classifiers (including logistic regression, support vector machines, K-nearest neighbour, light gradient boosting machine, and multilayer perceptron) was evaluated using an external test set.

RESULTS

The models demonstrated high predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.918 to 0.994 in the training set for IDH mutation prediction and from 0.758 to 0.939 for TERT promoter mutation prediction. In the external test sets, the two-cluster radiomics features and the logistic regression model yielded the highest prediction for IDH mutation, resulting in an AUC of 0.905. Additionally, the most effective predictive performance with an AUC of 0.803 was achieved using the four-cluster radiomics features and the support vector machine model, specifically for TERT promoter mutation prediction.

CONCLUSION

The present study underscores the potential of sub-regional radiomics analysis in predicting IDH and TERT promoter mutations in glioma patients. These models have the capacity to refine preoperative glioma diagnosis and contribute to personalised therapeutic interventions for patients.

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor

BACKGROUND

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy.

AIM

To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions.

METHODS

A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test.

RESULTS

The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility.

CONCLUSION

Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas

Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas' high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging's role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.

MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas

The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.

Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study

PURPOSE

This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)-based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients.

METHODS

We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient's T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles.

RESULTS

TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 - 0.923; p = 0.025).

CONCLUSION

MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.

An alternative extension of telomeres related prognostic model to predict survival in lower grade glioma

OBJECTIVE

The alternative extension of the telomeres (ALT) mechanism is activated in lower grade glioma (LGG), but the role of the ALT mechanism has not been well discussed. The primary purpose was to demonstrate the significance of the ALT mechanism in prognosis estimation for LGG patients.

METHOD

Gene expression and clinical data of LGG patients were collected from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohort, respectively. ALT-related genes obtained from the TelNet database and potential prognostic genes related to ALT were selected by LASSO regression to calculate an ALT-related risk score. Multivariate Cox regression analysis was performed to construct a prognosis signature, and a nomogram was used to represent this signature. Possible pathways of the ALT-related risk score are explored by enrichment analysis.

RESULT

The ALT-related risk score was calculated based on the LASSO regression coefficients of 22 genes and then divided into high-risk and low-risk groups according to the median. The ALT-related risk score is an independent predictor of LGG (HR and 95% CI in CGGA cohort: 5.70 (3.79, 8.58); in TCGA cohort: 1.96 (1.09, 3.54)). ROC analysis indicated that the model contained ALT-related risk score was superior to conventional clinical features (AUC: 0.818 vs 0.729) in CGGA cohorts. The results in the TCGA cohort also shown a powerful ability of ALT-related risk score (AUC: 0.766 vs 0.691). The predicted probability and actual probability of the nomogram are consistent. Enrichment analysis demonstrated that the ALT mechanism was involved in the cell cycle, DNA repair, immune processes, and others.

CONCLUSION

ALT-related risk score based on the 22-gene is an important factor in predicting the prognosis of LGG patients.

Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI

BACKGROUND

Studies have shown that magnetic resonance imaging (MRI)-based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status in patients with glioblastoma (GBM) remains unclear.

PURPOSE

To evaluate the value of deep learning (DL) in multiparametric MRI-based radiomics in identifying TERT promoter mutations in patients with GBM preoperatively.

STUDY TYPE

Retrospective.

POPULATION

A total of 274 patients with isocitrate dehydrogenase-wildtype GBM were included in the study. The training and external validation cohorts included 156 (54.3 ± 12.7 years; 96 males) and 118 (54 .2 ± 13.4 years; 73 males) patients, respectively.

FIELD STRENGTH/SEQUENCE

Axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1CE), T1-weighted spin-echo inversion recovery sequence (T1WI), and T2-weighted spin-echo inversion recovery sequence (T2WI) on 1.5-T and 3.0-T scanners were used in this study.

ASSESSMENT

Overall tumor area regions (the tumor core and edema) were segmented, and the radiomics and DL features were extracted from preprocessed multiparameter preoperative brain MRI images-T1WI, T1CE, and T2WI. A model based on the DLR signature, clinical signature, and clinical DLR (CDLR) nomogram was developed and validated to identify TERT promoter mutation status.

STATISTICAL TESTS

The Mann-Whitney U test, Pearson test, least absolute shrinkage and selection operator, and logistic regression analysis were applied for feature selection and construction of radiomics and DL signatures. Results were considered statistically significant at P-value <0.05.

RESULTS

The DLR signature showed the best discriminative power for predicting TERT promoter mutations, yielding an AUC of 0.990 and 0.890 in the training and external validation cohorts, respectively. Furthermore, the DLR signature outperformed CDLR nomogram (P = 0.670) and significantly outperformed clinical models in the validation cohort.

DATA CONCLUSION

The multiparameter MRI-based DLR signature exhibited a promising performance for the assessment of TERT promoter mutations in patients with GBM, which could provide information for individualized treatment.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Multi-parameter MRI based radiomics nomogram for predicting telomerase reverse transcriptase promoter mutation and prognosis in glioblastoma

Objective

To investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM).

Methods

We retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images. A radiomics nomogram was created on the basis of radiomics signature (rad-score) and clinical traits. The performance of the nomogram in TERT mutation identification was assessed using receiver operating characteristic (ROC) curve, calibration curves, and clinical decision curves. Pathologically confirmed TERT mutations and risk score-based TERT mutations were employed to assess patient prognosis, respectively.

Results

The random forest (RF) algorithm outperformed the other two algorithms, yielding the best diagnostic efficacy in differentiating TERT mutations, with area under the curve (AUC) values of 0.892 (95% CI: 0.828-0.956) and 0.824 (95% CI: 0.677-0.971) in the training set and validation sets, respectively. Furthermore, the predictive power of the radiomics nomogram constructed with the rad-score and clinical variables reached 0.916 (95%CI: 0.864, 0.968) in the training set and 0.880 (95%CI: 0.743, 1) in the validation set. Calibration curve and decision curve analysis findings further uphold the clinical application value of the radiomics nomogram. The overall survival of the high-risk subgroup was significantly shorter than that of the low-risk subgroup, which was consistent with the results of the pathologically confirmed TERT mutation group.

Conclusion

The radiomics nomogram could non-invasively provide promising insights for predicting TERT mutations and prognosis in GBM patients with excellent identification and calibration abilities.

Imaging features of localized IDH wild-type histologically diffuse astrocytomas: a single-institution case series

Isocitrate dehydrogenase wild-type (IDHwt) diffuse astrocytomas feature highly infiltrative patterns, such as a gliomatosis cerebri growth pattern with widespread involvement. Among these tumors, localized IDHwt histologically diffuse astrocytomas are rarer than the infiltrative type. The aim of this study was to assess and describe the clinical, radiographic, histopathological, and molecular characteristics of this rare type of IDHwt histologically diffuse astrocytomas and thereby provide more information on how its features affect clinical prognoses and outcomes. We retrospectively analyzed the records of five patients with localized IDHwt histologically diffuse astrocytomas between July 2017 and January 2020. All patients were female, and their mean age at the time of the initial treatment was 55.0 years. All patients had focal disease that did not include gliomatosis cerebri or multifocal disease. All patients received a histopathological diagnosis of diffuse astrocytomas at the time of the initial treatment. For recurrent tumors, second surgeries were performed at a mean of 12.4 months after the initial surgery. A histopathological diagnosis of glioblastoma was made in four patients and one of gliosarcoma in one patient. The initial status of IDH1, IDH2, H3F3A, HIST1H3B, and BRAF was "wild-type" in all patients. TERT promoter mutations (C250T or C228T) were detected in four patients. No tumors harbored a 1p/19q codeletion, EGFR amplification, or chromosome 7 gain/10 loss (+ 7/ - 10). We assessed clinical cases of localized IDHwt histologically diffuse astrocytomas that resulted in malignant recurrence and a poor clinical prognosis similar to that of glioblastomas. Our case series suggests that even in patients with histologically diffuse astrocytomas and those who present with radiographic imaging findings suggestive of a localized tumor mass, physicians should consider the possibility of IDHwt histologically diffuse astrocytomas.